text: # -*- coding: utf-8 -*-
  """Adds docstrings to functions defined in the torch._C"""
  
  import re
  
  import torch._C
  from torch._C import _add_docstr as add_docstr
  
  
  def parse_kwargs(desc):
      """Maps a description of args to a dictionary of {argname: description}.
      Input:
          ('    weight (Tensor): a weight tensor\n' +
           '        Some optional description')
      Output: {
          'weight': \
          'weight (Tensor): a weight tensor\n        Some optional description'
      }
      """
      # Split on exactly 4 spaces after a newline
      regx = re.compile(r"\n\s{4}(?!\s)")
      kwargs = [section.strip() for section in regx.split(desc)]
      kwargs = [section for section in kwargs if len(section) > 0]
      return {desc.split(' ')[0]: desc for desc in kwargs}
  
  
  def merge_dicts(*dicts):
      return {x: d[x] for d in dicts for x in d}
  
  
  common_args = parse_kwargs("""
      input (Tensor): the input tensor.
      generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling
      out (Tensor, optional): the output tensor.
      memory_format (:class:`torch.memory_format`, optional): the desired memory format of
          returned tensor. Default: ``torch.preserve_format``.
  """)
  
  reduceops_common_args = merge_dicts(common_args, parse_kwargs("""
      dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
          If specified, the input tensor is casted to :attr:`dtype` before the operation
          is performed. This is useful for preventing data type overflows. Default: None.
      keepdim (bool): whether the output tensor has :attr:`dim` retained or not.
  """))
  
  multi_dim_common = merge_dicts(reduceops_common_args, parse_kwargs("""
      dim (int or tuple of ints): the dimension or dimensions to reduce.
  """), {'keepdim_details': """
  If :attr:`keepdim` is ``True``, the output tensor is of the same size
  as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1.
  Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the
  output tensor having 1 (or ``len(dim)``) fewer dimension(s).
  """})
  
  single_dim_common = merge_dicts(reduceops_common_args, parse_kwargs("""
      dim (int): the dimension to reduce.
  """), {'keepdim_details': """If :attr:`keepdim` is ``True``, the output tensor is of the same size
  as :attr:`input` except in the dimension :attr:`dim` where it is of size 1.
  Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in
  the output tensor having 1 fewer dimension than :attr:`input`."""})
  
  factory_common_args = merge_dicts(common_args, parse_kwargs("""
      dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
          Default: if ``None``, uses a global default (see :func:`torch.set_default_tensor_type`).
      layout (:class:`torch.layout`, optional): the desired layout of returned Tensor.
          Default: ``torch.strided``.
      device (:class:`torch.device`, optional): the desired device of returned tensor.
          Default: if ``None``, uses the current device for the default tensor type
          (see :func:`torch.set_default_tensor_type`). :attr:`device` will be the CPU
          for CPU tensor types and the current CUDA device for CUDA tensor types.
      requires_grad (bool, optional): If autograd should record operations on the
          returned tensor. Default: ``False``.
      pin_memory (bool, optional): If set, returned tensor would be allocated in
          the pinned memory. Works only for CPU tensors. Default: ``False``.
      memory_format (:class:`torch.memory_format`, optional): the desired memory format of
          returned Tensor. Default: ``torch.contiguous_format``.
  """))
  
  factory_like_common_args = parse_kwargs("""
      input (Tensor): the size of :attr:`input` will determine size of the output tensor.
      layout (:class:`torch.layout`, optional): the desired layout of returned tensor.
          Default: if ``None``, defaults to the layout of :attr:`input`.
      dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor.
          Default: if ``None``, defaults to the dtype of :attr:`input`.
      device (:class:`torch.device`, optional): the desired device of returned tensor.
          Default: if ``None``, defaults to the device of :attr:`input`.
      requires_grad (bool, optional): If autograd should record operations on the
          returned tensor. Default: ``False``.
      pin_memory (bool, optional): If set, returned tensor would be allocated in
          the pinned memory. Works only for CPU tensors. Default: ``False``.
      memory_format (:class:`torch.memory_format`, optional): the desired memory format of
          returned Tensor. Default: ``torch.preserve_format``.
  """)
  
  factory_data_common_args = parse_kwargs("""
      data (array_like): Initial data for the tensor. Can be a list, tuple,
          NumPy ``ndarray``, scalar, and other types.
      dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
          Default: if ``None``, infers data type from :attr:`data`.
      device (:class:`torch.device`, optional): the desired device of returned tensor.
          Default: if ``None``, uses the current device for the default tensor type
          (see :func:`torch.set_default_tensor_type`). :attr:`device` will be the CPU
          for CPU tensor types and the current CUDA device for CUDA tensor types.
      requires_grad (bool, optional): If autograd should record operations on the
          returned tensor. Default: ``False``.
      pin_memory (bool, optional): If set, returned tensor would be allocated in
          the pinned memory. Works only for CPU tensors. Default: ``False``.
  """)
  
  tf32_notes = {
      "tf32_note": """This operator supports :ref:`TensorFloat32<tf32_on_ampere>`."""
  }
  
  
  reproducibility_notes = {
      "forward_reproducibility_note": """This operation may behave nondeterministically when given tensors on \
  a CUDA device. See :doc:`/notes/randomness` for more information.""",
      "backward_reproducibility_note": """This operation may produce nondeterministic gradients when given tensors on \
  a CUDA device. See :doc:`/notes/randomness` for more information.""",
      "cudnn_reproducibility_note": """In some circumstances when given tensors on a CUDA device \
  and using CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is \
  undesirable, you can try to make the operation deterministic (potentially at \
  a performance cost) by setting ``torch.backends.cudnn.deterministic = True``. \
  See :doc:`/notes/randomness` for more information."""
  }
  
  add_docstr(torch.abs, r"""
  abs(input, *, out=None) -> Tensor
  
  Computes the absolute value of each element in :attr:`input`.
  
  .. math::
      \text{out}_{i} = |\text{input}_{i}|
  """ + r"""
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> torch.abs(torch.tensor([-1, -2, 3]))
      tensor([ 1,  2,  3])
  """.format(**common_args))
  
  add_docstr(torch.absolute,
             r"""
  absolute(input, *, out=None) -> Tensor
  
  Alias for :func:`torch.abs`
  """)
  
  add_docstr(torch.acos, r"""
  acos(input, *, out=None) -> Tensor
  
  Computes the inverse cosine of each element in :attr:`input`.
  
  .. math::
      \text{out}_{i} = \cos^{-1}(\text{input}_{i})
  """ + r"""
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([ 0.3348, -0.5889,  0.2005, -0.1584])
      >>> torch.acos(a)
      tensor([ 1.2294,  2.2004,  1.3690,  1.7298])
  """.format(**common_args))
  
  add_docstr(torch.arccos, r"""
  arccos(input, *, out=None) -> Tensor
  
  Alias for :func:`torch.acos`.
  """)
  
  add_docstr(torch.acosh, r"""
  acosh(input, *, out=None) -> Tensor
  
  Returns a new tensor with the inverse hyperbolic cosine of the elements of :attr:`input`.
  
  Note:
      The domain of the inverse hyperbolic cosine is `[1, inf)` and values outside this range
      will be mapped to ``NaN``, except for `+ INF` for which the output is mapped to `+ INF`.
  
  .. math::
      \text{out}_{i} = \cosh^{-1}(\text{input}_{i})
  """ + r"""
  Args:
      {input}
  
  Keyword arguments:
      {out}
  
  Example::
  
      >>> a = torch.randn(4).uniform_(1, 2)
      >>> a
      tensor([ 1.3192, 1.9915, 1.9674, 1.7151 ])
      >>> torch.acosh(a)
      tensor([ 0.7791, 1.3120, 1.2979, 1.1341 ])
  """.format(**common_args))
  
  add_docstr(torch.arccosh, r"""
  arccosh(input, *, out=None) -> Tensor
  
  Alias for :func:`torch.acosh`.
  """)
  
  add_docstr(torch.add, r"""
  add(input, other, *, alpha=1, out=None) -> Tensor
  
  Adds :attr:`other`, scaled by :attr:`alpha`, to :attr:`input`.
  
  .. math::
      \text{{out}}_i = \text{{input}}_i + \text{{alpha}} \times \text{{other}}_i
  """ + r"""
  
  Supports :ref:`broadcasting to a common shape <broadcasting-semantics>`,
  :ref:`type promotion <type-promotion-doc>`, and integer, float, and complex inputs.
  
  Args:
      {input}
      other (Tensor or Number): the tensor or number to add to input.
  
  Keyword arguments:
      alpha (Number): the multiplier for :attr:`other`.
      {out}
  
  Examples::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([ 0.0202,  1.0985,  1.3506, -0.6056])
      >>> torch.add(a, 20)
      tensor([ 20.0202,  21.0985,  21.3506,  19.3944])
  
      >>> b = torch.randn(4)
      >>> b
      tensor([-0.9732, -0.3497,  0.6245,  0.4022])
      >>> c = torch.randn(4, 1)
      >>> c
      tensor([[ 0.3743],
              [-1.7724],
              [-0.5811],
              [-0.8017]])
      >>> torch.add(b, c, alpha=10)
      tensor([[  2.7695,   3.3930,   4.3672,   4.1450],
              [-18.6971, -18.0736, -17.0994, -17.3216],
              [ -6.7845,  -6.1610,  -5.1868,  -5.4090],
              [ -8.9902,  -8.3667,  -7.3925,  -7.6147]])
  """.format(**common_args))
  
  add_docstr(torch.addbmm,
             r"""
  addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor
  
  Performs a batch matrix-matrix product of matrices stored
  in :attr:`batch1` and :attr:`batch2`,
  with a reduced add step (all matrix multiplications get accumulated
  along the first dimension).
  :attr:`input` is added to the final result.
  
  :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the
  same number of matrices.
  
  If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a
  :math:`(b \times m \times p)` tensor, :attr:`input` must be
  :ref:`broadcastable <broadcasting-semantics>` with a :math:`(n \times p)` tensor
  and :attr:`out` will be a :math:`(n \times p)` tensor.
  
  .. math::
      out = \beta\ \text{input} + \alpha\ (\sum_{i=0}^{b-1} \text{batch1}_i \mathbin{@} \text{batch2}_i)
  
  If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in
  it will not be propagated.
  """ + r"""
  For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and :attr:`alpha`
  must be real numbers, otherwise they should be integers.
  
  {tf32_note}
  
  Args:
      batch1 (Tensor): the first batch of matrices to be multiplied
      batch2 (Tensor): the second batch of matrices to be multiplied
  
  Keyword args:
      beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`)
      input (Tensor): matrix to be added
      alpha (Number, optional): multiplier for `batch1 @ batch2` (:math:`\alpha`)
      {out}
  
  Example::
  
      >>> M = torch.randn(3, 5)
      >>> batch1 = torch.randn(10, 3, 4)
      >>> batch2 = torch.randn(10, 4, 5)
      >>> torch.addbmm(M, batch1, batch2)
      tensor([[  6.6311,   0.0503,   6.9768, -12.0362,  -2.1653],
              [ -4.8185,  -1.4255,  -6.6760,   8.9453,   2.5743],
              [ -3.8202,   4.3691,   1.0943,  -1.1109,   5.4730]])
  """.format(**common_args, **tf32_notes))
  
  add_docstr(torch.addcdiv, r"""
  addcdiv(input, tensor1, tensor2, *, value=1, out=None) -> Tensor
  
  Performs the element-wise division of :attr:`tensor1` by :attr:`tensor2`,
  multiply the result by the scalar :attr:`value` and add it to :attr:`input`.
  
  .. warning::
      Integer division with addcdiv is no longer supported, and in a future
      release addcdiv will perform a true division of tensor1 and tensor2.
      The historic addcdiv behavior can be implemented as
      (input + value * torch.trunc(tensor1 / tensor2)).to(input.dtype)
      for integer inputs and as (input + value * tensor1 / tensor2) for float inputs.
      The future addcdiv behavior is just the latter implementation:
      (input + value * tensor1 / tensor2), for all dtypes.
  
  .. math::
      \text{out}_i = \text{input}_i + \text{value} \times \frac{\text{tensor1}_i}{\text{tensor2}_i}
  """ + r"""
  
  The shapes of :attr:`input`, :attr:`tensor1`, and :attr:`tensor2` must be
  :ref:`broadcastable <broadcasting-semantics>`.
  
  For inputs of type `FloatTensor` or `DoubleTensor`, :attr:`value` must be
  a real number, otherwise an integer.
  
  Args:
      input (Tensor): the tensor to be added
      tensor1 (Tensor): the numerator tensor
      tensor2 (Tensor): the denominator tensor
  
  Keyword args:
      value (Number, optional): multiplier for :math:`\text{{tensor1}} / \text{{tensor2}}`
      {out}
  
  Example::
  
      >>> t = torch.randn(1, 3)
      >>> t1 = torch.randn(3, 1)
      >>> t2 = torch.randn(1, 3)
      >>> torch.addcdiv(t, t1, t2, value=0.1)
      tensor([[-0.2312, -3.6496,  0.1312],
              [-1.0428,  3.4292, -0.1030],
              [-0.5369, -0.9829,  0.0430]])
  """.format(**common_args))
  
  add_docstr(torch.addcmul,
             r"""
  addcmul(input, tensor1, tensor2, *, value=1, out=None) -> Tensor
  
  Performs the element-wise multiplication of :attr:`tensor1`
  by :attr:`tensor2`, multiply the result by the scalar :attr:`value`
  and add it to :attr:`input`.
  
  .. math::
      \text{out}_i = \text{input}_i + \text{value} \times \text{tensor1}_i \times \text{tensor2}_i
  """ + r"""
  The shapes of :attr:`tensor`, :attr:`tensor1`, and :attr:`tensor2` must be
  :ref:`broadcastable <broadcasting-semantics>`.
  
  For inputs of type `FloatTensor` or `DoubleTensor`, :attr:`value` must be
  a real number, otherwise an integer.
  
  Args:
      input (Tensor): the tensor to be added
      tensor1 (Tensor): the tensor to be multiplied
      tensor2 (Tensor): the tensor to be multiplied
  
  Keyword args:
      value (Number, optional): multiplier for :math:`tensor1 .* tensor2`
      {out}
  
  Example::
  
      >>> t = torch.randn(1, 3)
      >>> t1 = torch.randn(3, 1)
      >>> t2 = torch.randn(1, 3)
      >>> torch.addcmul(t, t1, t2, value=0.1)
      tensor([[-0.8635, -0.6391,  1.6174],
              [-0.7617, -0.5879,  1.7388],
              [-0.8353, -0.6249,  1.6511]])
  """.format(**common_args))
  
  add_docstr(torch.addmm,
             r"""
  addmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor
  
  Performs a matrix multiplication of the matrices :attr:`mat1` and :attr:`mat2`.
  The matrix :attr:`input` is added to the final result.
  
  If :attr:`mat1` is a :math:`(n \times m)` tensor, :attr:`mat2` is a
  :math:`(m \times p)` tensor, then :attr:`input` must be
  :ref:`broadcastable <broadcasting-semantics>` with a :math:`(n \times p)` tensor
  and :attr:`out` will be a :math:`(n \times p)` tensor.
  
  :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between
  :attr:`mat1` and :attr:`mat2` and the added matrix :attr:`input` respectively.
  
  .. math::
      \text{out} = \beta\ \text{input} + \alpha\ (\text{mat1}_i \mathbin{@} \text{mat2}_i)
  
  If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in
  it will not be propagated.
  """ + r"""
  For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and
  :attr:`alpha` must be real numbers, otherwise they should be integers.
  
  {tf32_note}
  
  Args:
      input (Tensor): matrix to be added
      mat1 (Tensor): the first matrix to be matrix multiplied
      mat2 (Tensor): the second matrix to be matrix multiplied
  
  Keyword args:
      beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`)
      alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`)
      {out}
  
  Example::
  
      >>> M = torch.randn(2, 3)
      >>> mat1 = torch.randn(2, 3)
      >>> mat2 = torch.randn(3, 3)
      >>> torch.addmm(M, mat1, mat2)
      tensor([[-4.8716,  1.4671, -1.3746],
              [ 0.7573, -3.9555, -2.8681]])
  """.format(**common_args, **tf32_notes))
  
  add_docstr(torch.sspaddmm,
             r"""
  sspaddmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor
  
  Matrix multiplies a sparse tensor :attr:`mat1` with a dense tensor
  :attr:`mat2`, then adds the sparse tensor :attr:`input` to the result.
  
  Note: This function is equivalent to :func:`torch.addmm`, except
  :attr:`input` and :attr:`mat1` are sparse.
  
  Args:
      input (Tensor): a sparse matrix to be added
      mat1 (Tensor): a sparse matrix to be matrix multiplied
      mat2 (Tensor): a dense matrix to be matrix multiplied
  
  Keyword args:
      beta (Number, optional): multiplier for :attr:`mat` (:math:`\beta`)
      alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`)
      {out}
  """.format(**common_args))
  
  add_docstr(torch.smm,
             r"""
  smm(input, mat) -> Tensor
  
  Performs a matrix multiplication of the sparse matrix :attr:`input`
  with the dense matrix :attr:`mat`.
  
  Args:
      input (Tensor): a sparse matrix to be matrix multiplied
      mat (Tensor): a dense matrix to be matrix multiplied
  """)
  
  add_docstr(torch.addmv,
             r"""
  addmv(input, mat, vec, *, beta=1, alpha=1, out=None) -> Tensor
  
  Performs a matrix-vector product of the matrix :attr:`mat` and
  the vector :attr:`vec`.
  The vector :attr:`input` is added to the final result.
  
  If :attr:`mat` is a :math:`(n \times m)` tensor, :attr:`vec` is a 1-D tensor of
  size `m`, then :attr:`input` must be
  :ref:`broadcastable <broadcasting-semantics>` with a 1-D tensor of size `n` and
  :attr:`out` will be 1-D tensor of size `n`.
  
  :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between
  :attr:`mat` and :attr:`vec` and the added tensor :attr:`input` respectively.
  
  .. math::
      \text{out} = \beta\ \text{input} + \alpha\ (\text{mat} \mathbin{@} \text{vec})
  
  If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in
  it will not be propagated.
  """ + r"""
  For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and
  :attr:`alpha` must be real numbers, otherwise they should be integers
  
  Args:
      input (Tensor): vector to be added
      mat (Tensor): matrix to be matrix multiplied
      vec (Tensor): vector to be matrix multiplied
  
  Keyword args:
      beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`)
      alpha (Number, optional): multiplier for :math:`mat @ vec` (:math:`\alpha`)
      {out}
  
  Example::
  
      >>> M = torch.randn(2)
      >>> mat = torch.randn(2, 3)
      >>> vec = torch.randn(3)
      >>> torch.addmv(M, mat, vec)
      tensor([-0.3768, -5.5565])
  """.format(**common_args))
  
  add_docstr(torch.addr,
             r"""
  addr(input, vec1, vec2, *, beta=1, alpha=1, out=None) -> Tensor
  
  Performs the outer-product of vectors :attr:`vec1` and :attr:`vec2`
  and adds it to the matrix :attr:`input`.
  
  Optional values :attr:`beta` and :attr:`alpha` are scaling factors on the
  outer product between :attr:`vec1` and :attr:`vec2` and the added matrix
  :attr:`input` respectively.
  
  .. math::
      \text{out} = \beta\ \text{input} + \alpha\ (\text{vec1} \otimes \text{vec2})
  
  If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in
  it will not be propagated.
  """ + r"""
  If :attr:`vec1` is a vector of size `n` and :attr:`vec2` is a vector
  of size `m`, then :attr:`input` must be
  :ref:`broadcastable <broadcasting-semantics>` with a matrix of size
  :math:`(n \times m)` and :attr:`out` will be a matrix of size
  :math:`(n \times m)`.
  
  Args:
      input (Tensor): matrix to be added
      vec1 (Tensor): the first vector of the outer product
      vec2 (Tensor): the second vector of the outer product
  
  Keyword args:
      beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`)
      alpha (Number, optional): multiplier for :math:`\text{{vec1}} \otimes \text{{vec2}}` (:math:`\alpha`)
      {out}
  
  Example::
  
      >>> vec1 = torch.arange(1., 4.)
      >>> vec2 = torch.arange(1., 3.)
      >>> M = torch.zeros(3, 2)
      >>> torch.addr(M, vec1, vec2)
      tensor([[ 1.,  2.],
              [ 2.,  4.],
              [ 3.,  6.]])
  """.format(**common_args))
  
  add_docstr(torch.allclose,
             r"""
  allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) -> bool
  
  This function checks if all :attr:`input` and :attr:`other` satisfy the condition:
  
  .. math::
      \lvert \text{input} - \text{other} \rvert \leq \texttt{atol} + \texttt{rtol} \times \lvert \text{other} \rvert
  """ + r"""
  elementwise, for all elements of :attr:`input` and :attr:`other`. The behaviour of this function is analogous to
  `numpy.allclose <https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html>`_
  
  Args:
      input (Tensor): first tensor to compare
      other (Tensor): second tensor to compare
      atol (float, optional): absolute tolerance. Default: 1e-08
      rtol (float, optional): relative tolerance. Default: 1e-05
      equal_nan (bool, optional): if ``True``, then two ``NaN`` s will be considered equal. Default: ``False``
  
  Example::
  
      >>> torch.allclose(torch.tensor([10000., 1e-07]), torch.tensor([10000.1, 1e-08]))
      False
      >>> torch.allclose(torch.tensor([10000., 1e-08]), torch.tensor([10000.1, 1e-09]))
      True
      >>> torch.allclose(torch.tensor([1.0, float('nan')]), torch.tensor([1.0, float('nan')]))
      False
      >>> torch.allclose(torch.tensor([1.0, float('nan')]), torch.tensor([1.0, float('nan')]), equal_nan=True)
      True
  """)
  
  add_docstr(torch.all,
             r"""
  all(input) -> Tensor
  
  Tests if all elements in :attr:`input` evaluate to `True`.
  
  .. note:: This function matches the behaviour of NumPy in returning
            output of dtype `bool` for all supported dtypes except `uint8`.
            For `uint8` the dtype of output is `uint8` itself.
  
  Example::
  
      >>> a = torch.rand(1, 2).bool()
      >>> a
      tensor([[False, True]], dtype=torch.bool)
      >>> torch.all(a)
      tensor(False, dtype=torch.bool)
      >>> a = torch.arange(0, 3)
      >>> a
      tensor([0, 1, 2])
      >>> torch.all(a)
      tensor(False)
  
  .. function:: all(input, dim, keepdim=False, *, out=None) -> Tensor
     :noindex:
  
  For each row of :attr:`input` in the given dimension :attr:`dim`,
  returns `True` if all elements in the row evaluate to `True` and `False` otherwise.
  
  {keepdim_details}
  
  Args:
      {input}
      {dim}
      {keepdim}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.rand(4, 2).bool()
      >>> a
      tensor([[True, True],
              [True, False],
              [True, True],
              [True, True]], dtype=torch.bool)
      >>> torch.all(a, dim=1)
      tensor([ True, False,  True,  True], dtype=torch.bool)
      >>> torch.all(a, dim=0)
      tensor([ True, False], dtype=torch.bool)
  """.format(**single_dim_common))
  
  add_docstr(torch.any,
             r"""
  any(input) -> Tensor
  
  Tests if any element in :attr:`input` evaluates to `True`.
  
  .. note:: This function matches the behaviour of NumPy in returning
            output of dtype `bool` for all supported dtypes except `uint8`.
            For `uint8` the dtype of output is `uint8` itself.
  
  Example::
  
      >>> a = torch.rand(1, 2).bool()
      >>> a
      tensor([[False, True]], dtype=torch.bool)
      >>> torch.any(a)
      tensor(True, dtype=torch.bool)
      >>> a = torch.arange(0, 3)
      >>> a
      tensor([0, 1, 2])
      >>> torch.any(a)
      tensor(True)
  
  .. function:: any(input, dim, keepdim=False, *, out=None) -> Tensor
     :noindex:
  
  For each row of :attr:`input` in the given dimension :attr:`dim`,
  returns `True` if any element in the row evaluate to `True` and `False` otherwise.
  
  {keepdim_details}
  
  Args:
      {input}
      {dim}
      {keepdim}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4, 2) < 0
      >>> a
      tensor([[ True,  True],
              [False,  True],
              [ True,  True],
              [False, False]])
      >>> torch.any(a, 1)
      tensor([ True,  True,  True, False])
      >>> torch.any(a, 0)
      tensor([True, True])
  """.format(**single_dim_common))
  
  add_docstr(torch.angle,
             r"""
  angle(input, *, out=None) -> Tensor
  
  Computes the element-wise angle (in radians) of the given :attr:`input` tensor.
  
  .. math::
      \text{out}_{i} = angle(\text{input}_{i})
  """ + r"""
  Args:
      {input}
  
  Keyword args:
      {out}
  
  .. note:: Starting in PyTorch 1.8, angle returns pi for negative real numbers,
            zero for non-negative real numbers, and propagates NaNs. Previously
            the function would return zero for all real numbers and not propagate
            floating-point NaNs.
  
  Example::
  
      >>> torch.angle(torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j]))*180/3.14159
      tensor([ 135.,  135,  -45])
  """.format(**common_args))
  
  add_docstr(torch.as_strided,
             r"""
  as_strided(input, size, stride, storage_offset=0) -> Tensor
  
  Create a view of an existing `torch.Tensor` :attr:`input` with specified
  :attr:`size`, :attr:`stride` and :attr:`storage_offset`.
  
  .. warning::
      Prefer using other view functions, like :meth:`torch.Tensor.expand`,
      to setting a view's strides manually with `as_strided`, as this
      function's behavior depends on the implementation of a tensor's storage.
      The constructed view of the storage must only refer to elements within
      the storage or a runtime error will be thrown, and if the view is
      "overlapped" (with multiple indices referring to the same element in
      memory) its behavior is undefined.
  
  Args:
      {input}
      size (tuple or ints): the shape of the output tensor
      stride (tuple or ints): the stride of the output tensor
      storage_offset (int, optional): the offset in the underlying storage of the output tensor
  
  Example::
  
      >>> x = torch.randn(3, 3)
      >>> x
      tensor([[ 0.9039,  0.6291,  1.0795],
              [ 0.1586,  2.1939, -0.4900],
              [-0.1909, -0.7503,  1.9355]])
      >>> t = torch.as_strided(x, (2, 2), (1, 2))
      >>> t
      tensor([[0.9039, 1.0795],
              [0.6291, 0.1586]])
      >>> t = torch.as_strided(x, (2, 2), (1, 2), 1)
      tensor([[0.6291, 0.1586],
              [1.0795, 2.1939]])
  """.format(**common_args))
  
  add_docstr(torch.as_tensor,
             r"""
  as_tensor(data, dtype=None, device=None) -> Tensor
  
  Convert the data into a `torch.Tensor`. If the data is already a `Tensor` with the same `dtype` and `device`,
  no copy will be performed, otherwise a new `Tensor` will be returned with computational graph retained if data
  `Tensor` has ``requires_grad=True``. Similarly, if the data is an ``ndarray`` of the corresponding `dtype` and
  the `device` is the cpu, no copy will be performed.
  
  Args:
      {data}
      {dtype}
      {device}
  
  Example::
  
      >>> a = numpy.array([1, 2, 3])
      >>> t = torch.as_tensor(a)
      >>> t
      tensor([ 1,  2,  3])
      >>> t[0] = -1
      >>> a
      array([-1,  2,  3])
  
      >>> a = numpy.array([1, 2, 3])
      >>> t = torch.as_tensor(a, device=torch.device('cuda'))
      >>> t
      tensor([ 1,  2,  3])
      >>> t[0] = -1
      >>> a
      array([1,  2,  3])
  """.format(**factory_data_common_args))
  
  add_docstr(torch.asin, r"""
  asin(input, *, out=None) -> Tensor
  
  Returns a new tensor with the arcsine  of the elements of :attr:`input`.
  
  .. math::
      \text{out}_{i} = \sin^{-1}(\text{input}_{i})
  """ + r"""
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([-0.5962,  1.4985, -0.4396,  1.4525])
      >>> torch.asin(a)
      tensor([-0.6387,     nan, -0.4552,     nan])
  """.format(**common_args))
  
  add_docstr(torch.arcsin, r"""
  arcsin(input, *, out=None) -> Tensor
  
  Alias for :func:`torch.asin`.
  """)
  
  add_docstr(torch.asinh,
             r"""
  asinh(input, *, out=None) -> Tensor
  
  Returns a new tensor with the inverse hyperbolic sine of the elements of :attr:`input`.
  
  .. math::
      \text{out}_{i} = \sinh^{-1}(\text{input}_{i})
  """ + r"""
  Args:
      {input}
  
  Keyword arguments:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([ 0.1606, -1.4267, -1.0899, -1.0250 ])
      >>> torch.asinh(a)
      tensor([ 0.1599, -1.1534, -0.9435, -0.8990 ])
  """.format(**common_args))
  
  add_docstr(torch.arcsinh, r"""
  arcsinh(input, *, out=None) -> Tensor
  
  Alias for :func:`torch.asinh`.
  """)
  
  add_docstr(torch.atan, r"""
  atan(input, *, out=None) -> Tensor
  
  Returns a new tensor with the arctangent  of the elements of :attr:`input`.
  
  .. math::
      \text{out}_{i} = \tan^{-1}(\text{input}_{i})
  """ + r"""
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([ 0.2341,  0.2539, -0.6256, -0.6448])
      >>> torch.atan(a)
      tensor([ 0.2299,  0.2487, -0.5591, -0.5727])
  """.format(**common_args))
  
  add_docstr(torch.arctan, r"""
  arctan(input, *, out=None) -> Tensor
  
  Alias for :func:`torch.atan`.
  """)
  
  add_docstr(torch.atan2,
             r"""
  atan2(input, other, *, out=None) -> Tensor
  
  Element-wise arctangent of :math:`\text{{input}}_{{i}} / \text{{other}}_{{i}}`
  with consideration of the quadrant. Returns a new tensor with the signed angles
  in radians between vector :math:`(\text{{other}}_{{i}}, \text{{input}}_{{i}})`
  and vector :math:`(1, 0)`. (Note that :math:`\text{{other}}_{{i}}`, the second
  parameter, is the x-coordinate, while :math:`\text{{input}}_{{i}}`, the first
  parameter, is the y-coordinate.)
  
  The shapes of ``input`` and ``other`` must be
  :ref:`broadcastable <broadcasting-semantics>`.
  
  Args:
      input (Tensor): the first input tensor
      other (Tensor): the second input tensor
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([ 0.9041,  0.0196, -0.3108, -2.4423])
      >>> torch.atan2(a, torch.randn(4))
      tensor([ 0.9833,  0.0811, -1.9743, -1.4151])
  """.format(**common_args))
  
  add_docstr(torch.atanh, r"""
  atanh(input, *, out=None) -> Tensor
  
  Returns a new tensor with the inverse hyperbolic tangent of the elements of :attr:`input`.
  
  Note:
      The domain of the inverse hyperbolic tangent is `(-1, 1)` and values outside this range
      will be mapped to ``NaN``, except for the values `1` and `-1` for which the output is
      mapped to `+/-INF` respectively.
  
  .. math::
      \text{out}_{i} = \tanh^{-1}(\text{input}_{i})
  """ + r"""
  Args:
      {input}
  
  Keyword arguments:
      {out}
  
  Example::
  
      >>> a = torch.randn(4).uniform_(-1, 1)
      >>> a
      tensor([ -0.9385, 0.2968, -0.8591, -0.1871 ])
      >>> torch.atanh(a)
      tensor([ -1.7253, 0.3060, -1.2899, -0.1893 ])
  """.format(**common_args))
  
  add_docstr(torch.arctanh, r"""
  arctanh(input, *, out=None) -> Tensor
  
  Alias for :func:`torch.atanh`.
  """)
  
  add_docstr(torch.baddbmm,
             r"""
  baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor
  
  Performs a batch matrix-matrix product of matrices in :attr:`batch1`
  and :attr:`batch2`.
  :attr:`input` is added to the final result.
  
  :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the same
  number of matrices.
  
  If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a
  :math:`(b \times m \times p)` tensor, then :attr:`input` must be
  :ref:`broadcastable <broadcasting-semantics>` with a
  :math:`(b \times n \times p)` tensor and :attr:`out` will be a
  :math:`(b \times n \times p)` tensor. Both :attr:`alpha` and :attr:`beta` mean the
  same as the scaling factors used in :meth:`torch.addbmm`.
  
  .. math::
      \text{out}_i = \beta\ \text{input}_i + \alpha\ (\text{batch1}_i \mathbin{@} \text{batch2}_i)
  
  If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in
  it will not be propagated.
  """ + r"""
  For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and
  :attr:`alpha` must be real numbers, otherwise they should be integers.
  
  {tf32_note}
  
  Args:
      input (Tensor): the tensor to be added
      batch1 (Tensor): the first batch of matrices to be multiplied
      batch2 (Tensor): the second batch of matrices to be multiplied
  
  Keyword args:
      beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`)
      alpha (Number, optional): multiplier for :math:`\text{{batch1}} \mathbin{{@}} \text{{batch2}}` (:math:`\alpha`)
      {out}
  
  Example::
  
      >>> M = torch.randn(10, 3, 5)
      >>> batch1 = torch.randn(10, 3, 4)
      >>> batch2 = torch.randn(10, 4, 5)
      >>> torch.baddbmm(M, batch1, batch2).size()
      torch.Size([10, 3, 5])
  """.format(**common_args, **tf32_notes))
  
  add_docstr(torch.bernoulli,
             r"""
  bernoulli(input, *, generator=None, out=None) -> Tensor
  
  Draws binary random numbers (0 or 1) from a Bernoulli distribution.
  
  The :attr:`input` tensor should be a tensor containing probabilities
  to be used for drawing the binary random number.
  Hence, all values in :attr:`input` have to be in the range:
  :math:`0 \leq \text{input}_i \leq 1`.
  
  The :math:`\text{i}^{th}` element of the output tensor will draw a
  value :math:`1` according to the :math:`\text{i}^{th}` probability value given
  in :attr:`input`.
  
  .. math::
      \text{out}_{i} \sim \mathrm{Bernoulli}(p = \text{input}_{i})
  """ + r"""
  The returned :attr:`out` tensor only has values 0 or 1 and is of the same
  shape as :attr:`input`.
  
  :attr:`out` can have integral ``dtype``, but :attr:`input` must have floating
  point ``dtype``.
  
  Args:
      input (Tensor): the input tensor of probability values for the Bernoulli distribution
  
  Keyword args:
      {generator}
      {out}
  
  Example::
  
      >>> a = torch.empty(3, 3).uniform_(0, 1)  # generate a uniform random matrix with range [0, 1]
      >>> a
      tensor([[ 0.1737,  0.0950,  0.3609],
              [ 0.7148,  0.0289,  0.2676],
              [ 0.9456,  0.8937,  0.7202]])
      >>> torch.bernoulli(a)
      tensor([[ 1.,  0.,  0.],
              [ 0.,  0.,  0.],
              [ 1.,  1.,  1.]])
  
      >>> a = torch.ones(3, 3) # probability of drawing "1" is 1
      >>> torch.bernoulli(a)
      tensor([[ 1.,  1.,  1.],
              [ 1.,  1.,  1.],
              [ 1.,  1.,  1.]])
      >>> a = torch.zeros(3, 3) # probability of drawing "1" is 0
      >>> torch.bernoulli(a)
      tensor([[ 0.,  0.,  0.],
              [ 0.,  0.,  0.],
              [ 0.,  0.,  0.]])
  """.format(**common_args))
  
  add_docstr(torch.bincount,
             r"""
  bincount(input, weights=None, minlength=0) -> Tensor
  
  Count the frequency of each value in an array of non-negative ints.
  
  The number of bins (size 1) is one larger than the largest value in
  :attr:`input` unless :attr:`input` is empty, in which case the result is a
  tensor of size 0. If :attr:`minlength` is specified, the number of bins is at least
  :attr:`minlength` and if :attr:`input` is empty, then the result is tensor of size
  :attr:`minlength` filled with zeros. If ``n`` is the value at position ``i``,
  ``out[n] += weights[i]`` if :attr:`weights` is specified else
  ``out[n] += 1``.
  
  Note:
      {backward_reproducibility_note}
  
  Arguments:
      input (Tensor): 1-d int tensor
      weights (Tensor): optional, weight for each value in the input tensor.
          Should be of same size as input tensor.
      minlength (int): optional, minimum number of bins. Should be non-negative.
  
  Returns:
      output (Tensor): a tensor of shape ``Size([max(input) + 1])`` if
      :attr:`input` is non-empty, else ``Size(0)``
  
  Example::
  
      >>> input = torch.randint(0, 8, (5,), dtype=torch.int64)
      >>> weights = torch.linspace(0, 1, steps=5)
      >>> input, weights
      (tensor([4, 3, 6, 3, 4]),
       tensor([ 0.0000,  0.2500,  0.5000,  0.7500,  1.0000])
  
      >>> torch.bincount(input)
      tensor([0, 0, 0, 2, 2, 0, 1])
  
      >>> input.bincount(weights)
      tensor([0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 0.0000, 0.5000])
  """.format(**reproducibility_notes))
  
  add_docstr(torch.bitwise_not,
             r"""
  bitwise_not(input, *, out=None) -> Tensor
  
  Computes the bitwise NOT of the given input tensor. The input tensor must be of
  integral or Boolean types. For bool tensors, it computes the logical NOT.
  
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example:
  
      >>> torch.bitwise_not(torch.tensor([-1, -2, 3], dtype=torch.int8))
      tensor([ 0,  1, -4], dtype=torch.int8)
  """.format(**common_args))
  
  add_docstr(torch.bmm,
             r"""
  bmm(input, mat2, *, out=None) -> Tensor
  
  Performs a batch matrix-matrix product of matrices stored in :attr:`input`
  and :attr:`mat2`.
  
  :attr:`input` and :attr:`mat2` must be 3-D tensors each containing
  the same number of matrices.
  
  If :attr:`input` is a :math:`(b \times n \times m)` tensor, :attr:`mat2` is a
  :math:`(b \times m \times p)` tensor, :attr:`out` will be a
  :math:`(b \times n \times p)` tensor.
  
  .. math::
      \text{out}_i = \text{input}_i \mathbin{@} \text{mat2}_i
  """ + r"""
  {tf32_note}
  
  .. note:: This function does not :ref:`broadcast <broadcasting-semantics>`.
            For broadcasting matrix products, see :func:`torch.matmul`.
  
  Args:
      input (Tensor): the first batch of matrices to be multiplied
      mat2 (Tensor): the second batch of matrices to be multiplied
  
  Keyword Args:
      {out}
  
  Example::
  
      >>> input = torch.randn(10, 3, 4)
      >>> mat2 = torch.randn(10, 4, 5)
      >>> res = torch.bmm(input, mat2)
      >>> res.size()
      torch.Size([10, 3, 5])
  """.format(**common_args, **tf32_notes))
  
  add_docstr(torch.bitwise_and,
             r"""
  bitwise_and(input, other, *, out=None) -> Tensor
  
  Computes the bitwise AND of :attr:`input` and :attr:`other`. The input tensor must be of
  integral or Boolean types. For bool tensors, it computes the logical AND.
  
  Args:
      input: the first input tensor
      other: the second input tensor
  
  Keyword args:
      {out}
  
  Example:
  
      >>> torch.bitwise_and(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8))
      tensor([1, 0,  3], dtype=torch.int8)
      >>> torch.bitwise_and(torch.tensor([True, True, False]), torch.tensor([False, True, False]))
      tensor([ False, True, False])
  """.format(**common_args))
  
  add_docstr(torch.bitwise_or,
             r"""
  bitwise_or(input, other, *, out=None) -> Tensor
  
  Computes the bitwise OR of :attr:`input` and :attr:`other`. The input tensor must be of
  integral or Boolean types. For bool tensors, it computes the logical OR.
  
  Args:
      input: the first input tensor
      other: the second input tensor
  
  Keyword args:
      {out}
  
  Example:
  
      >>> torch.bitwise_or(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8))
      tensor([-1, -2,  3], dtype=torch.int8)
      >>> torch.bitwise_or(torch.tensor([True, True, False]), torch.tensor([False, True, False]))
      tensor([ True, True, False])
  """.format(**common_args))
  
  add_docstr(torch.bitwise_xor,
             r"""
  bitwise_xor(input, other, *, out=None) -> Tensor
  
  Computes the bitwise XOR of :attr:`input` and :attr:`other`. The input tensor must be of
  integral or Boolean types. For bool tensors, it computes the logical XOR.
  
  Args:
      input: the first input tensor
      other: the second input tensor
  
  Keyword args:
      {out}
  
  Example:
  
      >>> torch.bitwise_xor(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8))
      tensor([-2, -2,  0], dtype=torch.int8)
      >>> torch.bitwise_xor(torch.tensor([True, True, False]), torch.tensor([False, True, False]))
      tensor([ True, False, False])
  """.format(**common_args))
  
  add_docstr(torch.bitwise_left_shift,
             r"""
  bitwise_left_shift(input, other, *, out=None) -> Tensor
  
  Computes the left arithmetic shift of :attr:`input` by :attr:`other` bits.
  The result will have the same dtype as :attr:`input`.
  
  The operation applied is:
  
  .. math::
      \text{{out}}_i = \text{{input}}_i \times 2 ^ {{\text{{other}}_i}}
  
  Args:
      input (Tensor or Scalar): the first input tensor
      other (Tensor or Scalar): the second input tensor
  
  Keyword args:
      {out}
  
  Example:
  
      >>> torch.bitwise_left_shift(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8))
      tensor([-2, -2, 24], dtype=torch.int8)
  """.format(**common_args))
  
  add_docstr(torch.bitwise_right_shift,
             r"""
  bitwise_right_shift(input, other, *, out=None) -> Tensor
  
  Computes the right arithmetic shift of :attr:`input` by :attr:`other` bits.
  The result will have the same dtype as :attr:`input`.
  
  The operation applied is:
  
  .. math::
      \text{{out}}_i = \text{{input}}_i / 2 ^ {{\text{{other}}_i}}
  
  Args:
      input (Tensor or Scalar): the first input tensor
      other (Tensor or Scalar): the second input tensor
  
  Keyword args:
      {out}
  
  Example:
  
      >>> torch.bitwise_right_shift(torch.tensor([-2, -7, 31], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8))
      tensor([-1, -7,  3], dtype=torch.int8)
  """.format(**common_args))
  
  add_docstr(torch.broadcast_to,
             r"""
  broadcast_to(input, shape) -> Tensor
  
  Broadcasts :attr:`input` to the shape :attr:`\shape`.
  Equivalent to calling ``input.expand(shape)``. See :meth:`~Tensor.expand` for details.
  
  Args:
      {input}
      shape (list, tuple, or :class:`torch.Size`): the new shape.
  
  Example::
  
      >>> x = torch.tensor([1, 2, 3])
      >>> torch.broadcast_to(x, (3, 3))
      tensor([[1, 2, 3],
              [1, 2, 3],
              [1, 2, 3]])
  """.format(**common_args))
  
  add_docstr(torch.stack,
             r"""
  stack(tensors, dim=0, *, out=None) -> Tensor
  
  Concatenates a sequence of tensors along a new dimension.
  
  All tensors need to be of the same size.
  
  Arguments:
      tensors (sequence of Tensors): sequence of tensors to concatenate
      dim (int): dimension to insert. Has to be between 0 and the number
          of dimensions of concatenated tensors (inclusive)
  
  Keyword args:
      {out}
  """.format(**common_args))
  
  add_docstr(torch.hstack,
             r"""
  hstack(tensors, *, out=None) -> Tensor
  
  Stack tensors in sequence horizontally (column wise).
  
  This is equivalent to concatenation along the first axis for 1-D tensors, and along the second axis for all other tensors.
  
  Args:
      tensors (sequence of Tensors): sequence of tensors to concatenate
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.tensor([1, 2, 3])
      >>> b = torch.tensor([4, 5, 6])
      >>> torch.hstack((a,b))
      tensor([1, 2, 3, 4, 5, 6])
      >>> a = torch.tensor([[1],[2],[3]])
      >>> b = torch.tensor([[4],[5],[6]])
      >>> torch.hstack((a,b))
      tensor([[1, 4],
              [2, 5],
              [3, 6]])
  
  """.format(**common_args))
  
  add_docstr(torch.vstack,
             r"""
  vstack(tensors, *, out=None) -> Tensor
  
  Stack tensors in sequence vertically (row wise).
  
  This is equivalent to concatenation along the first axis after all 1-D tensors have been reshaped by :func:`torch.atleast_2d`.
  
  Args:
      tensors (sequence of Tensors): sequence of tensors to concatenate
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.tensor([1, 2, 3])
      >>> b = torch.tensor([4, 5, 6])
      >>> torch.vstack((a,b))
      tensor([[1, 2, 3],
              [4, 5, 6]])
      >>> a = torch.tensor([[1],[2],[3]])
      >>> b = torch.tensor([[4],[5],[6]])
      >>> torch.vstack((a,b))
      tensor([[1],
              [2],
              [3],
              [4],
              [5],
              [6]])
  
  
  """.format(**common_args))
  
  add_docstr(torch.dstack,
             r"""
  dstack(tensors, *, out=None) -> Tensor
  
  Stack tensors in sequence depthwise (along third axis).
  
  This is equivalent to concatenation along the third axis after 1-D and 2-D tensors have been reshaped by :func:`torch.atleast_3d`.
  
  Args:
      tensors (sequence of Tensors): sequence of tensors to concatenate
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.tensor([1, 2, 3])
      >>> b = torch.tensor([4, 5, 6])
      >>> torch.dstack((a,b))
      tensor([[[1, 4],
               [2, 5],
               [3, 6]]])
      >>> a = torch.tensor([[1],[2],[3]])
      >>> b = torch.tensor([[4],[5],[6]])
      >>> torch.dstack((a,b))
      tensor([[[1, 4]],
              [[2, 5]],
              [[3, 6]]])
  
  
  """.format(**common_args))
  
  add_docstr(torch.tensor_split,
             r"""
  tensor_split(input, indices_or_sections, dim=0) -> List of Tensors
  
  Splits a tensor into multiple sub-tensors, all of which are views of :attr:`input`,
  along dimension :attr:`dim` according to the indices or number of sections specified
  by :attr:`indices_or_sections`. This function is based on NumPy's
  :func:`numpy.array_split`.
  
  Args:
      input (Tensor): the tensor to split
      indices_or_sections (Tensor, int or list or tuple of ints):
          If :attr:`indices_or_sections` is an integer ``n`` or a zero dimensional long tensor
          with value ``n``, :attr:`input` is split into ``n`` sections along dimension :attr:`dim`.
          If :attr:`input` is divisible by ``n`` along dimension :attr:`dim`, each
          section will be of equal size, :code:`input.size(dim) / n`. If :attr:`input`
          is not divisible by ``n``, the sizes of the first :code:`int(input.size(dim) % n)`
          sections will have size :code:`int(input.size(dim) / n) + 1`, and the rest will
          have size :code:`int(input.size(dim) / n)`.
  
          If :attr:`indices_or_sections` is a list or tuple of ints, or a one-dimensional long
          tensor, then :attr:`input` is split along dimension :attr:`dim` at each of the indices
          in the list, tuple or tensor. For instance, :code:`indices_or_sections=[2, 3]` and :code:`dim=0`
          would result in the tensors :code:`input[:2]`, :code:`input[2:3]`, and :code:`input[3:]`.
  
          If indices_or_sections is a tensor, it must be a zero-dimensional or one-dimensional
          long tensor on the CPU.
  
      dim (int, optional): dimension along which to split the tensor. Default: ``0``
  
  Example::
  
      >>> x = torch.arange(8)
      >>> torch.tensor_split(x, 3)
      (tensor([0, 1, 2]), tensor([3, 4, 5]), tensor([6, 7]))
  
      >>> x = torch.arange(7)
      >>> torch.tensor_split(x, 3)
      (tensor([0, 1, 2]), tensor([3, 4]), tensor([5, 6]))
      >>> torch.tensor_split(x, (1, 6))
      (tensor([0]), tensor([1, 2, 3, 4, 5]), tensor([6]))
  
      >>> x = torch.arange(14).reshape(2, 7)
      >>> x
      tensor([[ 0,  1,  2,  3,  4,  5,  6],
              [ 7,  8,  9, 10, 11, 12, 13]])
      >>> torch.tensor_split(x, 3, dim=1)
      (tensor([[0, 1, 2],
              [7, 8, 9]]),
       tensor([[ 3,  4],
              [10, 11]]),
       tensor([[ 5,  6],
              [12, 13]]))
      >>> torch.tensor_split(x, (1, 6), dim=1)
      (tensor([[0],
              [7]]),
       tensor([[ 1,  2,  3,  4,  5],
              [ 8,  9, 10, 11, 12]]),
       tensor([[ 6],
              [13]]))
  """)
  
  add_docstr(torch.chunk,
             r"""
  chunk(input, chunks, dim=0) -> List of Tensors
  
  Attempts to split a tensor into the specified number of chunks. Each chunk is a view of
  the input tensor.
  
  
  .. note::
  
      This function may return less then the specified number of chunks!
  
  .. seealso::
  
      :func:`torch.tensor_split` a function that always returns exactly the specified number of chunks
  
  If the tensor size along the given dimesion :attr:`dim` is divisible by :attr:`chunks`,
  all returned chunks will be the same size.
  If the tensor size along the given dimension :attr:`dim` is not divisible by :attr:`chunks`,
  all returned chunks will be the same size, except the last one.
  If such division is not possible, this function may return less
  than the specified number of chunks.
  
  Arguments:
      input (Tensor): the tensor to split
      chunks (int): number of chunks to return
      dim (int): dimension along which to split the tensor
  
  Example::
      >>> torch.arange(11).chunk(6)
      (tensor([0, 1]),
       tensor([2, 3]),
       tensor([4, 5]),
       tensor([6, 7]),
       tensor([8, 9]),
       tensor([10]))
      >>> torch.arange(12).chunk(6)
      (tensor([0, 1]),
       tensor([2, 3]),
       tensor([4, 5]),
       tensor([6, 7]),
       tensor([8, 9]),
       tensor([10, 11]))
      >>> torch.arange(13).chunk(6)
      (tensor([0, 1, 2]),
       tensor([3, 4, 5]),
       tensor([6, 7, 8]),
       tensor([ 9, 10, 11]),
       tensor([12]))
  """)
  
  add_docstr(torch.unsafe_chunk,
             r"""
  unsafe_chunk(input, chunks, dim=0) -> List of Tensors
  
  Works like :func:`torch.chunk` but without enforcing the autograd restrictions
  on inplace modification of the outputs.
  
  .. warning::
      This function is safe to use as long as only the input, or only the outputs
      are modified inplace after calling this function. It is user's
      responsibility to ensure that is the case. If both the input and one or more
      of the outputs are modified inplace, gradients computed by autograd will be
      silently incorrect.
  """)
  
  add_docstr(torch.unsafe_split,
             r"""
  unsafe_split(tensor, split_size_or_sections, dim=0) -> List of Tensors
  
  Works like :func:`torch.split` but without enforcing the autograd restrictions
  on inplace modification of the outputs.
  
  .. warning::
      This function is safe to use as long as only the input, or only the outputs
      are modified inplace after calling this function. It is user's
      responsibility to ensure that is the case. If both the input and one or more
      of the outputs are modified inplace, gradients computed by autograd will be
      silently incorrect.
  """)
  
  add_docstr(torch.hsplit,
             r"""
  hsplit(input, indices_or_sections) -> List of Tensors
  
  Splits :attr:`input`, a tensor with one or more dimensions, into multiple tensors
  horizontally according to :attr:`indices_or_sections`. Each split is a view of
  :attr:`input`.
  
  If :attr:`input` is one dimensional this is equivalent to calling
  torch.tensor_split(input, indices_or_sections, dim=0) (the split dimension is
  zero), and if :attr:`input` has two or more dimensions it's equivalent to calling
  torch.tensor_split(input, indices_or_sections, dim=1) (the split dimension is 1),
  except that if :attr:`indices_or_sections` is an integer it must evenly divide
  the split dimension or a runtime error will be thrown.
  
  This function is based on NumPy's :func:`numpy.hsplit`.
  
  Args:
      input (Tensor): tensor to split.
      indices_or_sections (Tensor, int or list or tuple of ints): See argument in :func:`torch.tensor_split`.
  
  Example::
      >>> t = torch.arange(16.0).reshape(4,4)
      >>> t
      tensor([[ 0.,  1.,  2.,  3.],
              [ 4.,  5.,  6.,  7.],
              [ 8.,  9., 10., 11.],
              [12., 13., 14., 15.]])
      >>> torch.hsplit(t, 2)
      (tensor([[ 0.,  1.],
               [ 4.,  5.],
               [ 8.,  9.],
               [12., 13.]]),
       tensor([[ 2.,  3.],
               [ 6.,  7.],
               [10., 11.],
               [14., 15.]]))
      >>> torch.hsplit(t, [3, 6])
      (tensor([[ 0.,  1.,  2.],
               [ 4.,  5.,  6.],
               [ 8.,  9., 10.],
               [12., 13., 14.]]),
       tensor([[ 3.],
               [ 7.],
               [11.],
               [15.]]),
       tensor([], size=(4, 0)))
  
  """)
  
  add_docstr(torch.vsplit,
             r"""
  vsplit(input, indices_or_sections) -> List of Tensors
  
  Splits :attr:`input`, a tensor with two or more dimensions, into multiple tensors
  vertically according to :attr:`indices_or_sections`. Each split is a view of
  :attr:`input`.
  
  This is equivalent to calling torch.tensor_split(input, indices_or_sections, dim=0)
  (the split dimension is 0), except that if :attr:`indices_or_sections` is an integer
  it must evenly divide the split dimension or a runtime error will be thrown.
  
  This function is based on NumPy's :func:`numpy.vsplit`.
  
  Args:
      input (Tensor): tensor to split.
      indices_or_sections (Tensor, int or list or tuple of ints): See argument in :func:`torch.tensor_split`.
  
  Example::
      >>> t = torch.arange(16.0).reshape(4,4)
      >>> t
      tensor([[ 0.,  1.,  2.,  3.],
              [ 4.,  5.,  6.,  7.],
              [ 8.,  9., 10., 11.],
              [12., 13., 14., 15.]])
      >>> torch.vsplit(t, 2)
      (tensor([[0., 1., 2., 3.],
               [4., 5., 6., 7.]]),
       tensor([[ 8.,  9., 10., 11.],
               [12., 13., 14., 15.]]))
      >>> torch.vsplit(t, [3, 6])
      (tensor([[ 0.,  1.,  2.,  3.],
               [ 4.,  5.,  6.,  7.],
               [ 8.,  9., 10., 11.]]),
       tensor([[12., 13., 14., 15.]]),
       tensor([], size=(0, 4)))
  
  """)
  
  add_docstr(torch.dsplit,
             r"""
  dsplit(input, indices_or_sections) -> List of Tensors
  
  Splits :attr:`input`, a tensor with three or more dimensions, into multiple tensors
  depthwise according to :attr:`indices_or_sections`. Each split is a view of
  :attr:`input`.
  
  This is equivalent to calling torch.tensor_split(input, indices_or_sections, dim=2)
  (the split dimension is 1), except that if :attr:`indices_or_sections` is an integer
  it must evenly divide the split dimension or a runtime error will be thrown.
  
  This function is based on NumPy's :func:`numpy.dsplit`.
  
  Args:
      input (Tensor): tensor to split.
      indices_or_sections (Tensor, int or list or tuple of ints): See argument in :func:`torch.tensor_split`.
  
  Example::
      >>> t = torch.arange(16.0).reshape(2, 2, 4)
      >>> t
      tensor([[[ 0.,  1.,  2.,  3.],
               [ 4.,  5.,  6.,  7.]],
              [[ 8.,  9., 10., 11.],
               [12., 13., 14., 15.]]])
      >>> torch.dsplit(t, 2)
      (tensor([[[ 0.,  1.],
              [ 4.,  5.]],
             [[ 8.,  9.],
              [12., 13.]]]),
       tensor([[[ 2.,  3.],
                [ 6.,  7.]],
               [[10., 11.],
                [14., 15.]]]))
  
      >>> torch.dsplit(t, [3, 6])
      (tensor([[[ 0.,  1.,  2.],
                [ 4.,  5.,  6.]],
               [[ 8.,  9., 10.],
                [12., 13., 14.]]]),
       tensor([[[ 3.],
                [ 7.]],
               [[11.],
                [15.]]]),
       tensor([], size=(2, 2, 0)))
  
  """)
  
  add_docstr(torch.can_cast,
             r"""
  can_cast(from, to) -> bool
  
  Determines if a type conversion is allowed under PyTorch casting rules
  described in the type promotion :ref:`documentation <type-promotion-doc>`.
  
  Args:
      from (dtype): The original :class:`torch.dtype`.
      to (dtype): The target :class:`torch.dtype`.
  
  Example::
  
      >>> torch.can_cast(torch.double, torch.float)
      True
      >>> torch.can_cast(torch.float, torch.int)
      False
  """)
  
  add_docstr(torch.corrcoef, r"""
  corrcoef(input) -> Tensor
  
  Estimates the Pearson product-moment correlation coefficient matrix of the variables given by the :attr:`input` matrix,
  where rows are the variables and columns are the observations.
  
  .. note::
  
      The correlation coefficient matrix R is computed using the covariance matrix C as given by
      :math:`R_{ij} = \frac{ C_{ij} } { \sqrt{ C_{ii} * C_{jj} } }`
  
  .. note::
  
      Due to floating point rounding, the resulting array may not be Hermitian and its diagonal elements may not be 1.
      The real and imaginary values are clipped to the interval [-1, 1] in an attempt to improve this situation.
  
  Args:
      input (Tensor): A 2D matrix containing multiple variables and observations, or a
          Scalar or 1D vector representing a single variable.
  
  Returns:
      (Tensor) The correlation coefficient matrix of the variables.
  
  .. seealso::
  
          :func:`torch.cov` covariance matrix.
  
  Example::
  
      >>> x = torch.tensor([[0, 1, 2], [2, 1, 0]])
      >>> torch.corrcoef(x)
      tensor([[ 1., -1.],
              [-1.,  1.]])
      >>> x = torch.randn(2, 4)
      >>> x
      tensor([[-0.2678, -0.0908, -0.3766,  0.2780],
              [-0.5812,  0.1535,  0.2387,  0.2350]])
      >>> torch.corrcoef(x)
      tensor([[1.0000, 0.3582],
              [0.3582, 1.0000]])
      >>> torch.corrcoef(x[0])
      tensor(1.)
  """)
  
  add_docstr(torch.cov, r"""
  cov(input, *, correction=1, fweights=None, aweights=None) -> Tensor
  
  Estimates the covariance matrix of the variables given by the :attr:`input` matrix, where rows are
  the variables and columns are the observations.
  
  A covariance matrix is a square matrix giving the covariance of each pair of variables. The diagonal contains
  the variance of each variable (covariance of a variable with itself). By definition, if :attr:`input` represents
  a single variable (Scalar or 1D) then its variance is returned.
  
  The unbiased sample covariance of the variables :math:`x` and :math:`y` is given by:
  
  .. math::
      \text{cov}_w(x,y) = \frac{\sum^{N}_{i = 1}(x_{i} - \bar{x})(y_{i} - \bar{y})}{N~-~1}
  
  where :math:`\bar{x}` and :math:`\bar{y}` are the simple means of the :math:`x` and :math:`y` respectively.
  
  If :attr:`fweights` and/or :attr:`aweights` are provided, the unbiased weighted covariance
  is calculated, which is given by:
  
  .. math::
      \text{cov}_w(x,y) = \frac{\sum^{N}_{i = 1}w_i(x_{i} - \mu_x^*)(y_{i} - \mu_y^*)}{\sum^{N}_{i = 1}w_i~-~1}
  
  where :math:`w` denotes :attr:`fweights` or :attr:`aweights` based on whichever is provided, or
  :math:`w = fweights \times aweights` if both are provided, and
  :math:`\mu_x^* = \frac{\sum^{N}_{i = 1}w_ix_{i} }{\sum^{N}_{i = 1}w_i}` is the weighted mean of the variable.
  
  Args:
      input (Tensor): A 2D matrix containing multiple variables and observations, or a
          Scalar or 1D vector representing a single variable.
  
  Keyword Args:
      correction (int, optional): difference between the sample size and sample degrees of freedom.
          Defaults to Bessel's correction, ``correction = 1`` which returns the unbiased estimate,
          even if both :attr:`fweights` and :attr:`aweights` are specified. ``correction = 0``
          will return the simple average. Defaults to ``1``.
      fweights (tensor, optional): A Scalar or 1D tensor of observation vector frequencies representing the number of
          times each observation should be repeated. Its numel must equal the number of columns of :attr:`input`.
          Must have integral dtype. Ignored if ``None``. `Defaults to ``None``.
      aweights (tensor, optional): A Scalar or 1D array of observation vector weights.
          These relative weights are typically large for observations considered “important” and smaller for
          observations considered less “important”. Its numel must equal the number of columns of :attr:`input`.
          Must have floating point dtype. Ignored if ``None``. `Defaults to ``None``.
  
  Returns:
      (Tensor) The covariance matrix of the variables.
  
  .. seealso::
  
          :func:`torch.corrcoef` normalized covariance matrix.
  
  Example::
      >>> x = torch.tensor([[0, 2], [1, 1], [2, 0]]).T
      >>> x
      tensor([[0, 1, 2],
              [2, 1, 0]])
      >>> torch.cov(x)
      tensor([[ 1., -1.],
              [-1.,  1.]])
      >>> torch.cov(x, correction=0)
      tensor([[ 0.6667, -0.6667],
              [-0.6667,  0.6667]])
      >>> fw = torch.randint(1, 10, (3,))
      >>> fw
      tensor([1, 6, 9])
      >>> aw = torch.rand(3)
      >>> aw
      tensor([0.4282, 0.0255, 0.4144])
      >>> torch.cov(x, fweights=fw, aweights=aw)
      tensor([[ 0.4169, -0.4169],
              [-0.4169,  0.4169]])
  """)
  
  add_docstr(torch.cat,
             r"""
  cat(tensors, dim=0, *, out=None) -> Tensor
  
  Concatenates the given sequence of :attr:`seq` tensors in the given dimension.
  All tensors must either have the same shape (except in the concatenating
  dimension) or be empty.
  
  :func:`torch.cat` can be seen as an inverse operation for :func:`torch.split`
  and :func:`torch.chunk`.
  
  :func:`torch.cat` can be best understood via examples.
  
  Args:
      tensors (sequence of Tensors): any python sequence of tensors of the same type.
          Non-empty tensors provided must have the same shape, except in the
          cat dimension.
      dim (int, optional): the dimension over which the tensors are concatenated
  
  Keyword args:
      {out}
  
  Example::
  
      >>> x = torch.randn(2, 3)
      >>> x
      tensor([[ 0.6580, -1.0969, -0.4614],
              [-0.1034, -0.5790,  0.1497]])
      >>> torch.cat((x, x, x), 0)
      tensor([[ 0.6580, -1.0969, -0.4614],
              [-0.1034, -0.5790,  0.1497],
              [ 0.6580, -1.0969, -0.4614],
              [-0.1034, -0.5790,  0.1497],
              [ 0.6580, -1.0969, -0.4614],
              [-0.1034, -0.5790,  0.1497]])
      >>> torch.cat((x, x, x), 1)
      tensor([[ 0.6580, -1.0969, -0.4614,  0.6580, -1.0969, -0.4614,  0.6580,
               -1.0969, -0.4614],
              [-0.1034, -0.5790,  0.1497, -0.1034, -0.5790,  0.1497, -0.1034,
               -0.5790,  0.1497]])
  """.format(**common_args))
  
  add_docstr(torch.concat,
             r"""
  concat(tensors, dim=0, *, out=None) -> Tensor
  
  Alias of :func:`torch.cat`.
  """)
  
  add_docstr(torch.ceil,
             r"""
  ceil(input, *, out=None) -> Tensor
  
  Returns a new tensor with the ceil of the elements of :attr:`input`,
  the smallest integer greater than or equal to each element.
  
  .. math::
      \text{out}_{i} = \left\lceil \text{input}_{i} \right\rceil
  """ + r"""
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([-0.6341, -1.4208, -1.0900,  0.5826])
      >>> torch.ceil(a)
      tensor([-0., -1., -1.,  1.])
  """.format(**common_args))
  
  add_docstr(torch.real,
             r"""
  real(input) -> Tensor
  
  Returns a new tensor containing real values of the :attr:`self` tensor.
  The returned tensor and :attr:`self` share the same underlying storage.
  
  .. warning::
      :func:`real` is only supported for tensors with complex dtypes.
  
  Args:
      {input}
  
  Example::
  
      >>> x=torch.randn(4, dtype=torch.cfloat)
      >>> x
      tensor([(0.3100+0.3553j), (-0.5445-0.7896j), (-1.6492-0.0633j), (-0.0638-0.8119j)])
      >>> x.real
      tensor([ 0.3100, -0.5445, -1.6492, -0.0638])
  
  """.format(**common_args))
  
  add_docstr(torch.imag,
             r"""
  imag(input) -> Tensor
  
  Returns a new tensor containing imaginary values of the :attr:`self` tensor.
  The returned tensor and :attr:`self` share the same underlying storage.
  
  .. warning::
      :func:`imag` is only supported for tensors with complex dtypes.
  
  Args:
      {input}
  
  Example::
  
      >>> x=torch.randn(4, dtype=torch.cfloat)
      >>> x
      tensor([(0.3100+0.3553j), (-0.5445-0.7896j), (-1.6492-0.0633j), (-0.0638-0.8119j)])
      >>> x.imag
      tensor([ 0.3553, -0.7896, -0.0633, -0.8119])
  
  """.format(**common_args))
  
  add_docstr(torch.view_as_real,
             r"""
  view_as_real(input) -> Tensor
  
  Returns a view of :attr:`input` as a real tensor. For an input complex tensor of
  :attr:`size` :math:`m1, m2, \dots, mi`, this function returns a new
  real tensor of size :math:`m1, m2, \dots, mi, 2`, where the last dimension of size 2
  represents the real and imaginary components of complex numbers.
  
  .. warning::
      :func:`view_as_real` is only supported for tensors with ``complex dtypes``.
  
  Args:
      {input}
  
  Example::
  
      >>> x=torch.randn(4, dtype=torch.cfloat)
      >>> x
      tensor([(0.4737-0.3839j), (-0.2098-0.6699j), (0.3470-0.9451j), (-0.5174-1.3136j)])
      >>> torch.view_as_real(x)
      tensor([[ 0.4737, -0.3839],
              [-0.2098, -0.6699],
              [ 0.3470, -0.9451],
              [-0.5174, -1.3136]])
  """.format(**common_args))
  
  add_docstr(torch.view_as_complex,
             r"""
  view_as_complex(input) -> Tensor
  
  Returns a view of :attr:`input` as a complex tensor. For an input complex
  tensor of :attr:`size` :math:`m1, m2, \dots, mi, 2`, this function returns a
  new complex tensor of :attr:`size` :math:`m1, m2, \dots, mi` where the last
  dimension of the input tensor is expected to represent the real and imaginary
  components of complex numbers.
  
  .. warning::
      :func:`view_as_complex` is only supported for tensors with
      :class:`torch.dtype` ``torch.float64`` and ``torch.float32``.  The input is
      expected to have the last dimension of :attr:`size` 2. In addition, the
      tensor must have a `stride` of 1 for its last dimension. The strides of all
      other dimensions must be even numbers.
  
  Args:
      {input}
  
  Example::
  
      >>> x=torch.randn(4, 2)
      >>> x
      tensor([[ 1.6116, -0.5772],
              [-1.4606, -0.9120],
              [ 0.0786, -1.7497],
              [-0.6561, -1.6623]])
      >>> torch.view_as_complex(x)
      tensor([(1.6116-0.5772j), (-1.4606-0.9120j), (0.0786-1.7497j), (-0.6561-1.6623j)])
  """.format(**common_args))
  
  add_docstr(torch.reciprocal,
             r"""
  reciprocal(input, *, out=None) -> Tensor
  
  Returns a new tensor with the reciprocal of the elements of :attr:`input`
  
  .. math::
      \text{out}_{i} = \frac{1}{\text{input}_{i}}
  
  .. note::
      Unlike NumPy's reciprocal, torch.reciprocal supports integral inputs. Integral
      inputs to reciprocal are automatically :ref:`promoted <type-promotion-doc>` to
      the default scalar type.
  """ + r"""
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([-0.4595, -2.1219, -1.4314,  0.7298])
      >>> torch.reciprocal(a)
      tensor([-2.1763, -0.4713, -0.6986,  1.3702])
  """.format(**common_args))
  
  add_docstr(torch.cholesky, r"""
  cholesky(input, upper=False, *, out=None) -> Tensor
  
  Computes the Cholesky decomposition of a symmetric positive-definite
  matrix :math:`A` or for batches of symmetric positive-definite matrices.
  
  If :attr:`upper` is ``True``, the returned matrix ``U`` is upper-triangular, and
  the decomposition has the form:
  
  .. math::
  
    A = U^TU
  
  If :attr:`upper` is ``False``, the returned matrix ``L`` is lower-triangular, and
  the decomposition has the form:
  
  .. math::
  
      A = LL^T
  
  If :attr:`upper` is ``True``, and :math:`A` is a batch of symmetric positive-definite
  matrices, then the returned tensor will be composed of upper-triangular Cholesky factors
  of each of the individual matrices. Similarly, when :attr:`upper` is ``False``, the returned
  tensor will be composed of lower-triangular Cholesky factors of each of the individual
  matrices.
  
  .. warning::
  
      :func:`torch.cholesky` is deprecated in favor of :func:`torch.linalg.cholesky`
      and will be removed in a future PyTorch release.
  
      ``L = torch.cholesky(A)`` should be replaced with
  
      .. code:: python
  
          L = torch.linalg.cholesky(A)
  
      ``U = torch.cholesky(A, upper=True)`` should be replaced with
  
      .. code:: python
  
          U = torch.linalg.cholesky(A).transpose(-2, -1).conj()
  
      This transform will produce equivalent results for all valid (symmetric positive definite) inputs.
  
  Args:
      input (Tensor): the input tensor :math:`A` of size :math:`(*, n, n)` where `*` is zero or more
                  batch dimensions consisting of symmetric positive-definite matrices.
      upper (bool, optional): flag that indicates whether to return a
                              upper or lower triangular matrix. Default: ``False``
  
  Keyword args:
      out (Tensor, optional): the output matrix
  
  Example::
  
      >>> a = torch.randn(3, 3)
      >>> a = torch.mm(a, a.t()) # make symmetric positive-definite
      >>> l = torch.cholesky(a)
      >>> a
      tensor([[ 2.4112, -0.7486,  1.4551],
              [-0.7486,  1.3544,  0.1294],
              [ 1.4551,  0.1294,  1.6724]])
      >>> l
      tensor([[ 1.5528,  0.0000,  0.0000],
              [-0.4821,  1.0592,  0.0000],
              [ 0.9371,  0.5487,  0.7023]])
      >>> torch.mm(l, l.t())
      tensor([[ 2.4112, -0.7486,  1.4551],
              [-0.7486,  1.3544,  0.1294],
              [ 1.4551,  0.1294,  1.6724]])
      >>> a = torch.randn(3, 2, 2)
      >>> a = torch.matmul(a, a.transpose(-1, -2)) + 1e-03 # make symmetric positive-definite
      >>> l = torch.cholesky(a)
      >>> z = torch.matmul(l, l.transpose(-1, -2))
      >>> torch.max(torch.abs(z - a)) # Max non-zero
      tensor(2.3842e-07)
  """)
  
  add_docstr(torch.cholesky_solve, r"""
  cholesky_solve(input, input2, upper=False, *, out=None) -> Tensor
  
  Solves a linear system of equations with a positive semidefinite
  matrix to be inverted given its Cholesky factor matrix :math:`u`.
  
  If :attr:`upper` is ``False``, :math:`u` is and lower triangular and `c` is
  returned such that:
  
  .. math::
      c = (u u^T)^{{-1}} b
  
  If :attr:`upper` is ``True`` or not provided, :math:`u` is upper triangular
  and `c` is returned such that:
  
  .. math::
      c = (u^T u)^{{-1}} b
  
  `torch.cholesky_solve(b, u)` can take in 2D inputs `b, u` or inputs that are
  batches of 2D matrices. If the inputs are batches, then returns
  batched outputs `c`
  
  Supports real-valued and complex-valued inputs.
  For the complex-valued inputs the transpose operator above is the conjugate transpose.
  
  Args:
      input (Tensor): input matrix :math:`b` of size :math:`(*, m, k)`,
                  where :math:`*` is zero or more batch dimensions
      input2 (Tensor): input matrix :math:`u` of size :math:`(*, m, m)`,
                  where :math:`*` is zero of more batch dimensions composed of
                  upper or lower triangular Cholesky factor
      upper (bool, optional): whether to consider the Cholesky factor as a
                              lower or upper triangular matrix. Default: ``False``.
  
  Keyword args:
      out (Tensor, optional): the output tensor for `c`
  
  Example::
  
      >>> a = torch.randn(3, 3)
      >>> a = torch.mm(a, a.t()) # make symmetric positive definite
      >>> u = torch.cholesky(a)
      >>> a
      tensor([[ 0.7747, -1.9549,  1.3086],
              [-1.9549,  6.7546, -5.4114],
              [ 1.3086, -5.4114,  4.8733]])
      >>> b = torch.randn(3, 2)
      >>> b
      tensor([[-0.6355,  0.9891],
              [ 0.1974,  1.4706],
              [-0.4115, -0.6225]])
      >>> torch.cholesky_solve(b, u)
      tensor([[ -8.1625,  19.6097],
              [ -5.8398,  14.2387],
              [ -4.3771,  10.4173]])
      >>> torch.mm(a.inverse(), b)
      tensor([[ -8.1626,  19.6097],
              [ -5.8398,  14.2387],
              [ -4.3771,  10.4173]])
  """)
  
  add_docstr(torch.cholesky_inverse, r"""
  cholesky_inverse(input, upper=False, *, out=None) -> Tensor
  
  Computes the inverse of a symmetric positive-definite matrix :math:`A` using its
  Cholesky factor :math:`u`: returns matrix ``inv``. The inverse is computed using
  LAPACK routines ``dpotri`` and ``spotri`` (and the corresponding MAGMA routines).
  
  If :attr:`upper` is ``False``, :math:`u` is lower triangular
  such that the returned tensor is
  
  .. math::
      inv = (uu^{{T}})^{{-1}}
  
  If :attr:`upper` is ``True`` or not provided, :math:`u` is upper
  triangular such that the returned tensor is
  
  .. math::
      inv = (u^T u)^{{-1}}
  
  Args:
      input (Tensor): the input 2-D tensor :math:`u`, a upper or lower triangular
             Cholesky factor
      upper (bool, optional): whether to return a lower (default) or upper triangular matrix
  
  Keyword args:
      out (Tensor, optional): the output tensor for `inv`
  
  Example::
  
      >>> a = torch.randn(3, 3)
      >>> a = torch.mm(a, a.t()) + 1e-05 * torch.eye(3) # make symmetric positive definite
      >>> u = torch.cholesky(a)
      >>> a
      tensor([[  0.9935,  -0.6353,   1.5806],
              [ -0.6353,   0.8769,  -1.7183],
              [  1.5806,  -1.7183,  10.6618]])
      >>> torch.cholesky_inverse(u)
      tensor([[ 1.9314,  1.2251, -0.0889],
              [ 1.2251,  2.4439,  0.2122],
              [-0.0889,  0.2122,  0.1412]])
      >>> a.inverse()
      tensor([[ 1.9314,  1.2251, -0.0889],
              [ 1.2251,  2.4439,  0.2122],
              [-0.0889,  0.2122,  0.1412]])
  """)
  
  add_docstr(torch.clone, r"""
  clone(input, *, memory_format=torch.preserve_format) -> Tensor
  
  Returns a copy of :attr:`input`.
  
  .. note::
  
      This function is differentiable, so gradients will flow back from the
      result of this operation to :attr:`input`. To create a tensor without an
      autograd relationship to :attr:`input` see :meth:`~Tensor.detach`.
  
  Args:
      {input}
  
  Keyword args:
      {memory_format}
  """.format(**common_args))
  
  add_docstr(torch.clamp, r"""
  clamp(input, min=None, max=None, *, out=None) -> Tensor
  
  Clamps all elements in :attr:`input` into the range `[` :attr:`min`, :attr:`max` `]`.
  Letting min_value and max_value be :attr:`min` and :attr:`max`, respectively, this returns:
  
  .. math::
      y_i = \min(\max(x_i, \text{min\_value}_i), \text{max\_value}_i)
  
  If :attr:`min` is ``None``, there is no lower bound.
  Or, if :attr:`max` is ``None`` there is no upper bound.
  """ + r"""
  
  .. note::
      If :attr:`min` is greater than :attr:`max` :func:`torch.clamp(..., min, max) <torch.clamp>`
      sets all elements in :attr:`input` to the value of :attr:`max`.
  
  Args:
      {input}
      min (Number or Tensor, optional): lower-bound of the range to be clamped to
      max (Number or Tensor, optional): upper-bound of the range to be clamped to
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([-1.7120,  0.1734, -0.0478, -0.0922])
      >>> torch.clamp(a, min=-0.5, max=0.5)
      tensor([-0.5000,  0.1734, -0.0478, -0.0922])
  
      >>> min = torch.linspace(-1, 1, steps=4)
      >>> torch.clamp(a, min=min)
      tensor([-1.0000,  0.1734,  0.3333,  1.0000])
  
  """.format(**common_args))
  
  add_docstr(torch.clip, r"""
  clip(input, min=None, max=None, *, out=None) -> Tensor
  
  Alias for :func:`torch.clamp`.
  """)
  
  add_docstr(torch.column_stack,
             r"""
  column_stack(tensors, *, out=None) -> Tensor
  
  Creates a new tensor by horizontally stacking the tensors in :attr:`tensors`.
  
  Equivalent to ``torch.hstack(tensors)``, except each zero or one dimensional tensor ``t``
  in :attr:`tensors` is first reshaped into a ``(t.numel(), 1)`` column before being stacked horizontally.
  
  Args:
      tensors (sequence of Tensors): sequence of tensors to concatenate
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.tensor([1, 2, 3])
      >>> b = torch.tensor([4, 5, 6])
      >>> torch.column_stack((a, b))
      tensor([[1, 4],
          [2, 5],
          [3, 6]])
      >>> a = torch.arange(5)
      >>> b = torch.arange(10).reshape(5, 2)
      >>> torch.column_stack((a, b, b))
      tensor([[0, 0, 1, 0, 1],
              [1, 2, 3, 2, 3],
              [2, 4, 5, 4, 5],
              [3, 6, 7, 6, 7],
              [4, 8, 9, 8, 9]])
  
  """.format(**common_args))
  
  add_docstr(torch.complex,
             r"""
  complex(real, imag, *, out=None) -> Tensor
  
  Constructs a complex tensor with its real part equal to :attr:`real` and its
  imaginary part equal to :attr:`imag`.
  
  Args:
      real (Tensor): The real part of the complex tensor. Must be float or double.
      imag (Tensor): The imaginary part of the complex tensor. Must be same dtype
          as :attr:`real`.
  
  Keyword args:
      out (Tensor): If the inputs are ``torch.float32``, must be
          ``torch.complex64``. If the inputs are ``torch.float64``, must be
          ``torch.complex128``.
  
  Example::
  
      >>> real = torch.tensor([1, 2], dtype=torch.float32)
      >>> imag = torch.tensor([3, 4], dtype=torch.float32)
      >>> z = torch.complex(real, imag)
      >>> z
      tensor([(1.+3.j), (2.+4.j)])
      >>> z.dtype
      torch.complex64
  
  """)
  
  add_docstr(torch.polar,
             r"""
  polar(abs, angle, *, out=None) -> Tensor
  
  Constructs a complex tensor whose elements are Cartesian coordinates
  corresponding to the polar coordinates with absolute value :attr:`abs` and angle
  :attr:`angle`.
  
  .. math::
      \text{out} = \text{abs} \cdot \cos(\text{angle}) + \text{abs} \cdot \sin(\text{angle}) \cdot j
  
  .. note::
      `torch.polar` is similar to
      `std::polar <https://en.cppreference.com/w/cpp/numeric/complex/polar>`_
      and does not compute the polar decomposition
      of a complex tensor like Python's `cmath.polar` and SciPy's `linalg.polar` do.
      The behavior of this function is undefined if `abs` is negative or NaN, or if `angle` is
      infinite.
  
  """ + r"""
  Args:
      abs (Tensor): The absolute value the complex tensor. Must be float or double.
      angle (Tensor): The angle of the complex tensor. Must be same dtype as
          :attr:`abs`.
  
  Keyword args:
      out (Tensor): If the inputs are ``torch.float32``, must be
          ``torch.complex64``. If the inputs are ``torch.float64``, must be
          ``torch.complex128``.
  
  Example::
  
      >>> import numpy as np
      >>> abs = torch.tensor([1, 2], dtype=torch.float64)
      >>> angle = torch.tensor([np.pi / 2, 5 * np.pi / 4], dtype=torch.float64)
      >>> z = torch.polar(abs, angle)
      >>> z
      tensor([(0.0000+1.0000j), (-1.4142-1.4142j)], dtype=torch.complex128)
  """)
  
  add_docstr(torch.conj_physical,
             r"""
  conj_physical(input, *, out=None) -> Tensor
  
  Computes the element-wise conjugate of the given :attr:`input` tensor.
  If :attr:`input` has a non-complex dtype, this function just returns :attr:`input`.
  
  .. note::
     This performs the conjugate operation regardless of the fact conjugate bit is set or not.
  
  .. warning:: In the future, :func:`torch.conj_physical` may return a non-writeable view for an :attr:`input` of
               non-complex dtype. It's recommended that programs not modify the tensor returned by :func:`torch.conj_physical`
               when :attr:`input` is of non-complex dtype to be compatible with this change.
  
  .. math::
      \text{out}_{i} = conj(\text{input}_{i})
  """ + r"""
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> torch.conj_physical(torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j]))
      tensor([-1 - 1j, -2 - 2j, 3 + 3j])
  """.format(**common_args))
  
  add_docstr(torch.conj,
             r"""
  conj(input) -> Tensor
  
  Returns a view of :attr:`input` with a flipped conjugate bit. If :attr:`input` has a non-complex dtype,
  this function just returns :attr:`input`.
  
  .. note::
      :func:`torch.conj` performs a lazy conjugation, but the actual conjugated tensor can be materialized
      at any time using :func:`torch.resolve_conj`.
  
  .. warning:: In the future, :func:`torch.conj` may return a non-writeable view for an :attr:`input` of
               non-complex dtype. It's recommended that programs not modify the tensor returned by :func:`torch.conj_physical`
               when :attr:`input` is of non-complex dtype to be compatible with this change.
  
  Args:
      {input}
  
  Example::
  
      >>> x = torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j])
      >>> x.is_conj()
      False
      >>> y = torch.conj(x)
      >>> y.is_conj()
      True
  """.format(**common_args))
  
  add_docstr(torch.resolve_conj,
             r"""
  resolve_conj(input) -> Tensor
  
  Returns a new tensor with materialized conjugation if :attr:`input`'s conjugate bit is set to `True`,
  else returns :attr:`input`. The output tensor will always have its conjugate bit set to `False`.
  
  Args:
      {input}
  
  Example::
  
      >>> x = torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j])
      >>> y = x.conj()
      >>> y.is_conj()
      True
      >>> z = y.resolve_conj()
      >>> z
      tensor([-1 - 1j, -2 - 2j, 3 + 3j])
      >>> z.is_conj()
      False
  """.format(**common_args))
  
  add_docstr(torch.resolve_neg,
             r"""
  resolve_neg(input) -> Tensor
  
  Returns a new tensor with materialized negation if :attr:`input`'s negative bit is set to `True`,
  else returns :attr:`input`. The output tensor will always have its negative bit set to `False`.
  Args:
      {input}
  
  Example::
  
      >>> x = torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j])
      >>> y = x.conj()
      >>> z = y.imag
      >>> z.is_neg()
      True
      >>> out = y.resolve_neg()
      >>> out
      tensor([-1, -2, -3])
      >>> out.is_neg()
      False
  
  """.format(**common_args))
  
  add_docstr(torch.copysign,
             r"""
  copysign(input, other, *, out=None) -> Tensor
  
  Create a new floating-point tensor with the magnitude of :attr:`input` and the sign of :attr:`other`, elementwise.
  
  .. math::
      \text{out}_{i} = \begin{cases}
          -|\text{input}_{i}| & \text{if} \text{other}_{i} \leq -0.0 \\
          |\text{input}_{i}| & \text{if} \text{other}_{i} \geq 0.0 \\
      \end{cases}
  """ + r"""
  
  Supports :ref:`broadcasting to a common shape <broadcasting-semantics>`,
  and integer and float inputs.
  
  Args:
      input (Tensor): magnitudes.
      other (Tensor or Number): contains value(s) whose signbit(s) are
          applied to the magnitudes in :attr:`input`.
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(5)
      >>> a
      tensor([-1.2557, -0.0026, -0.5387,  0.4740, -0.9244])
      >>> torch.copysign(a, 1)
      tensor([1.2557, 0.0026, 0.5387, 0.4740, 0.9244])
      >>> a = torch.randn(4, 4)
      >>> a
      tensor([[ 0.7079,  0.2778, -1.0249,  0.5719],
              [-0.0059, -0.2600, -0.4475, -1.3948],
              [ 0.3667, -0.9567, -2.5757, -0.1751],
              [ 0.2046, -0.0742,  0.2998, -0.1054]])
      >>> b = torch.randn(4)
      tensor([ 0.2373,  0.3120,  0.3190, -1.1128])
      >>> torch.copysign(a, b)
      tensor([[ 0.7079,  0.2778,  1.0249, -0.5719],
              [ 0.0059,  0.2600,  0.4475, -1.3948],
              [ 0.3667,  0.9567,  2.5757, -0.1751],
              [ 0.2046,  0.0742,  0.2998, -0.1054]])
  
  """.format(**common_args))
  
  add_docstr(torch.cos,
             r"""
  cos(input, *, out=None) -> Tensor
  
  Returns a new tensor with the cosine  of the elements of :attr:`input`.
  
  .. math::
      \text{out}_{i} = \cos(\text{input}_{i})
  """ + r"""
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([ 1.4309,  1.2706, -0.8562,  0.9796])
      >>> torch.cos(a)
      tensor([ 0.1395,  0.2957,  0.6553,  0.5574])
  """.format(**common_args))
  
  add_docstr(torch.cosh,
             r"""
  cosh(input, *, out=None) -> Tensor
  
  Returns a new tensor with the hyperbolic cosine  of the elements of
  :attr:`input`.
  
  .. math::
      \text{out}_{i} = \cosh(\text{input}_{i})
  """ + r"""
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([ 0.1632,  1.1835, -0.6979, -0.7325])
      >>> torch.cosh(a)
      tensor([ 1.0133,  1.7860,  1.2536,  1.2805])
  
  .. note::
     When :attr:`input` is on the CPU, the implementation of torch.cosh may use
     the Sleef library, which rounds very large results to infinity or negative
     infinity. See `here <https://sleef.org/purec.xhtml>`_ for details.
  """.format(**common_args))
  
  add_docstr(torch.cross,
             r"""
  cross(input, other, dim=None, *, out=None) -> Tensor
  
  
  Returns the cross product of vectors in dimension :attr:`dim` of :attr:`input`
  and :attr:`other`.
  
  :attr:`input` and :attr:`other` must have the same size, and the size of their
  :attr:`dim` dimension should be 3.
  
  If :attr:`dim` is not given, it defaults to the first dimension found with the
  size 3. Note that this might be unexpected.
  
  Args:
      {input}
      other (Tensor): the second input tensor
      dim  (int, optional): the dimension to take the cross-product in.
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4, 3)
      >>> a
      tensor([[-0.3956,  1.1455,  1.6895],
              [-0.5849,  1.3672,  0.3599],
              [-1.1626,  0.7180, -0.0521],
              [-0.1339,  0.9902, -2.0225]])
      >>> b = torch.randn(4, 3)
      >>> b
      tensor([[-0.0257, -1.4725, -1.2251],
              [-1.1479, -0.7005, -1.9757],
              [-1.3904,  0.3726, -1.1836],
              [-0.9688, -0.7153,  0.2159]])
      >>> torch.cross(a, b, dim=1)
      tensor([[ 1.0844, -0.5281,  0.6120],
              [-2.4490, -1.5687,  1.9792],
              [-0.8304, -1.3037,  0.5650],
              [-1.2329,  1.9883,  1.0551]])
      >>> torch.cross(a, b)
      tensor([[ 1.0844, -0.5281,  0.6120],
              [-2.4490, -1.5687,  1.9792],
              [-0.8304, -1.3037,  0.5650],
              [-1.2329,  1.9883,  1.0551]])
  """.format(**common_args))
  
  add_docstr(torch.logcumsumexp,
             r"""
  logcumsumexp(input, dim, *, out=None) -> Tensor
  Returns the logarithm of the cumulative summation of the exponentiation of
  elements of :attr:`input` in the dimension :attr:`dim`.
  
  For summation index :math:`j` given by `dim` and other indices :math:`i`, the result is
  
      .. math::
          \text{{logcumsumexp}}(x)_{{ij}} = \log \sum\limits_{{j=0}}^{{i}} \exp(x_{{ij}})
  
  Args:
      {input}
      dim  (int): the dimension to do the operation over
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(10)
      >>> torch.logcumsumexp(a, dim=0)
      tensor([-0.42296738, -0.04462666,  0.86278635,  0.94622083,  1.05277811,
               1.39202815,  1.83525007,  1.84492621,  2.06084887,  2.06844475]))
  """.format(**reduceops_common_args))
  
  add_docstr(torch.cummax,
             r"""
  cummax(input, dim, *, out=None) -> (Tensor, LongTensor)
  Returns a namedtuple ``(values, indices)`` where ``values`` is the cumulative maximum of
  elements of :attr:`input` in the dimension :attr:`dim`. And ``indices`` is the index
  location of each maximum value found in the dimension :attr:`dim`.
  
  .. math::
      y_i = max(x_1, x_2, x_3, \dots, x_i)
  
  Args:
      {input}
      dim  (int): the dimension to do the operation over
  
  Keyword args:
      out (tuple, optional): the result tuple of two output tensors (values, indices)
  
  Example::
  
      >>> a = torch.randn(10)
      >>> a
      tensor([-0.3449, -1.5447,  0.0685, -1.5104, -1.1706,  0.2259,  1.4696, -1.3284,
           1.9946, -0.8209])
      >>> torch.cummax(a, dim=0)
      torch.return_types.cummax(
          values=tensor([-0.3449, -0.3449,  0.0685,  0.0685,  0.0685,  0.2259,  1.4696,  1.4696,
           1.9946,  1.9946]),
          indices=tensor([0, 0, 2, 2, 2, 5, 6, 6, 8, 8]))
  """.format(**reduceops_common_args))
  
  add_docstr(torch.cummin,
             r"""
  cummin(input, dim, *, out=None) -> (Tensor, LongTensor)
  Returns a namedtuple ``(values, indices)`` where ``values`` is the cumulative minimum of
  elements of :attr:`input` in the dimension :attr:`dim`. And ``indices`` is the index
  location of each maximum value found in the dimension :attr:`dim`.
  
  .. math::
      y_i = min(x_1, x_2, x_3, \dots, x_i)
  
  Args:
      {input}
      dim  (int): the dimension to do the operation over
  
  Keyword args:
      out (tuple, optional): the result tuple of two output tensors (values, indices)
  
  Example::
  
      >>> a = torch.randn(10)
      >>> a
      tensor([-0.2284, -0.6628,  0.0975,  0.2680, -1.3298, -0.4220, -0.3885,  1.1762,
           0.9165,  1.6684])
      >>> torch.cummin(a, dim=0)
      torch.return_types.cummin(
          values=tensor([-0.2284, -0.6628, -0.6628, -0.6628, -1.3298, -1.3298, -1.3298, -1.3298,
          -1.3298, -1.3298]),
          indices=tensor([0, 1, 1, 1, 4, 4, 4, 4, 4, 4]))
  """.format(**reduceops_common_args))
  
  add_docstr(torch.cumprod,
             r"""
  cumprod(input, dim, *, dtype=None, out=None) -> Tensor
  
  Returns the cumulative product of elements of :attr:`input` in the dimension
  :attr:`dim`.
  
  For example, if :attr:`input` is a vector of size N, the result will also be
  a vector of size N, with elements.
  
  .. math::
      y_i = x_1 \times x_2\times x_3\times \dots \times x_i
  
  Args:
      {input}
      dim  (int): the dimension to do the operation over
  
  Keyword args:
      {dtype}
      {out}
  
  Example::
  
      >>> a = torch.randn(10)
      >>> a
      tensor([ 0.6001,  0.2069, -0.1919,  0.9792,  0.6727,  1.0062,  0.4126,
              -0.2129, -0.4206,  0.1968])
      >>> torch.cumprod(a, dim=0)
      tensor([ 0.6001,  0.1241, -0.0238, -0.0233, -0.0157, -0.0158, -0.0065,
               0.0014, -0.0006, -0.0001])
  
      >>> a[5] = 0.0
      >>> torch.cumprod(a, dim=0)
      tensor([ 0.6001,  0.1241, -0.0238, -0.0233, -0.0157, -0.0000, -0.0000,
               0.0000, -0.0000, -0.0000])
  """.format(**reduceops_common_args))
  
  add_docstr(torch.cumsum,
             r"""
  cumsum(input, dim, *, dtype=None, out=None) -> Tensor
  
  Returns the cumulative sum of elements of :attr:`input` in the dimension
  :attr:`dim`.
  
  For example, if :attr:`input` is a vector of size N, the result will also be
  a vector of size N, with elements.
  
  .. math::
      y_i = x_1 + x_2 + x_3 + \dots + x_i
  
  Args:
      {input}
      dim  (int): the dimension to do the operation over
  
  Keyword args:
      {dtype}
      {out}
  
  Example::
  
      >>> a = torch.randn(10)
      >>> a
      tensor([-0.8286, -0.4890,  0.5155,  0.8443,  0.1865, -0.1752, -2.0595,
               0.1850, -1.1571, -0.4243])
      >>> torch.cumsum(a, dim=0)
      tensor([-0.8286, -1.3175, -0.8020,  0.0423,  0.2289,  0.0537, -2.0058,
              -1.8209, -2.9780, -3.4022])
  """.format(**reduceops_common_args))
  
  add_docstr(torch.count_nonzero,
             r"""
  count_nonzero(input, dim=None) -> Tensor
  
  Counts the number of non-zero values in the tensor :attr:`input` along the given :attr:`dim`.
  If no dim is specified then all non-zeros in the tensor are counted.
  
  Args:
      {input}
      dim (int or tuple of ints, optional): Dim or tuple of dims along which to count non-zeros.
  
  Example::
  
      >>> x = torch.zeros(3,3)
      >>> x[torch.randn(3,3) > 0.5] = 1
      >>> x
      tensor([[0., 1., 1.],
              [0., 0., 0.],
              [0., 0., 1.]])
      >>> torch.count_nonzero(x)
      tensor(3)
      >>> torch.count_nonzero(x, dim=0)
      tensor([0, 1, 2])
  """.format(**reduceops_common_args))
  
  add_docstr(torch.dequantize,
             r"""
  dequantize(tensor) -> Tensor
  
  Returns an fp32 Tensor by dequantizing a quantized Tensor
  
  Args:
      tensor (Tensor): A quantized Tensor
  
  .. function:: dequantize(tensors) -> sequence of Tensors
     :noindex:
  
  Given a list of quantized Tensors, dequantize them and return a list of fp32 Tensors
  
  Args:
       tensors (sequence of Tensors): A list of quantized Tensors
  """)
  
  add_docstr(torch.diag,
             r"""
  diag(input, diagonal=0, *, out=None) -> Tensor
  
  - If :attr:`input` is a vector (1-D tensor), then returns a 2-D square tensor
    with the elements of :attr:`input` as the diagonal.
  - If :attr:`input` is a matrix (2-D tensor), then returns a 1-D tensor with
    the diagonal elements of :attr:`input`.
  
  The argument :attr:`diagonal` controls which diagonal to consider:
  
  - If :attr:`diagonal` = 0, it is the main diagonal.
  - If :attr:`diagonal` > 0, it is above the main diagonal.
  - If :attr:`diagonal` < 0, it is below the main diagonal.
  
  Args:
      {input}
      diagonal (int, optional): the diagonal to consider
  
  Keyword args:
      {out}
  
  .. seealso::
  
          :func:`torch.diagonal` always returns the diagonal of its input.
  
          :func:`torch.diagflat` always constructs a tensor with diagonal elements
          specified by the input.
  
  Examples:
  
  Get the square matrix where the input vector is the diagonal::
  
      >>> a = torch.randn(3)
      >>> a
      tensor([ 0.5950,-0.0872, 2.3298])
      >>> torch.diag(a)
      tensor([[ 0.5950, 0.0000, 0.0000],
              [ 0.0000,-0.0872, 0.0000],
              [ 0.0000, 0.0000, 2.3298]])
      >>> torch.diag(a, 1)
      tensor([[ 0.0000, 0.5950, 0.0000, 0.0000],
              [ 0.0000, 0.0000,-0.0872, 0.0000],
              [ 0.0000, 0.0000, 0.0000, 2.3298],
              [ 0.0000, 0.0000, 0.0000, 0.0000]])
  
  Get the k-th diagonal of a given matrix::
  
      >>> a = torch.randn(3, 3)
      >>> a
      tensor([[-0.4264, 0.0255,-0.1064],
              [ 0.8795,-0.2429, 0.1374],
              [ 0.1029,-0.6482,-1.6300]])
      >>> torch.diag(a, 0)
      tensor([-0.4264,-0.2429,-1.6300])
      >>> torch.diag(a, 1)
      tensor([ 0.0255, 0.1374])
  """.format(**common_args))
  
  add_docstr(torch.diag_embed,
             r"""
  diag_embed(input, offset=0, dim1=-2, dim2=-1) -> Tensor
  
  Creates a tensor whose diagonals of certain 2D planes (specified by
  :attr:`dim1` and :attr:`dim2`) are filled by :attr:`input`.
  To facilitate creating batched diagonal matrices, the 2D planes formed by
  the last two dimensions of the returned tensor are chosen by default.
  
  The argument :attr:`offset` controls which diagonal to consider:
  
  - If :attr:`offset` = 0, it is the main diagonal.
  - If :attr:`offset` > 0, it is above the main diagonal.
  - If :attr:`offset` < 0, it is below the main diagonal.
  
  The size of the new matrix will be calculated to make the specified diagonal
  of the size of the last input dimension.
  Note that for :attr:`offset` other than :math:`0`, the order of :attr:`dim1`
  and :attr:`dim2` matters. Exchanging them is equivalent to changing the
  sign of :attr:`offset`.
  
  Applying :meth:`torch.diagonal` to the output of this function with
  the same arguments yields a matrix identical to input. However,
  :meth:`torch.diagonal` has different default dimensions, so those
  need to be explicitly specified.
  
  Args:
      {input} Must be at least 1-dimensional.
      offset (int, optional): which diagonal to consider. Default: 0
          (main diagonal).
      dim1 (int, optional): first dimension with respect to which to
          take diagonal. Default: -2.
      dim2 (int, optional): second dimension with respect to which to
          take diagonal. Default: -1.
  
  Example::
  
      >>> a = torch.randn(2, 3)
      >>> torch.diag_embed(a)
      tensor([[[ 1.5410,  0.0000,  0.0000],
               [ 0.0000, -0.2934,  0.0000],
               [ 0.0000,  0.0000, -2.1788]],
  
              [[ 0.5684,  0.0000,  0.0000],
               [ 0.0000, -1.0845,  0.0000],
               [ 0.0000,  0.0000, -1.3986]]])
  
      >>> torch.diag_embed(a, offset=1, dim1=0, dim2=2)
      tensor([[[ 0.0000,  1.5410,  0.0000,  0.0000],
               [ 0.0000,  0.5684,  0.0000,  0.0000]],
  
              [[ 0.0000,  0.0000, -0.2934,  0.0000],
               [ 0.0000,  0.0000, -1.0845,  0.0000]],
  
              [[ 0.0000,  0.0000,  0.0000, -2.1788],
               [ 0.0000,  0.0000,  0.0000, -1.3986]],
  
              [[ 0.0000,  0.0000,  0.0000,  0.0000],
               [ 0.0000,  0.0000,  0.0000,  0.0000]]])
  """.format(**common_args))
  
  
  add_docstr(torch.diagflat,
             r"""
  diagflat(input, offset=0) -> Tensor
  
  - If :attr:`input` is a vector (1-D tensor), then returns a 2-D square tensor
    with the elements of :attr:`input` as the diagonal.
  - If :attr:`input` is a tensor with more than one dimension, then returns a
    2-D tensor with diagonal elements equal to a flattened :attr:`input`.
  
  The argument :attr:`offset` controls which diagonal to consider:
  
  - If :attr:`offset` = 0, it is the main diagonal.
  - If :attr:`offset` > 0, it is above the main diagonal.
  - If :attr:`offset` < 0, it is below the main diagonal.
  
  Args:
      {input}
      offset (int, optional): the diagonal to consider. Default: 0 (main
          diagonal).
  
  Examples::
  
      >>> a = torch.randn(3)
      >>> a
      tensor([-0.2956, -0.9068,  0.1695])
      >>> torch.diagflat(a)
      tensor([[-0.2956,  0.0000,  0.0000],
              [ 0.0000, -0.9068,  0.0000],
              [ 0.0000,  0.0000,  0.1695]])
      >>> torch.diagflat(a, 1)
      tensor([[ 0.0000, -0.2956,  0.0000,  0.0000],
              [ 0.0000,  0.0000, -0.9068,  0.0000],
              [ 0.0000,  0.0000,  0.0000,  0.1695],
              [ 0.0000,  0.0000,  0.0000,  0.0000]])
  
      >>> a = torch.randn(2, 2)
      >>> a
      tensor([[ 0.2094, -0.3018],
              [-0.1516,  1.9342]])
      >>> torch.diagflat(a)
      tensor([[ 0.2094,  0.0000,  0.0000,  0.0000],
              [ 0.0000, -0.3018,  0.0000,  0.0000],
              [ 0.0000,  0.0000, -0.1516,  0.0000],
              [ 0.0000,  0.0000,  0.0000,  1.9342]])
  """.format(**common_args))
  
  add_docstr(torch.diagonal,
             r"""
  diagonal(input, offset=0, dim1=0, dim2=1) -> Tensor
  
  Returns a partial view of :attr:`input` with the its diagonal elements
  with respect to :attr:`dim1` and :attr:`dim2` appended as a dimension
  at the end of the shape.
  
  The argument :attr:`offset` controls which diagonal to consider:
  
  - If :attr:`offset` = 0, it is the main diagonal.
  - If :attr:`offset` > 0, it is above the main diagonal.
  - If :attr:`offset` < 0, it is below the main diagonal.
  
  Applying :meth:`torch.diag_embed` to the output of this function with
  the same arguments yields a diagonal matrix with the diagonal entries
  of the input. However, :meth:`torch.diag_embed` has different default
  dimensions, so those need to be explicitly specified.
  
  Args:
      {input} Must be at least 2-dimensional.
      offset (int, optional): which diagonal to consider. Default: 0
          (main diagonal).
      dim1 (int, optional): first dimension with respect to which to
          take diagonal. Default: 0.
      dim2 (int, optional): second dimension with respect to which to
          take diagonal. Default: 1.
  
  .. note::  To take a batch diagonal, pass in dim1=-2, dim2=-1.
  
  Examples::
  
      >>> a = torch.randn(3, 3)
      >>> a
      tensor([[-1.0854,  1.1431, -0.1752],
              [ 0.8536, -0.0905,  0.0360],
              [ 0.6927, -0.3735, -0.4945]])
  
  
      >>> torch.diagonal(a, 0)
      tensor([-1.0854, -0.0905, -0.4945])
  
  
      >>> torch.diagonal(a, 1)
      tensor([ 1.1431,  0.0360])
  
  
      >>> x = torch.randn(2, 5, 4, 2)
      >>> torch.diagonal(x, offset=-1, dim1=1, dim2=2)
      tensor([[[-1.2631,  0.3755, -1.5977, -1.8172],
               [-1.1065,  1.0401, -0.2235, -0.7938]],
  
              [[-1.7325, -0.3081,  0.6166,  0.2335],
               [ 1.0500,  0.7336, -0.3836, -1.1015]]])
  """.format(**common_args))
  
  add_docstr(torch.diff, r"""
  diff(input, n=1, dim=-1, prepend=None, append=None) -> Tensor
  
  Computes the n-th forward difference along the given dimension.
  
  The first-order differences are given by `out[i] = input[i + 1] - input[i]`. Higher-order
  differences are calculated by using :func:`torch.diff` recursively.
  
  .. note::  Only `n = 1` is currently supported
  
  Args:
      input (Tensor): the tensor to compute the differences on
      n (int, optional): the number of times to recursively compute the difference
      dim (int, optional): the dimension to compute the difference along.
          Default is the last dimension.
      prepend, append (Tensor, optional): values to prepend or append to
          :attr:`input` along :attr:`dim` before computing the difference.
          Their dimensions must be equivalent to that of input, and their shapes
          must match input's shape except on :attr:`dim`.
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.tensor([1, 3, 2])
      >>> torch.diff(a)
      tensor([ 2, -1])
      >>> b = torch.tensor([4, 5])
      >>> torch.diff(a, append=b)
      tensor([ 2, -1,  2,  1])
      >>> c = torch.tensor([[1, 2, 3], [3, 4, 5]])
      >>> torch.diff(c, dim=0)
      tensor([[2, 2, 2]])
      >>> torch.diff(c, dim=1)
      tensor([[1, 1],
              [1, 1]])
  """.format(**common_args))
  
  add_docstr(torch.digamma, r"""
  digamma(input, *, out=None) -> Tensor
  
  Alias for :func:`torch.special.digamma`.
  """)
  
  add_docstr(torch.dist,
             r"""
  dist(input, other, p=2) -> Tensor
  
  Returns the p-norm of (:attr:`input` - :attr:`other`)
  
  The shapes of :attr:`input` and :attr:`other` must be
  :ref:`broadcastable <broadcasting-semantics>`.
  
  Args:
      {input}
      other (Tensor): the Right-hand-side input tensor
      p (float, optional): the norm to be computed
  
  Example::
  
      >>> x = torch.randn(4)
      >>> x
      tensor([-1.5393, -0.8675,  0.5916,  1.6321])
      >>> y = torch.randn(4)
      >>> y
      tensor([ 0.0967, -1.0511,  0.6295,  0.8360])
      >>> torch.dist(x, y, 3.5)
      tensor(1.6727)
      >>> torch.dist(x, y, 3)
      tensor(1.6973)
      >>> torch.dist(x, y, 0)
      tensor(inf)
      >>> torch.dist(x, y, 1)
      tensor(2.6537)
  """.format(**common_args))
  
  add_docstr(torch.div, r"""
  div(input, other, *, rounding_mode=None, out=None) -> Tensor
  
  Divides each element of the input ``input`` by the corresponding element of
  :attr:`other`.
  
  .. math::
      \text{{out}}_i = \frac{{\text{{input}}_i}}{{\text{{other}}_i}}
  
  .. note::
      By default, this performs a "true" division like Python 3.
      See the :attr:`rounding_mode` argument for floor division.
  
  Supports :ref:`broadcasting to a common shape <broadcasting-semantics>`,
  :ref:`type promotion <type-promotion-doc>`, and integer, float, and complex inputs.
  Always promotes integer types to the default scalar type.
  
  Args:
      input (Tensor): the dividend
      other (Tensor or Number): the divisor
  
  Keyword args:
      rounding_mode (str, optional): Type of rounding applied to the result:
  
          * None - default behavior. Performs no rounding and, if both :attr:`input` and
            :attr:`other` are integer types, promotes the inputs to the default scalar type.
            Equivalent to true division in Python (the ``/`` operator) and NumPy's ``np.true_divide``.
          * ``"trunc"`` - rounds the results of the division towards zero.
            Equivalent to C-style integer division.
          * ``"floor"`` - rounds the results of the division down.
            Equivalent to floor division in Python (the ``//`` operator) and NumPy's ``np.floor_divide``.
  
      {out}
  
  Examples::
  
      >>> x = torch.tensor([ 0.3810,  1.2774, -0.2972, -0.3719,  0.4637])
      >>> torch.div(x, 0.5)
      tensor([ 0.7620,  2.5548, -0.5944, -0.7438,  0.9274])
  
      >>> a = torch.tensor([[-0.3711, -1.9353, -0.4605, -0.2917],
      ...                   [ 0.1815, -1.0111,  0.9805, -1.5923],
      ...                   [ 0.1062,  1.4581,  0.7759, -1.2344],
      ...                   [-0.1830, -0.0313,  1.1908, -1.4757]])
      >>> b = torch.tensor([ 0.8032,  0.2930, -0.8113, -0.2308])
      >>> torch.div(a, b)
      tensor([[-0.4620, -6.6051,  0.5676,  1.2639],
              [ 0.2260, -3.4509, -1.2086,  6.8990],
              [ 0.1322,  4.9764, -0.9564,  5.3484],
              [-0.2278, -0.1068, -1.4678,  6.3938]])
  
      >>> torch.div(a, b, rounding_mode='trunc')
      tensor([[-0., -6.,  0.,  1.],
              [ 0., -3., -1.,  6.],
              [ 0.,  4., -0.,  5.],
              [-0., -0., -1.,  6.]])
  
      >>> torch.div(a, b, rounding_mode='floor')
      tensor([[-1., -7.,  0.,  1.],
              [ 0., -4., -2.,  6.],
              [ 0.,  4., -1.,  5.],
              [-1., -1., -2.,  6.]])
  
  """.format(**common_args))
  
  add_docstr(torch.divide, r"""
  divide(input, other, *, rounding_mode=None, out=None) -> Tensor
  
  Alias for :func:`torch.div`.
  """)
  
  add_docstr(torch.dot,
             r"""
  dot(input, other, *, out=None) -> Tensor
  
  Computes the dot product of two 1D tensors.
  
  .. note::
  
      Unlike NumPy's dot, torch.dot intentionally only supports computing the dot product
      of two 1D tensors with the same number of elements.
  
  Args:
      input (Tensor): first tensor in the dot product, must be 1D.
      other (Tensor): second tensor in the dot product, must be 1D.
  
  Keyword args:
      {out}
  
  Example::
  
      >>> torch.dot(torch.tensor([2, 3]), torch.tensor([2, 1]))
      tensor(7)
  """.format(**common_args))
  
  add_docstr(torch.vdot,
             r"""
  vdot(input, other, *, out=None) -> Tensor
  
  Computes the dot product of two 1D tensors. The vdot(a, b) function handles complex numbers
  differently than dot(a, b). If the first argument is complex, the complex conjugate of the
  first argument is used for the calculation of the dot product.
  
  .. note::
  
      Unlike NumPy's vdot, torch.vdot intentionally only supports computing the dot product
      of two 1D tensors with the same number of elements.
  
  Args:
      input (Tensor): first tensor in the dot product, must be 1D. Its conjugate is used if it's complex.
      other (Tensor): second tensor in the dot product, must be 1D.
  
  Keyword args:
      {out}
  
  Example::
  
      >>> torch.vdot(torch.tensor([2, 3]), torch.tensor([2, 1]))
      tensor(7)
      >>> a = torch.tensor((1 +2j, 3 - 1j))
      >>> b = torch.tensor((2 +1j, 4 - 0j))
      >>> torch.vdot(a, b)
      tensor([16.+1.j])
      >>> torch.vdot(b, a)
      tensor([16.-1.j])
  """.format(**common_args))
  
  add_docstr(torch.eig,
             r"""
  eig(input, eigenvectors=False, *, out=None) -> (Tensor, Tensor)
  
  Computes the eigenvalues and eigenvectors of a real square matrix.
  
  .. note::
      Since eigenvalues and eigenvectors might be complex, backward pass is supported only
      if eigenvalues and eigenvectors are all real valued.
  
      When :attr:`input` is on CUDA, :func:`torch.eig() <torch.eig>` causes
      host-device synchronization.
  
  .. warning::
  
      :func:`torch.eig` is deprecated in favor of :func:`torch.linalg.eig`
      and will be removed in a future PyTorch release.
      :func:`torch.linalg.eig` returns complex tensors of dtype `cfloat` or `cdouble`
      rather than real tensors mimicking complex tensors.
  
      ``L, _ = torch.eig(A)`` should be replaced with
  
      .. code :: python
  
          L_complex = torch.linalg.eigvals(A)
  
      ``L, V = torch.eig(A, eigenvectors=True)`` should be replaced with
  
      .. code :: python
  
          L_complex, V_complex = torch.linalg.eig(A)
  
  Args:
      input (Tensor): the square matrix of shape :math:`(n \times n)` for which the eigenvalues and eigenvectors
          will be computed
      eigenvectors (bool): ``True`` to compute both eigenvalues and eigenvectors;
          otherwise, only eigenvalues will be computed
  
  Keyword args:
      out (tuple, optional): the output tensors
  
  Returns:
      (Tensor, Tensor): A namedtuple (eigenvalues, eigenvectors) containing
  
          - **eigenvalues** (*Tensor*): Shape :math:`(n \times 2)`. Each row is an eigenvalue of ``input``,
            where the first element is the real part and the second element is the imaginary part.
            The eigenvalues are not necessarily ordered.
          - **eigenvectors** (*Tensor*): If ``eigenvectors=False``, it's an empty tensor.
            Otherwise, this tensor of shape :math:`(n \times n)` can be used to compute normalized (unit length)
            eigenvectors of corresponding eigenvalues as follows.
            If the corresponding `eigenvalues[j]` is a real number, column `eigenvectors[:, j]` is the eigenvector
            corresponding to `eigenvalues[j]`.
            If the corresponding `eigenvalues[j]` and `eigenvalues[j + 1]` form a complex conjugate pair, then the
            true eigenvectors can be computed as
            :math:`\text{true eigenvector}[j] = eigenvectors[:, j] + i \times eigenvectors[:, j + 1]`,
            :math:`\text{true eigenvector}[j + 1] = eigenvectors[:, j] - i \times eigenvectors[:, j + 1]`.
  
  Example::
  
      Trivial example with a diagonal matrix. By default, only eigenvalues are computed:
  
      >>> a = torch.diag(torch.tensor([1, 2, 3], dtype=torch.double))
      >>> e, v = torch.eig(a)
      >>> e
      tensor([[1., 0.],
              [2., 0.],
              [3., 0.]], dtype=torch.float64)
      >>> v
      tensor([], dtype=torch.float64)
  
      Compute also the eigenvectors:
  
      >>> e, v = torch.eig(a, eigenvectors=True)
      >>> e
      tensor([[1., 0.],
              [2., 0.],
              [3., 0.]], dtype=torch.float64)
      >>> v
      tensor([[1., 0., 0.],
              [0., 1., 0.],
              [0., 0., 1.]], dtype=torch.float64)
  
  """)
  
  add_docstr(torch.eq, r"""
  eq(input, other, *, out=None) -> Tensor
  
  Computes element-wise equality
  
  The second argument can be a number or a tensor whose shape is
  :ref:`broadcastable <broadcasting-semantics>` with the first argument.
  
  Args:
      input (Tensor): the tensor to compare
      other (Tensor or float): the tensor or value to compare
  
  Keyword args:
      {out}
  
  Returns:
      A boolean tensor that is True where :attr:`input` is equal to :attr:`other` and False elsewhere
  
  Example::
  
      >>> torch.eq(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]]))
      tensor([[ True, False],
              [False, True]])
  """.format(**common_args))
  
  add_docstr(torch.equal,
             r"""
  equal(input, other) -> bool
  
  ``True`` if two tensors have the same size and elements, ``False`` otherwise.
  
  Example::
  
      >>> torch.equal(torch.tensor([1, 2]), torch.tensor([1, 2]))
      True
  """)
  
  add_docstr(torch.erf,
             r"""
  erf(input, *, out=None) -> Tensor
  
  Alias for :func:`torch.special.erf`.
  """)
  
  add_docstr(torch.erfc,
             r"""
  erfc(input, *, out=None) -> Tensor
  
  Alias for :func:`torch.special.erfc`.
  """)
  
  add_docstr(torch.erfinv,
             r"""
  erfinv(input, *, out=None) -> Tensor
  
  Alias for :func:`torch.special.erfinv`.
  """)
  
  add_docstr(torch.exp,
             r"""
  exp(input, *, out=None) -> Tensor
  
  Returns a new tensor with the exponential of the elements
  of the input tensor :attr:`input`.
  
  .. math::
      y_{i} = e^{x_{i}}
  """ + r"""
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> torch.exp(torch.tensor([0, math.log(2.)]))
      tensor([ 1.,  2.])
  """.format(**common_args))
  
  add_docstr(torch.exp2,
             r"""
  exp2(input, *, out=None) -> Tensor
  
  Alias for :func:`torch.special.exp2`.
  """)
  
  add_docstr(torch.expm1,
             r"""
  expm1(input, *, out=None) -> Tensor
  
  Alias for :func:`torch.special.expm1`.
  """)
  
  add_docstr(torch.eye,
             r"""
  eye(n, m=None, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
  
  Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.
  
  Args:
      n (int): the number of rows
      m (int, optional): the number of columns with default being :attr:`n`
  
  Keyword arguments:
      {out}
      {dtype}
      {layout}
      {device}
      {requires_grad}
  
  Returns:
      Tensor: A 2-D tensor with ones on the diagonal and zeros elsewhere
  
  Example::
  
      >>> torch.eye(3)
      tensor([[ 1.,  0.,  0.],
              [ 0.,  1.,  0.],
              [ 0.,  0.,  1.]])
  """.format(**factory_common_args))
  
  add_docstr(torch.floor,
             r"""
  floor(input, *, out=None) -> Tensor
  
  Returns a new tensor with the floor of the elements of :attr:`input`,
  the largest integer less than or equal to each element.
  
  .. math::
      \text{out}_{i} = \left\lfloor \text{input}_{i} \right\rfloor
  """ + r"""
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([-0.8166,  1.5308, -0.2530, -0.2091])
      >>> torch.floor(a)
      tensor([-1.,  1., -1., -1.])
  """.format(**common_args))
  
  add_docstr(torch.floor_divide, r"""
  floor_divide(input, other, *, out=None) -> Tensor
  
  .. warning::
  
      :func:`torch.floor_divide` is deprecated and will be removed in a future PyTorch
      release. Its name is a misnomer because it actually rounds the quotient
      towards zero instead of taking its floor. To keep the current behavior use
      :func:`torch.div` with ``rounding_mode='trunc'``. To actually perform floor
      division, use :func:`torch.div` with ``rounding_mode='floor'``.
  
  Computes :attr:`input` divided by :attr:`other`, elementwise, and rounds each
  quotient towards zero. Equivalently, it truncates the quotient(s):
  
  .. math::
      \text{{out}}_i = \text{trunc} \left( \frac{{\text{{input}}_i}}{{\text{{other}}_i}} \right)
  
  """ + r"""
  
  Supports broadcasting to a common shape, type promotion, and integer and float inputs.
  
  Args:
      input (Tensor or Number): the dividend
      other (Tensor or Number): the divisor
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.tensor([4.0, 3.0])
      >>> b = torch.tensor([2.0, 2.0])
      >>> torch.floor_divide(a, b)
      tensor([2.0, 1.0])
      >>> torch.floor_divide(a, 1.4)
      tensor([2.0, 2.0])
  """.format(**common_args))
  
  add_docstr(torch.fmod,
             r"""
  fmod(input, other, *, out=None) -> Tensor
  
  Applies C++'s `std::fmod <https://en.cppreference.com/w/cpp/numeric/math/fmod>`_
  for floating point tensors, and the modulus operation for integer tensors. The result
  has the same sign as the dividend :attr:`input` and its absolute value
  is less than that of :attr:`other`.
  
  Supports :ref:`broadcasting to a common shape <broadcasting-semantics>`,
  :ref:`type promotion <type-promotion-doc>`, and integer and float inputs.
  
  .. note::
  
      When the divisor is zero, returns ``NaN`` for floating point dtypes
      on both CPU and GPU; raises ``RuntimeError`` for integer division by
      zero on CPU; Integer division by zero on GPU may return any value.
  
  .. note::
  
     Complex inputs are not supported. In some cases, it is not mathematically
     possible to satisfy the definition of a modulo operation with complex numbers.
  
  Args:
      input (Tensor): the dividend
      other (Tensor or Scalar): the divisor
  
  Keyword args:
      {out}
  
  Example::
  
      >>> torch.fmod(torch.tensor([-3., -2, -1, 1, 2, 3]), 2)
      tensor([-1., -0., -1.,  1.,  0.,  1.])
      >>> torch.fmod(torch.tensor([1, 2, 3, 4, 5]), -1.5)
      tensor([1.0000, 0.5000, 0.0000, 1.0000, 0.5000])
  
  .. seealso::
  
      :func:`torch.remainder` which is similar to :func:`torch.fmod` except that if the sign
      of the modulus is different than the sign of the divisor :attr:`other` then the divisor
      is added to the modulus.
  """.format(**common_args))
  
  add_docstr(torch.frac,
             r"""
  frac(input, *, out=None) -> Tensor
  
  Computes the fractional portion of each element in :attr:`input`.
  
  .. math::
      \text{out}_{i} = \text{input}_{i} - \left\lfloor |\text{input}_{i}| \right\rfloor * \operatorname{sgn}(\text{input}_{i})
  
  Example::
  
      >>> torch.frac(torch.tensor([1, 2.5, -3.2]))
      tensor([ 0.0000,  0.5000, -0.2000])
  """)
  
  add_docstr(torch.frexp,
             r"""
  frexp(input, *, out=None) -> (Tensor mantissa, Tensor exponent)
  
  Decomposes :attr:`input` into mantissa and exponent tensors
  such that :math:`\text{input} = \text{mantissa} \times 2^{\text{exponent}}`.
  
  The range of mantissa is the open interval (-1, 1).
  
  Supports float inputs.
  
  Args:
      input (Tensor): the input tensor
  
  
  Keyword args:
      out (tuple, optional): the output tensors
  
  Example::
  
      >>> x = torch.arange(9.)
      >>> mantissa, exponent = torch.frexp(x)
      >>> mantissa
      tensor([0.0000, 0.5000, 0.5000, 0.7500, 0.5000, 0.6250, 0.7500, 0.8750, 0.5000])
      >>> exponent
      tensor([0, 1, 2, 2, 3, 3, 3, 3, 4], dtype=torch.int32)
      >>> torch.ldexp(mantissa, exponent)
      tensor([0., 1., 2., 3., 4., 5., 6., 7., 8.])
  """)
  
  add_docstr(torch.from_numpy,
             r"""
  from_numpy(ndarray) -> Tensor
  
  Creates a :class:`Tensor` from a :class:`numpy.ndarray`.
  
  The returned tensor and :attr:`ndarray` share the same memory. Modifications to
  the tensor will be reflected in the :attr:`ndarray` and vice versa. The returned
  tensor is not resizable.
  
  It currently accepts :attr:`ndarray` with dtypes of ``numpy.float64``,
  ``numpy.float32``, ``numpy.float16``, ``numpy.complex64``, ``numpy.complex128``,
  ``numpy.int64``, ``numpy.int32``, ``numpy.int16``, ``numpy.int8``, ``numpy.uint8``,
  and ``numpy.bool``.
  
  Example::
  
      >>> a = numpy.array([1, 2, 3])
      >>> t = torch.from_numpy(a)
      >>> t
      tensor([ 1,  2,  3])
      >>> t[0] = -1
      >>> a
      array([-1,  2,  3])
  """)
  
  add_docstr(torch.frombuffer,
             r"""
  frombuffer(buffer, *, dtype, count=-1, offset=0, requires_grad=False) -> Tensor
  
  Creates a 1-dimensional :class:`Tensor` from an object that implements
  the Python buffer protocol.
  
  Skips the first :attr:`offset` bytes in the buffer, and interprets the rest of
  the raw bytes as a 1-dimensional tensor of type :attr:`dtype` with :attr:`count`
  elements.
  
  Note that either of the following must be true:
  
  1. :attr:`count` is a positive non-zero number, and the total number of bytes
  in the buffer is less than :attr:`offset` plus :attr:`count` times the size
  (in bytes) of :attr:`dtype`.
  
  2. :attr:`count` is negative, and the length (number of bytes) of the buffer
  subtracted by the :attr:`offset` is a multiple of the size (in bytes) of
  :attr:`dtype`.
  
  The returned tensor and buffer share the same memory. Modifications to
  the tensor will be reflected in the buffer and vice versa. The returned
  tensor is not resizable.
  
  .. note::
      This function increments the reference count for the object that
      owns the shared memory. Therefore, such memory will not be deallocated
      before the returned tensor goes out of scope.
  
  .. warning::
      This function's behavior is undefined when passed an object implementing
      the buffer protocol whose data is not on the CPU. Doing so is likely to
      cause a segmentation fault.
  
  .. warning::
      This function does not try to infer the :attr:`dtype` (hence, it is not
      optional). Passing a different :attr:`dtype` than its source may result
      in unexpected behavior.
  
  Args:
      buffer (object): a Python object that exposes the buffer interface.
  
  Keyword args:
      dtype (:class:`torch.dtype`): the desired data type of returned tensor.
      count (int, optional): the number of desired elements to be read.
          If negative, all the elements (until the end of the buffer) will be
          read. Default: -1.
      offset (int, optional): the number of bytes to skip at the start of
          the buffer. Default: 0.
      {requires_grad}
  
  Example::
  
      >>> import array
      >>> a = array.array('i', [1, 2, 3])
      >>> t = torch.frombuffer(a, dtype=torch.int32)
      >>> t
      tensor([ 1,  2,  3])
      >>> t[0] = -1
      >>> a
      array([-1,  2,  3])
  
      >>> # Interprets the signed char bytes as 32-bit integers.
      >>> # Each 4 signed char elements will be interpreted as
      >>> # 1 signed 32-bit integer.
      >>> import array
      >>> a = array.array('b', [-1, 0, 0, 0])
      >>> torch.frombuffer(a, dtype=torch.int32)
      tensor([255], dtype=torch.int32)
  """.format(**factory_common_args))
  
  add_docstr(torch.flatten,
             r"""
  flatten(input, start_dim=0, end_dim=-1) -> Tensor
  
  Flattens :attr:`input` by reshaping it into a one-dimensional tensor. If :attr:`start_dim` or :attr:`end_dim`
  are passed, only dimensions starting with :attr:`start_dim` and ending with :attr:`end_dim` are flattened.
  The order of elements in :attr:`input` is unchanged.
  
  Unlike NumPy's flatten, which always copies input's data, this function may return the original object, a view,
  or copy. If no dimensions are flattened, then the original object :attr:`input` is returned. Otherwise, if input can
  be viewed as the flattened shape, then that view is returned. Finally, only if the input cannot be viewed as the
  flattened shape is input's data copied. See :meth:`torch.Tensor.view` for details on when a view will be returned.
  
  .. note::
      Flattening a zero-dimensional tensor will return a one-dimensional view.
  
  Args:
      {input}
      start_dim (int): the first dim to flatten
      end_dim (int): the last dim to flatten
  
  Example::
  
      >>> t = torch.tensor([[[1, 2],
      ...                    [3, 4]],
      ...                   [[5, 6],
      ...                    [7, 8]]])
      >>> torch.flatten(t)
      tensor([1, 2, 3, 4, 5, 6, 7, 8])
      >>> torch.flatten(t, start_dim=1)
      tensor([[1, 2, 3, 4],
              [5, 6, 7, 8]])
  """.format(**common_args))
  
  add_docstr(torch.gather,
             r"""
  gather(input, dim, index, *, sparse_grad=False, out=None) -> Tensor
  
  Gathers values along an axis specified by `dim`.
  
  For a 3-D tensor the output is specified by::
  
      out[i][j][k] = input[index[i][j][k]][j][k]  # if dim == 0
      out[i][j][k] = input[i][index[i][j][k]][k]  # if dim == 1
      out[i][j][k] = input[i][j][index[i][j][k]]  # if dim == 2
  
  :attr:`input` and :attr:`index` must have the same number of dimensions.
  It is also required that ``index.size(d) <= input.size(d)`` for all
  dimensions ``d != dim``.  :attr:`out` will have the same shape as :attr:`index`.
  Note that ``input`` and ``index`` do not broadcast against each other.
  
  Args:
      input (Tensor): the source tensor
      dim (int): the axis along which to index
      index (LongTensor): the indices of elements to gather
  
  Keyword arguments:
      sparse_grad (bool, optional): If ``True``, gradient w.r.t. :attr:`input` will be a sparse tensor.
      out (Tensor, optional): the destination tensor
  
  Example::
  
      >>> t = torch.tensor([[1, 2], [3, 4]])
      >>> torch.gather(t, 1, torch.tensor([[0, 0], [1, 0]]))
      tensor([[ 1,  1],
              [ 4,  3]])
  """)
  
  
  add_docstr(torch.gcd,
             r"""
  gcd(input, other, *, out=None) -> Tensor
  
  Computes the element-wise greatest common divisor (GCD) of :attr:`input` and :attr:`other`.
  
  Both :attr:`input` and :attr:`other` must have integer types.
  
  .. note::
      This defines :math:`gcd(0, 0) = 0`.
  
  Args:
      {input}
      other (Tensor): the second input tensor
  
  Keyword arguments:
      {out}
  
  Example::
  
      >>> a = torch.tensor([5, 10, 15])
      >>> b = torch.tensor([3, 4, 5])
      >>> torch.gcd(a, b)
      tensor([1, 2, 5])
      >>> c = torch.tensor([3])
      >>> torch.gcd(a, c)
      tensor([1, 1, 3])
  """.format(**common_args))
  
  add_docstr(torch.ge, r"""
  ge(input, other, *, out=None) -> Tensor
  
  Computes :math:`\text{input} \geq \text{other}` element-wise.
  """ + r"""
  
  The second argument can be a number or a tensor whose shape is
  :ref:`broadcastable <broadcasting-semantics>` with the first argument.
  
  Args:
      input (Tensor): the tensor to compare
      other (Tensor or float): the tensor or value to compare
  
  Keyword args:
      {out}
  
  Returns:
      A boolean tensor that is True where :attr:`input` is greater than or equal to :attr:`other` and False elsewhere
  
  Example::
  
      >>> torch.ge(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]]))
      tensor([[True, True], [False, True]])
  """.format(**common_args))
  
  add_docstr(torch.greater_equal, r"""
  greater_equal(input, other, *, out=None) -> Tensor
  
  Alias for :func:`torch.ge`.
  """)
  
  add_docstr(torch.gradient,
             r"""
  gradient(input, *, spacing=1, dim=None, edge_order=1) -> List of Tensors
  
  Estimates the gradient of a function :math:`g : \mathbb{R}^n \rightarrow \mathbb{R}` in
  one or more dimensions using the `second-order accurate central differences method
  <https://www.ams.org/journals/mcom/1988-51-184/S0025-5718-1988-0935077-0/S0025-5718-1988-0935077-0.pdf>`_.
  
  The gradient of :math:`g` is estimated using samples. By default, when :attr:`spacing` is not
  specified, the samples are entirely described by :attr:`input`, and the mapping of input coordinates
  to an output is the same as the tensor's mapping of indices to values. For example, for a three-dimensional
  :attr:`input` the function described is :math:`g : \mathbb{R}^3 \rightarrow \mathbb{R}`, and
  :math:`g(1, 2, 3)\ == input[1, 2, 3]`.
  
  When :attr:`spacing` is specified, it modifies the relationship between :attr:`input` and input coordinates.
  This is detailed in the "Keyword Arguments" section below.
  
  The gradient is estimated by estimating each partial derivative of :math:`g` independently. This estimation is
  accurate if :math:`g` is in :math:`C^3` (it has at least 3 continuous derivatives), and the estimation can be
  improved by providing closer samples. Mathematically, the value at each interior point of a partial derivative
  is estimated using `Taylor’s theorem with remainder <https://en.wikipedia.org/wiki/Taylor%27s_theorem>`_.
  Letting :math:`x` be an interior point and  :math:`x+h_r` be point neighboring it, the partial gradient at
  :math:`f(x+h_r)` is estimated using:
  
  .. math::
      \begin{aligned}
          f(x+h_r) = f(x) + h_r f'(x) + {h_r}^2  \frac{f''(x)}{2} + {h_r}^3 \frac{f'''(x_r)}{6} \\
      \end{aligned}
  
  where :math:`x_r` is a number in the interval :math:`[x, x+ h_r]`  and using the fact that :math:`f \in C^3`
  we derive :
  
  .. math::
      f'(x) \approx \frac{ {h_l}^2 f(x+h_r) - {h_r}^2 f(x-h_l)
            + ({h_r}^2-{h_l}^2 ) f(x) }{ {h_r} {h_l}^2 + {h_r}^2 {h_l} }
  
  .. note::
      We estimate the gradient of functions in complex domain
      :math:`g : \mathbb{C}^n \rightarrow \mathbb{C}` in the same way.
  
  The value of each partial derivative at the boundary points is computed differently. See edge_order below.
  
  Args:
      input (``Tensor``): the tensor that represents the values of the function
  
  Keyword args:
      spacing (``scalar``, ``list of scalar``, ``list of Tensor``, optional): :attr:`spacing` can be used to modify
          how the :attr:`input` tensor's indices relate to sample coordinates. If :attr:`spacing` is a scalar then
          the indices are multiplied by the scalar to produce the coordinates. For example, if :attr:`spacing=2` the
          indices (1, 2, 3) become coordinates (2, 4, 6). If :attr:`spacing` is a list of scalars then the corresponding
          indices are multiplied. For example, if :attr:`spacing=(2, -1, 3)` the indices (1, 2, 3) become coordinates (2, -2, 9).
          Finally, if :attr:`spacing` is a list of one-dimensional tensors then each tensor specifies the coordinates for
          the corresponding dimension. For example, if the indices are (1, 2, 3) and the tensors are (t0, t1, t2), then
          the coordinates are (t0[1], t1[2], t2[3])
  
      dim (``int``, ``list of int``, optional): the dimension or dimensions to approximate the gradient over.  By default
          the partial  gradient in every dimension is computed. Note that when :attr:`dim` is  specified the elements of
          the :attr:`spacing` argument must correspond with the specified dims."
  
      edge_order (``int``, optional): 1 or 2, for `first-order
          <https://www.ams.org/journals/mcom/1988-51-184/S0025-5718-1988-0935077-0/S0025-5718-1988-0935077-0.pdf>`_ or
          `second-order <https://www.ams.org/journals/mcom/1988-51-184/S0025-5718-1988-0935077-0/S0025-5718-1988-0935077-0.pdf>`_
          estimation of the boundary ("edge") values, respectively.
  
  Examples::
  
      >>> # Estimates the gradient of f(x)=x^2 at points [-2, -1, 2, 4]
      >>> coordinates = (torch.tensor([-2., -1., 1., 4.]),)
      >>> values = torch.tensor([4., 1., 1., 16.], )
      >>> torch.gradient(values, spacing = coordinates)
      (tensor([-3., -2., 2., 5.]),)
  
      >>> # Estimates the gradient of the R^2 -> R function whose samples are
      >>> # described by the tensor t. Implicit coordinates are [0, 1] for the outermost
      >>> # dimension and [0, 1, 2, 3] for the innermost dimension, and function estimates
      >>> # partial derivative for both dimensions.
      >>> t = torch.tensor([[1, 2, 4, 8], [10, 20, 40, 80]])
      >>> torch.gradient(t)
      (tensor([[ 9., 18., 36., 72.],
               [ 9., 18., 36., 72.]]),
       tensor([[ 1.0000, 1.5000, 3.0000, 4.0000],
               [10.0000, 15.0000, 30.0000, 40.0000]]))
  
      >>> # A scalar value for spacing modifies the relationship between tensor indices
      >>> # and input coordinates by multiplying the indices to find the
      >>> # coordinates. For example, below the indices of the innermost
      >>> # 0, 1, 2, 3 translate to coordinates of [0, 2, 4, 6], and the indices of
      >>> # the outermost dimension 0, 1 translate to coordinates of [0, 2].
      >>> torch.gradient(t, spacing = 2.0) # dim = None (implicitly [0, 1])
      (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000],
                [ 4.5000, 9.0000, 18.0000, 36.0000]]),
       tensor([[ 0.5000, 0.7500, 1.5000, 2.0000],
                [ 5.0000, 7.5000, 15.0000, 20.0000]]))
      >>> # doubling the spacing between samples halves the estimated partial gradients.
  
      >>>
      >>> # Estimates only the partial derivative for dimension 1
      >>> torch.gradient(t, dim = 1) # spacing = None (implicitly 1.)
      (tensor([[ 1.0000, 1.5000, 3.0000, 4.0000],
               [10.0000, 15.0000, 30.0000, 40.0000]]),)
  
      >>> # When spacing is a list of scalars, the relationship between the tensor
      >>> # indices and input coordinates changes based on dimension.
      >>> # For example, below, the indices of the innermost dimension 0, 1, 2, 3 translate
      >>> # to coordinates of [0, 3, 6, 9], and the indices of the outermost dimension
      >>> # 0, 1 translate to coordinates of [0, 2].
      >>> torch.gradient(t, spacing = [3., 2.])
      (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000],
               [ 4.5000, 9.0000, 18.0000, 36.0000]]),
       tensor([[ 0.3333, 0.5000, 1.0000, 1.3333],
               [ 3.3333, 5.0000, 10.0000, 13.3333]]))
  
      >>> # The following example is a replication of the previous one with explicit
      >>> # coordinates.
      >>> coords = (torch.tensor([0, 2]), torch.tensor([0, 3, 6, 9]))
      >>> torch.gradient(t, spacing = coords)
      (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000],
               [ 4.5000, 9.0000, 18.0000, 36.0000]]),
       tensor([[ 0.3333, 0.5000, 1.0000, 1.3333],
               [ 3.3333, 5.0000, 10.0000, 13.3333]]))
  
  """)
  
  add_docstr(torch.geqrf,
             r"""
  geqrf(input, *, out=None) -> (Tensor, Tensor)
  
  This is a low-level function for calling LAPACK's geqrf directly. This function
  returns a namedtuple (a, tau) as defined in `LAPACK documentation for geqrf`_ .
  
  Computes a QR decomposition of :attr:`input`.
  Both `Q` and `R` matrices are stored in the same output tensor `a`.
  The elements of `R` are stored on and above the diagonal.
  Elementary reflectors (or Householder vectors) implicitly defining matrix `Q`
  are stored below the diagonal.
  The results of this function can be used together with :func:`torch.linalg.householder_product`
  to obtain the `Q` matrix or
  with :func:`torch.ormqr`, which uses an implicit representation of the `Q` matrix,
  for an efficient matrix-matrix multiplication.
  
  See `LAPACK documentation for geqrf`_ for further details.
  
  .. note::
      See also :func:`torch.linalg.qr`, which computes Q and R matrices, and :func:`torch.linalg.lstsq`
      with the ``driver="gels"`` option for a function that can solve matrix equations using a QR decomposition.
  
  Args:
      input (Tensor): the input matrix
  
  Keyword args:
      out (tuple, optional): the output tuple of (Tensor, Tensor). Ignored if `None`. Default: `None`.
  
  .. _LAPACK documentation for geqrf:
      http://www.netlib.org/lapack/explore-html/df/dc5/group__variants_g_ecomputational_ga3766ea903391b5cf9008132f7440ec7b.html
  
  """)
  
  add_docstr(torch.inner, r"""
  inner(input, other, *, out=None) -> Tensor
  
  Computes the dot product for 1D tensors. For higher dimensions, sums the product
  of elements from :attr:`input` and :attr:`other` along their last dimension.
  
  .. note::
  
      If either :attr:`input` or :attr:`other` is a scalar, the result is equivalent
      to `torch.mul(input, other)`.
  
      If both :attr:`input` and :attr:`other` are non-scalars, the size of their last
      dimension must match and the result is equivalent to `torch.tensordot(input,
      other, dims=([-1], [-1]))`
  
  Args:
      input (Tensor): First input tensor
      other (Tensor): Second input tensor
  
  Keyword args:
      out (Tensor, optional): Optional output tensor to write result into. The output
                              shape is `input.shape[:-1] + other.shape[:-1]`.
  
  Example::
  
      # Dot product
      >>> torch.inner(torch.tensor([1, 2, 3]), torch.tensor([0, 2, 1]))
      tensor(7)
  
      # Multidimensional input tensors
      >>> a = torch.randn(2, 3)
      >>> a
      tensor([[0.8173, 1.0874, 1.1784],
              [0.3279, 0.1234, 2.7894]])
      >>> b = torch.randn(2, 4, 3)
      >>> b
      tensor([[[-0.4682, -0.7159,  0.1506],
              [ 0.4034, -0.3657,  1.0387],
              [ 0.9892, -0.6684,  0.1774],
              [ 0.9482,  1.3261,  0.3917]],
  
              [[ 0.4537,  0.7493,  1.1724],
              [ 0.2291,  0.5749, -0.2267],
              [-0.7920,  0.3607, -0.3701],
              [ 1.3666, -0.5850, -1.7242]]])
      >>> torch.inner(a, b)
      tensor([[[-0.9837,  1.1560,  0.2907,  2.6785],
              [ 2.5671,  0.5452, -0.6912, -1.5509]],
  
              [[ 0.1782,  2.9843,  0.7366,  1.5672],
              [ 3.5115, -0.4864, -1.2476, -4.4337]]])
  
      # Scalar input
      >>> torch.inner(a, torch.tensor(2))
      tensor([[1.6347, 2.1748, 2.3567],
              [0.6558, 0.2469, 5.5787]])
  """)
  
  add_docstr(torch.outer, r"""
  outer(input, vec2, *, out=None) -> Tensor
  
  Outer product of :attr:`input` and :attr:`vec2`.
  If :attr:`input` is a vector of size :math:`n` and :attr:`vec2` is a vector of
  size :math:`m`, then :attr:`out` must be a matrix of size :math:`(n \times m)`.
  
  .. note:: This function does not :ref:`broadcast <broadcasting-semantics>`.
  
  Args:
      input (Tensor): 1-D input vector
      vec2 (Tensor): 1-D input vector
  
  Keyword args:
      out (Tensor, optional): optional output matrix
  
  Example::
  
      >>> v1 = torch.arange(1., 5.)
      >>> v2 = torch.arange(1., 4.)
      >>> torch.outer(v1, v2)
      tensor([[  1.,   2.,   3.],
              [  2.,   4.,   6.],
              [  3.,   6.,   9.],
              [  4.,   8.,  12.]])
  """)
  
  add_docstr(torch.ger,
             r"""
  ger(input, vec2, *, out=None) -> Tensor
  
  Alias of :func:`torch.outer`.
  
  .. warning::
      This function is deprecated and will be removed in a future PyTorch release.
      Use :func:`torch.outer` instead.
  """)
  
  add_docstr(torch.solve,
             r"""
  torch.solve(input, A, *, out=None) -> (Tensor, Tensor)
  
  This function returns the solution to the system of linear
  equations represented by :math:`AX = B` and the LU factorization of
  A, in order as a namedtuple `solution, LU`.
  
  `LU` contains `L` and `U` factors for LU factorization of `A`.
  
  `torch.solve(B, A)` can take in 2D inputs `B, A` or inputs that are
  batches of 2D matrices. If the inputs are batches, then returns
  batched outputs `solution, LU`.
  
  Supports real-valued and complex-valued inputs.
  
  .. warning::
  
      :func:`torch.solve` is deprecated in favor of :func:`torch.linalg.solve`
      and will be removed in a future PyTorch release.
      :func:`torch.linalg.solve` has its arguments reversed and does not return the
      LU factorization of the input. To get the LU factorization see :func:`torch.lu`,
      which may be used with :func:`torch.lu_solve` and :func:`torch.lu_unpack`.
  
      ``X = torch.solve(B, A).solution`` should be replaced with
  
      .. code:: python
  
          X = torch.linalg.solve(A, B)
  
  .. note::
  
      Irrespective of the original strides, the returned matrices
      `solution` and `LU` will be transposed, i.e. with strides like
      `B.contiguous().transpose(-1, -2).stride()` and
      `A.contiguous().transpose(-1, -2).stride()` respectively.
  
  Args:
      input (Tensor): input matrix :math:`B` of size :math:`(*, m, k)` , where :math:`*`
                  is zero or more batch dimensions.
      A (Tensor): input square matrix of size :math:`(*, m, m)`, where
                  :math:`*` is zero or more batch dimensions.
  
  Keyword args:
      out ((Tensor, Tensor), optional): optional output tuple.
  
  Example::
  
      >>> A = torch.tensor([[6.80, -2.11,  5.66,  5.97,  8.23],
      ...                   [-6.05, -3.30,  5.36, -4.44,  1.08],
      ...                   [-0.45,  2.58, -2.70,  0.27,  9.04],
      ...                   [8.32,  2.71,  4.35,  -7.17,  2.14],
      ...                   [-9.67, -5.14, -7.26,  6.08, -6.87]]).t()
      >>> B = torch.tensor([[4.02,  6.19, -8.22, -7.57, -3.03],
      ...                   [-1.56,  4.00, -8.67,  1.75,  2.86],
      ...                   [9.81, -4.09, -4.57, -8.61,  8.99]]).t()
      >>> X, LU = torch.solve(B, A)
      >>> torch.dist(B, torch.mm(A, X))
      tensor(1.00000e-06 *
             7.0977)
  
      >>> # Batched solver example
      >>> A = torch.randn(2, 3, 1, 4, 4)
      >>> B = torch.randn(2, 3, 1, 4, 6)
      >>> X, LU = torch.solve(B, A)
      >>> torch.dist(B, A.matmul(X))
      tensor(1.00000e-06 *
         3.6386)
  
  """)
  
  add_docstr(torch.get_default_dtype,
             r"""
  get_default_dtype() -> torch.dtype
  
  Get the current default floating point :class:`torch.dtype`.
  
  Example::
  
      >>> torch.get_default_dtype()  # initial default for floating point is torch.float32
      torch.float32
      >>> torch.set_default_dtype(torch.float64)
      >>> torch.get_default_dtype()  # default is now changed to torch.float64
      torch.float64
      >>> torch.set_default_tensor_type(torch.FloatTensor)  # setting tensor type also affects this
      >>> torch.get_default_dtype()  # changed to torch.float32, the dtype for torch.FloatTensor
      torch.float32
  
  """)
  
  add_docstr(torch.get_num_threads,
             r"""
  get_num_threads() -> int
  
  Returns the number of threads used for parallelizing CPU operations
  """)
  
  add_docstr(torch.get_num_interop_threads,
             r"""
  get_num_interop_threads() -> int
  
  Returns the number of threads used for inter-op parallelism on CPU
  (e.g. in JIT interpreter)
  """)
  
  add_docstr(torch.gt, r"""
  gt(input, other, *, out=None) -> Tensor
  
  Computes :math:`\text{input} > \text{other}` element-wise.
  """ + r"""
  
  The second argument can be a number or a tensor whose shape is
  :ref:`broadcastable <broadcasting-semantics>` with the first argument.
  
  Args:
      input (Tensor): the tensor to compare
      other (Tensor or float): the tensor or value to compare
  
  Keyword args:
      {out}
  
  Returns:
      A boolean tensor that is True where :attr:`input` is greater than :attr:`other` and False elsewhere
  
  Example::
  
      >>> torch.gt(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]]))
      tensor([[False, True], [False, False]])
  """.format(**common_args))
  
  add_docstr(torch.greater, r"""
  greater(input, other, *, out=None) -> Tensor
  
  Alias for :func:`torch.gt`.
  """)
  
  add_docstr(torch.histc,
             r"""
  histc(input, bins=100, min=0, max=0, *, out=None) -> Tensor
  
  Computes the histogram of a tensor.
  
  The elements are sorted into equal width bins between :attr:`min` and
  :attr:`max`. If :attr:`min` and :attr:`max` are both zero, the minimum and
  maximum values of the data are used.
  
  Elements lower than min and higher than max are ignored.
  
  Args:
      {input}
      bins (int): number of histogram bins
      min (int): lower end of the range (inclusive)
      max (int): upper end of the range (inclusive)
  
  Keyword args:
      {out}
  
  Returns:
      Tensor: Histogram represented as a tensor
  
  Example::
  
      >>> torch.histc(torch.tensor([1., 2, 1]), bins=4, min=0, max=3)
      tensor([ 0.,  2.,  1.,  0.])
  """.format(**common_args))
  
  add_docstr(torch.histogram,
             r"""
  histogram(input, bins, *, range=None, weight=None, density=False, out=None) -> (Tensor, Tensor)
  
  Computes a histogram of the values in a tensor.
  
  :attr:`bins` can be an integer or a 1D tensor.
  
  If :attr:`bins` is an int, it specifies the number of equal-width bins.
  By default, the lower and upper range of the bins is determined by the
  minimum and maximum elements of the input tensor. The :attr:`range`
  argument can be provided to specify a range for the bins.
  
  If :attr:`bins` is a 1D tensor, it specifies the sequence of bin edges
  including the rightmost edge. It should contain at least 2 elements
  and its elements should be increasing.
  
  Args:
      {input}
      bins: int or 1D Tensor. If int, defines the number of equal-width bins. If tensor,
            defines the sequence of bin edges including the rightmost edge.
  
  Keyword args:
      range (tuple of float): Defines the range of the bins.
      weight (Tensor): If provided, weight should have the same shape as input. Each value in
                       input contributes its associated weight towards its bin's result.
      density (bool): If False, the result will contain the count (or total weight) in each bin.
                      If True, the result is the value of the probability density function over the bins,
                      normalized such that the integral over the range of the bins is 1.
      {out} (tuple, optional): The result tuple of two output tensors (hist, bin_edges).
  
  Returns:
      hist (Tensor): 1D Tensor containing the values of the histogram.
      bin_edges(Tensor): 1D Tensor containing the edges of the histogram bins.
  
  Example::
  
      >>> torch.histogram(torch.tensor([1., 2, 1]), bins=4, range=(0., 3.), weight=torch.tensor([1., 2., 4.]))
      (tensor([ 0.,  5.,  2.,  0.]), tensor([0., 0.75, 1.5, 2.25, 3.]))
      >>> torch.histogram(torch.tensor([1., 2, 1]), bins=4, range=(0., 3.), weight=torch.tensor([1., 2., 4.]), density=True)
      (tensor([ 0.,  0.9524,  0.3810,  0.]), tensor([0., 0.75, 1.5, 2.25, 3.]))
  """.format(**common_args))
  
  add_docstr(torch.hypot,
             r"""
  hypot(input, other, *, out=None) -> Tensor
  
  Given the legs of a right triangle, return its hypotenuse.
  
  .. math::
      \text{out}_{i} = \sqrt{\text{input}_{i}^{2} + \text{other}_{i}^{2}}
  
  The shapes of ``input`` and ``other`` must be
  :ref:`broadcastable <broadcasting-semantics>`.
  """ + r"""
  Args:
      input (Tensor): the first input tensor
      other (Tensor): the second input tensor
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.hypot(torch.tensor([4.0]), torch.tensor([3.0, 4.0, 5.0]))
      tensor([5.0000, 5.6569, 6.4031])
  
  """.format(**common_args))
  
  add_docstr(torch.i0,
             r"""
  i0(input, *, out=None) -> Tensor
  
  Alias for :func:`torch.special.i0`.
  """)
  
  add_docstr(torch.igamma,
             r"""
  igamma(input, other, *, out=None) -> Tensor
  
  Alias for :func:`torch.special.gammainc`.
  """)
  
  add_docstr(torch.igammac,
             r"""
  igammac(input, other, *, out=None) -> Tensor
  
  Alias for :func:`torch.special.gammaincc`.
  """)
  
  add_docstr(torch.index_select,
             r"""
  index_select(input, dim, index, *, out=None) -> Tensor
  
  Returns a new tensor which indexes the :attr:`input` tensor along dimension
  :attr:`dim` using the entries in :attr:`index` which is a `LongTensor`.
  
  The returned tensor has the same number of dimensions as the original tensor
  (:attr:`input`).  The :attr:`dim`\ th dimension has the same size as the length
  of :attr:`index`; other dimensions have the same size as in the original tensor.
  
  .. note:: The returned tensor does **not** use the same storage as the original
            tensor.  If :attr:`out` has a different shape than expected, we
            silently change it to the correct shape, reallocating the underlying
            storage if necessary.
  
  Args:
      {input}
      dim (int): the dimension in which we index
      index (IntTensor or LongTensor): the 1-D tensor containing the indices to index
  
  Keyword args:
      {out}
  
  Example::
  
      >>> x = torch.randn(3, 4)
      >>> x
      tensor([[ 0.1427,  0.0231, -0.5414, -1.0009],
              [-0.4664,  0.2647, -0.1228, -1.1068],
              [-1.1734, -0.6571,  0.7230, -0.6004]])
      >>> indices = torch.tensor([0, 2])
      >>> torch.index_select(x, 0, indices)
      tensor([[ 0.1427,  0.0231, -0.5414, -1.0009],
              [-1.1734, -0.6571,  0.7230, -0.6004]])
      >>> torch.index_select(x, 1, indices)
      tensor([[ 0.1427, -0.5414],
              [-0.4664, -0.1228],
              [-1.1734,  0.7230]])
  """.format(**common_args))
  
  add_docstr(torch.inverse, r"""
  inverse(input, *, out=None) -> Tensor
  
  Alias for :func:`torch.linalg.inv`
  """)
  
  add_docstr(torch.isin, r"""
  isin(elements, test_elements, *, assume_unique=False, invert=False) -> Tensor
  
  Tests if each element of :attr:`elements` is in :attr:`test_elements`. Returns
  a boolean tensor of the same shape as :attr:`elements` that is True for elements
  in :attr:`test_elements` and False otherwise.
  
  .. note::
      One of :attr:`elements` or :attr:`test_elements` can be a scalar, but not both.
  
  Args:
      elements (Tensor or Scalar): Input elements
      test_elements (Tensor or Scalar): Values against which to test for each input element
      assume_unique (bool, optional): If True, assumes both :attr:`elements` and
          :attr:`test_elements` contain unique elements, which can speed up the
          calculation. Default: False
      invert (bool, optional): If True, inverts the boolean return tensor, resulting in True
          values for elements *not* in :attr:`test_elements`. Default: False
  
  Returns:
      A boolean tensor of the same shape as :attr:`elements` that is True for elements in
      :attr:`test_elements` and False otherwise
  
  Example:
      >>> torch.isin(torch.tensor([[1, 2], [3, 4]]), torch.tensor([2, 3]))
      tensor([[False,  True],
              [ True, False]])
  """)
  
  add_docstr(torch.isinf, r"""
  isinf(input) -> Tensor
  
  Tests if each element of :attr:`input` is infinite
  (positive or negative infinity) or not.
  
  .. note::
      Complex values are infinite when their real or imaginary part is
      infinite.
  
  Args:
      {input}
  
  Returns:
      A boolean tensor that is True where :attr:`input` is infinite and False elsewhere
  
  Example::
  
      >>> torch.isinf(torch.tensor([1, float('inf'), 2, float('-inf'), float('nan')]))
      tensor([False,  True,  False,  True,  False])
  """.format(**common_args))
  
  add_docstr(torch.isposinf,
             r"""
  isposinf(input, *, out=None) -> Tensor
  Tests if each element of :attr:`input` is positive infinity or not.
  
  Args:
    {input}
  
  Keyword args:
    {out}
  
  Example::
  
      >>> a = torch.tensor([-float('inf'), float('inf'), 1.2])
      >>> torch.isposinf(a)
      tensor([False,  True, False])
  """.format(**common_args))
  
  add_docstr(torch.isneginf,
             r"""
  isneginf(input, *, out=None) -> Tensor
  Tests if each element of :attr:`input` is negative infinity or not.
  
  Args:
    {input}
  
  Keyword args:
    {out}
  
  Example::
  
      >>> a = torch.tensor([-float('inf'), float('inf'), 1.2])
      >>> torch.isneginf(a)
      tensor([ True, False, False])
  """.format(**common_args))
  
  add_docstr(torch.isclose, r"""
  isclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) -> Tensor
  
  Returns a new tensor with boolean elements representing if each element of
  :attr:`input` is "close" to the corresponding element of :attr:`other`.
  Closeness is defined as:
  
  .. math::
      \lvert \text{input} - \text{other} \rvert \leq \texttt{atol} + \texttt{rtol} \times \lvert \text{other} \rvert
  """ + r"""
  
  where :attr:`input` and :attr:`other` are finite. Where :attr:`input`
  and/or :attr:`other` are nonfinite they are close if and only if
  they are equal, with NaNs being considered equal to each other when
  :attr:`equal_nan` is True.
  
  Args:
      input (Tensor): first tensor to compare
      other (Tensor): second tensor to compare
      atol (float, optional): absolute tolerance. Default: 1e-08
      rtol (float, optional): relative tolerance. Default: 1e-05
      equal_nan (bool, optional): if ``True``, then two ``NaN`` s will be considered equal. Default: ``False``
  
  Examples::
  
      >>> torch.isclose(torch.tensor((1., 2, 3)), torch.tensor((1 + 1e-10, 3, 4)))
      tensor([ True, False, False])
      >>> torch.isclose(torch.tensor((float('inf'), 4)), torch.tensor((float('inf'), 6)), rtol=.5)
      tensor([True, True])
  """)
  
  add_docstr(torch.isfinite, r"""
  isfinite(input) -> Tensor
  
  Returns a new tensor with boolean elements representing if each element is `finite` or not.
  
  Real values are finite when they are not NaN, negative infinity, or infinity.
  Complex values are finite when both their real and imaginary parts are finite.
  
  Args:
      {input}
  
  Returns:
      A boolean tensor that is True where :attr:`input` is finite and False elsewhere
  
  Example::
  
      >>> torch.isfinite(torch.tensor([1, float('inf'), 2, float('-inf'), float('nan')]))
      tensor([True,  False,  True,  False,  False])
  """.format(**common_args))
  
  add_docstr(torch.isnan, r"""
  isnan(input) -> Tensor
  
  Returns a new tensor with boolean elements representing if each element of :attr:`input`
  is NaN or not. Complex values are considered NaN when either their real
  and/or imaginary part is NaN.
  
  Arguments:
      {input}
  
  Returns:
      A boolean tensor that is True where :attr:`input` is NaN and False elsewhere
  
  Example::
  
      >>> torch.isnan(torch.tensor([1, float('nan'), 2]))
      tensor([False, True, False])
  """.format(**common_args))
  
  add_docstr(torch.isreal, r"""
  isreal(input) -> Tensor
  
  Returns a new tensor with boolean elements representing if each element of :attr:`input` is real-valued or not.
  All real-valued types are considered real. Complex values are considered real when their imaginary part is 0.
  
  Arguments:
      {input}
  
  Returns:
      A boolean tensor that is True where :attr:`input` is real and False elsewhere
  
  Example::
  
      >>> torch.isreal(torch.tensor([1, 1+1j, 2+0j]))
      tensor([True, False, True])
  """.format(**common_args))
  
  add_docstr(torch.is_floating_point, r"""
  is_floating_point(input) -> (bool)
  
  Returns True if the data type of :attr:`input` is a floating point data type i.e.,
  one of ``torch.float64``, ``torch.float32``, ``torch.float16``, and ``torch.bfloat16``.
  
  Args:
      {input}
  """.format(**common_args))
  
  add_docstr(torch.is_complex, r"""
  is_complex(input) -> (bool)
  
  Returns True if the data type of :attr:`input` is a complex data type i.e.,
  one of ``torch.complex64``, and ``torch.complex128``.
  
  Args:
      {input}
  """.format(**common_args))
  
  add_docstr(torch.is_grad_enabled, r"""
  is_grad_enabled() -> (bool)
  
  Returns True if grad mode is currently enabled.
  """.format(**common_args))
  
  add_docstr(torch.is_inference_mode_enabled, r"""
  is_inference_mode_enabled() -> (bool)
  
  Returns True if inference mode is currently enabled.
  """.format(**common_args))
  
  add_docstr(torch.is_inference, r"""
  is_inference(input) -> (bool)
  
  Returns True if :attr:`input` is an inference tensor.
  
  A non-view tensor is an inference tensor if and only if it was
  allocated during inference mode. A view tensor is an inference
  tensor if and only if the tensor it is a view of is an inference tensor.
  
  For details on inference mode please see
  `Inference Mode <https://pytorch.org/cppdocs/notes/inference_mode.html>`_.
  
  Args:
      {input}
  """.format(**common_args))
  
  add_docstr(torch.is_conj, r"""
  is_conj(input) -> (bool)
  
  Returns True if the :attr:`input` is a conjugated tensor, i.e. its conjugate bit is set to `True`.
  
  Args:
      {input}
  """.format(**common_args))
  
  add_docstr(torch.is_nonzero, r"""
  is_nonzero(input) -> (bool)
  
  Returns True if the :attr:`input` is a single element tensor which is not equal to zero
  after type conversions.
  i.e. not equal to ``torch.tensor([0.])`` or ``torch.tensor([0])`` or
  ``torch.tensor([False])``.
  Throws a ``RuntimeError`` if ``torch.numel() != 1`` (even in case
  of sparse tensors).
  
  Args:
      {input}
  
  Examples::
  
      >>> torch.is_nonzero(torch.tensor([0.]))
      False
      >>> torch.is_nonzero(torch.tensor([1.5]))
      True
      >>> torch.is_nonzero(torch.tensor([False]))
      False
      >>> torch.is_nonzero(torch.tensor([3]))
      True
      >>> torch.is_nonzero(torch.tensor([1, 3, 5]))
      Traceback (most recent call last):
      ...
      RuntimeError: bool value of Tensor with more than one value is ambiguous
      >>> torch.is_nonzero(torch.tensor([]))
      Traceback (most recent call last):
      ...
      RuntimeError: bool value of Tensor with no values is ambiguous
  """.format(**common_args))
  
  add_docstr(torch.kron,
             r"""
  kron(input, other, *, out=None) -> Tensor
  
  Computes the Kronecker product, denoted by :math:`\otimes`, of :attr:`input` and :attr:`other`.
  
  If :attr:`input` is a :math:`(a_0 \times a_1 \times \dots \times a_n)` tensor and :attr:`other` is a
  :math:`(b_0 \times b_1 \times \dots \times b_n)` tensor, the result will be a
  :math:`(a_0*b_0 \times a_1*b_1 \times \dots \times a_n*b_n)` tensor with the following entries:
  
  .. math::
      (\text{input} \otimes \text{other})_{k_0, k_1, \dots, k_n} =
          \text{input}_{i_0, i_1, \dots, i_n} * \text{other}_{j_0, j_1, \dots, j_n},
  
  where :math:`k_t = i_t * b_t + j_t` for :math:`0 \leq t \leq n`.
  If one tensor has fewer dimensions than the other it is unsqueezed until it has the same number of dimensions.
  
  Supports real-valued and complex-valued inputs.
  
  .. note::
      This function generalizes the typical definition of the Kronecker product for two matrices to two tensors,
      as described above. When :attr:`input` is a :math:`(m \times n)` matrix and :attr:`other` is a
      :math:`(p \times q)` matrix, the result will be a :math:`(p*m \times q*n)` block matrix:
  
      .. math::
          \mathbf{A} \otimes \mathbf{B}=\begin{bmatrix}
          a_{11} \mathbf{B} & \cdots & a_{1 n} \mathbf{B} \\
          \vdots & \ddots & \vdots \\
          a_{m 1} \mathbf{B} & \cdots & a_{m n} \mathbf{B} \end{bmatrix}
  
      where :attr:`input` is :math:`\mathbf{A}` and :attr:`other` is :math:`\mathbf{B}`.
  
  Arguments:
      input (Tensor)
      other (Tensor)
  
  Keyword args:
      out (Tensor, optional): The output tensor. Ignored if ``None``. Default: ``None``
  
  Examples::
  
      >>> mat1 = torch.eye(2)
      >>> mat2 = torch.ones(2, 2)
      >>> torch.kron(mat1, mat2)
      tensor([[1., 1., 0., 0.],
              [1., 1., 0., 0.],
              [0., 0., 1., 1.],
              [0., 0., 1., 1.]])
  
      >>> mat1 = torch.eye(2)
      >>> mat2 = torch.arange(1, 5).reshape(2, 2)
      >>> torch.kron(mat1, mat2)
      tensor([[1., 2., 0., 0.],
              [3., 4., 0., 0.],
              [0., 0., 1., 2.],
              [0., 0., 3., 4.]])
  """)
  
  add_docstr(torch.kthvalue,
             r"""
  kthvalue(input, k, dim=None, keepdim=False, *, out=None) -> (Tensor, LongTensor)
  
  Returns a namedtuple ``(values, indices)`` where ``values`` is the :attr:`k` th
  smallest element of each row of the :attr:`input` tensor in the given dimension
  :attr:`dim`. And ``indices`` is the index location of each element found.
  
  If :attr:`dim` is not given, the last dimension of the `input` is chosen.
  
  If :attr:`keepdim` is ``True``, both the :attr:`values` and :attr:`indices` tensors
  are the same size as :attr:`input`, except in the dimension :attr:`dim` where
  they are of size 1. Otherwise, :attr:`dim` is squeezed
  (see :func:`torch.squeeze`), resulting in both the :attr:`values` and
  :attr:`indices` tensors having 1 fewer dimension than the :attr:`input` tensor.
  
  .. note::
      When :attr:`input` is a CUDA tensor and there are multiple valid
      :attr:`k` th values, this function may nondeterministically return
      :attr:`indices` for any of them.
  
  Args:
      {input}
      k (int): k for the k-th smallest element
      dim (int, optional): the dimension to find the kth value along
      {keepdim}
  
  Keyword args:
      out (tuple, optional): the output tuple of (Tensor, LongTensor)
                             can be optionally given to be used as output buffers
  
  Example::
  
      >>> x = torch.arange(1., 6.)
      >>> x
      tensor([ 1.,  2.,  3.,  4.,  5.])
      >>> torch.kthvalue(x, 4)
      torch.return_types.kthvalue(values=tensor(4.), indices=tensor(3))
  
      >>> x=torch.arange(1.,7.).resize_(2,3)
      >>> x
      tensor([[ 1.,  2.,  3.],
              [ 4.,  5.,  6.]])
      >>> torch.kthvalue(x, 2, 0, True)
      torch.return_types.kthvalue(values=tensor([[4., 5., 6.]]), indices=tensor([[1, 1, 1]]))
  """.format(**single_dim_common))
  
  add_docstr(torch.lcm,
             r"""
  lcm(input, other, *, out=None) -> Tensor
  
  Computes the element-wise least common multiple (LCM) of :attr:`input` and :attr:`other`.
  
  Both :attr:`input` and :attr:`other` must have integer types.
  
  .. note::
      This defines :math:`lcm(0, 0) = 0` and :math:`lcm(0, a) = 0`.
  
  Args:
      {input}
      other (Tensor): the second input tensor
  
  Keyword arguments:
      {out}
  
  Example::
  
      >>> a = torch.tensor([5, 10, 15])
      >>> b = torch.tensor([3, 4, 5])
      >>> torch.lcm(a, b)
      tensor([15, 20, 15])
      >>> c = torch.tensor([3])
      >>> torch.lcm(a, c)
      tensor([15, 30, 15])
  """.format(**common_args))
  
  add_docstr(torch.ldexp, r"""
  ldexp(input, other, *, out=None) -> Tensor
  
  Multiplies :attr:`input` by 2**:attr:`other`.
  
  .. math::
      \text{{out}}_i = \text{{input}}_i * 2^\text{{other}}_i
  """ + r"""
  
  Typically this function is used to construct floating point numbers by multiplying
  mantissas in :attr:`input` with integral powers of two created from the exponents
  in :attr:`other`.
  
  Args:
      {input}
      other (Tensor): a tensor of exponents, typically integers.
  
  Keyword args:
      {out}
  
  Example::
  
      >>> torch.ldexp(torch.tensor([1.]), torch.tensor([1]))
      tensor([2.])
      >>> torch.ldexp(torch.tensor([1.0]), torch.tensor([1, 2, 3, 4]))
      tensor([ 2.,  4.,  8., 16.])
  
  
  """.format(**common_args))
  
  add_docstr(torch.le, r"""
  le(input, other, *, out=None) -> Tensor
  
  Computes :math:`\text{input} \leq \text{other}` element-wise.
  """ + r"""
  
  The second argument can be a number or a tensor whose shape is
  :ref:`broadcastable <broadcasting-semantics>` with the first argument.
  
  Args:
      input (Tensor): the tensor to compare
      other (Tensor or Scalar): the tensor or value to compare
  
  Keyword args:
      {out}
  
  Returns:
      A boolean tensor that is True where :attr:`input` is less than or equal to
      :attr:`other` and False elsewhere
  
  Example::
  
      >>> torch.le(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]]))
      tensor([[True, False], [True, True]])
  """.format(**common_args))
  
  add_docstr(torch.less_equal, r"""
  less_equal(input, other, *, out=None) -> Tensor
  
  Alias for :func:`torch.le`.
  """)
  
  add_docstr(torch.lerp,
             r"""
  lerp(input, end, weight, *, out=None)
  
  Does a linear interpolation of two tensors :attr:`start` (given by :attr:`input`) and :attr:`end` based
  on a scalar or tensor :attr:`weight` and returns the resulting :attr:`out` tensor.
  
  .. math::
      \text{out}_i = \text{start}_i + \text{weight}_i \times (\text{end}_i - \text{start}_i)
  """ + r"""
  The shapes of :attr:`start` and :attr:`end` must be
  :ref:`broadcastable <broadcasting-semantics>`. If :attr:`weight` is a tensor, then
  the shapes of :attr:`weight`, :attr:`start`, and :attr:`end` must be :ref:`broadcastable <broadcasting-semantics>`.
  
  Args:
      input (Tensor): the tensor with the starting points
      end (Tensor): the tensor with the ending points
      weight (float or tensor): the weight for the interpolation formula
  
  Keyword args:
      {out}
  
  Example::
  
      >>> start = torch.arange(1., 5.)
      >>> end = torch.empty(4).fill_(10)
      >>> start
      tensor([ 1.,  2.,  3.,  4.])
      >>> end
      tensor([ 10.,  10.,  10.,  10.])
      >>> torch.lerp(start, end, 0.5)
      tensor([ 5.5000,  6.0000,  6.5000,  7.0000])
      >>> torch.lerp(start, end, torch.full_like(start, 0.5))
      tensor([ 5.5000,  6.0000,  6.5000,  7.0000])
  """.format(**common_args))
  
  add_docstr(torch.lgamma,
             r"""
  lgamma(input, *, out=None) -> Tensor
  
  Computes the natural logarithm of the absolute value of the gamma function on :attr:`input`.
  
  .. math::
      \text{out}_{i} = \ln \Gamma(|\text{input}_{i}|)
  """ + """
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.arange(0.5, 2, 0.5)
      >>> torch.lgamma(a)
      tensor([ 0.5724,  0.0000, -0.1208])
  """.format(**common_args))
  
  add_docstr(torch.linspace, r"""
  linspace(start, end, steps, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
  
  Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly
  spaced from :attr:`start` to :attr:`end`, inclusive. That is, the value are:
  
  .. math::
      (\text{start},
      \text{start} + \frac{\text{end} - \text{start}}{\text{steps} - 1},
      \ldots,
      \text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{\text{steps} - 1},
      \text{end})
  """ + """
  
  .. warning::
      Not providing a value for :attr:`steps` is deprecated. For backwards
      compatibility, not providing a value for :attr:`steps` will create a tensor
      with 100 elements. Note that this behavior is not reflected in the
      documented function signature and should not be relied on. In a future
      PyTorch release, failing to provide a value for :attr:`steps` will throw a
      runtime error.
  
  Args:
      start (float): the starting value for the set of points
      end (float): the ending value for the set of points
      steps (int): size of the constructed tensor
  
  Keyword arguments:
      {out}
      dtype (torch.dtype, optional): the data type to perform the computation in.
          Default: if None, uses the global default dtype (see torch.get_default_dtype())
          when both :attr:`start` and :attr:`end` are real,
          and corresponding complex dtype when either is complex.
      {layout}
      {device}
      {requires_grad}
  
  
  Example::
  
      >>> torch.linspace(3, 10, steps=5)
      tensor([  3.0000,   4.7500,   6.5000,   8.2500,  10.0000])
      >>> torch.linspace(-10, 10, steps=5)
      tensor([-10.,  -5.,   0.,   5.,  10.])
      >>> torch.linspace(start=-10, end=10, steps=5)
      tensor([-10.,  -5.,   0.,   5.,  10.])
      >>> torch.linspace(start=-10, end=10, steps=1)
      tensor([-10.])
  """.format(**factory_common_args))
  
  add_docstr(torch.log,
             r"""
  log(input, *, out=None) -> Tensor
  
  Returns a new tensor with the natural logarithm of the elements
  of :attr:`input`.
  
  .. math::
      y_{i} = \log_{e} (x_{i})
  """ + r"""
  
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(5)
      >>> a
      tensor([-0.7168, -0.5471, -0.8933, -1.4428, -0.1190])
      >>> torch.log(a)
      tensor([ nan,  nan,  nan,  nan,  nan])
  """.format(**common_args))
  
  add_docstr(torch.log10,
             r"""
  log10(input, *, out=None) -> Tensor
  
  Returns a new tensor with the logarithm to the base 10 of the elements
  of :attr:`input`.
  
  .. math::
      y_{i} = \log_{10} (x_{i})
  """ + r"""
  
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.rand(5)
      >>> a
      tensor([ 0.5224,  0.9354,  0.7257,  0.1301,  0.2251])
  
  
      >>> torch.log10(a)
      tensor([-0.2820, -0.0290, -0.1392, -0.8857, -0.6476])
  
  """.format(**common_args))
  
  add_docstr(torch.log1p,
             r"""
  log1p(input, *, out=None) -> Tensor
  
  Returns a new tensor with the natural logarithm of (1 + :attr:`input`).
  
  .. math::
      y_i = \log_{e} (x_i + 1)
  """ + r"""
  .. note:: This function is more accurate than :func:`torch.log` for small
            values of :attr:`input`
  
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(5)
      >>> a
      tensor([-1.0090, -0.9923,  1.0249, -0.5372,  0.2492])
      >>> torch.log1p(a)
      tensor([    nan, -4.8653,  0.7055, -0.7705,  0.2225])
  """.format(**common_args))
  
  add_docstr(torch.log2,
             r"""
  log2(input, *, out=None) -> Tensor
  
  Returns a new tensor with the logarithm to the base 2 of the elements
  of :attr:`input`.
  
  .. math::
      y_{i} = \log_{2} (x_{i})
  """ + r"""
  
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.rand(5)
      >>> a
      tensor([ 0.8419,  0.8003,  0.9971,  0.5287,  0.0490])
  
  
      >>> torch.log2(a)
      tensor([-0.2483, -0.3213, -0.0042, -0.9196, -4.3504])
  
  """.format(**common_args))
  
  add_docstr(torch.logaddexp,
             r"""
  logaddexp(input, other, *, out=None) -> Tensor
  
  Logarithm of the sum of exponentiations of the inputs.
  
  Calculates pointwise :math:`\log\left(e^x + e^y\right)`. This function is useful
  in statistics where the calculated probabilities of events may be so small as to
  exceed the range of normal floating point numbers. In such cases the logarithm
  of the calculated probability is stored. This function allows adding
  probabilities stored in such a fashion.
  
  This op should be disambiguated with :func:`torch.logsumexp` which performs a
  reduction on a single tensor.
  
  Args:
      {input}
      other (Tensor): the second input tensor
  
  Keyword arguments:
      {out}
  
  Example::
  
      >>> torch.logaddexp(torch.tensor([-1.0]), torch.tensor([-1.0, -2, -3]))
      tensor([-0.3069, -0.6867, -0.8731])
      >>> torch.logaddexp(torch.tensor([-100.0, -200, -300]), torch.tensor([-1.0, -2, -3]))
      tensor([-1., -2., -3.])
      >>> torch.logaddexp(torch.tensor([1.0, 2000, 30000]), torch.tensor([-1.0, -2, -3]))
      tensor([1.1269e+00, 2.0000e+03, 3.0000e+04])
  """.format(**common_args))
  
  add_docstr(torch.logaddexp2,
             r"""
  logaddexp2(input, other, *, out=None) -> Tensor
  
  Logarithm of the sum of exponentiations of the inputs in base-2.
  
  Calculates pointwise :math:`\log_2\left(2^x + 2^y\right)`. See
  :func:`torch.logaddexp` for more details.
  
  Args:
      {input}
      other (Tensor): the second input tensor
  
  Keyword arguments:
      {out}
  """.format(**common_args))
  
  add_docstr(torch.xlogy,
             r"""
  xlogy(input, other, *, out=None) -> Tensor
  
  Alias for :func:`torch.special.xlogy`.
  """)
  
  add_docstr(torch.logical_and,
             r"""
  logical_and(input, other, *, out=None) -> Tensor
  
  Computes the element-wise logical AND of the given input tensors. Zeros are treated as ``False`` and nonzeros are
  treated as ``True``.
  
  Args:
      {input}
      other (Tensor): the tensor to compute AND with
  
  Keyword args:
      {out}
  
  Example::
  
      >>> torch.logical_and(torch.tensor([True, False, True]), torch.tensor([True, False, False]))
      tensor([ True, False, False])
      >>> a = torch.tensor([0, 1, 10, 0], dtype=torch.int8)
      >>> b = torch.tensor([4, 0, 1, 0], dtype=torch.int8)
      >>> torch.logical_and(a, b)
      tensor([False, False,  True, False])
      >>> torch.logical_and(a.double(), b.double())
      tensor([False, False,  True, False])
      >>> torch.logical_and(a.double(), b)
      tensor([False, False,  True, False])
      >>> torch.logical_and(a, b, out=torch.empty(4, dtype=torch.bool))
      tensor([False, False,  True, False])
  """.format(**common_args))
  
  add_docstr(torch.logical_not,
             r"""
  logical_not(input, *, out=None) -> Tensor
  
  Computes the element-wise logical NOT of the given input tensor. If not specified, the output tensor will have the bool
  dtype. If the input tensor is not a bool tensor, zeros are treated as ``False`` and non-zeros are treated as ``True``.
  
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> torch.logical_not(torch.tensor([True, False]))
      tensor([False,  True])
      >>> torch.logical_not(torch.tensor([0, 1, -10], dtype=torch.int8))
      tensor([ True, False, False])
      >>> torch.logical_not(torch.tensor([0., 1.5, -10.], dtype=torch.double))
      tensor([ True, False, False])
      >>> torch.logical_not(torch.tensor([0., 1., -10.], dtype=torch.double), out=torch.empty(3, dtype=torch.int16))
      tensor([1, 0, 0], dtype=torch.int16)
  """.format(**common_args))
  
  add_docstr(torch.logical_or,
             r"""
  logical_or(input, other, *, out=None) -> Tensor
  
  Computes the element-wise logical OR of the given input tensors. Zeros are treated as ``False`` and nonzeros are
  treated as ``True``.
  
  Args:
      {input}
      other (Tensor): the tensor to compute OR with
  
  Keyword args:
      {out}
  
  Example::
  
      >>> torch.logical_or(torch.tensor([True, False, True]), torch.tensor([True, False, False]))
      tensor([ True, False,  True])
      >>> a = torch.tensor([0, 1, 10, 0], dtype=torch.int8)
      >>> b = torch.tensor([4, 0, 1, 0], dtype=torch.int8)
      >>> torch.logical_or(a, b)
      tensor([ True,  True,  True, False])
      >>> torch.logical_or(a.double(), b.double())
      tensor([ True,  True,  True, False])
      >>> torch.logical_or(a.double(), b)
      tensor([ True,  True,  True, False])
      >>> torch.logical_or(a, b, out=torch.empty(4, dtype=torch.bool))
      tensor([ True,  True,  True, False])
  """.format(**common_args))
  
  add_docstr(torch.logical_xor,
             r"""
  logical_xor(input, other, *, out=None) -> Tensor
  
  Computes the element-wise logical XOR of the given input tensors. Zeros are treated as ``False`` and nonzeros are
  treated as ``True``.
  
  Args:
      {input}
      other (Tensor): the tensor to compute XOR with
  
  Keyword args:
      {out}
  
  Example::
  
      >>> torch.logical_xor(torch.tensor([True, False, True]), torch.tensor([True, False, False]))
      tensor([False, False,  True])
      >>> a = torch.tensor([0, 1, 10, 0], dtype=torch.int8)
      >>> b = torch.tensor([4, 0, 1, 0], dtype=torch.int8)
      >>> torch.logical_xor(a, b)
      tensor([ True,  True, False, False])
      >>> torch.logical_xor(a.double(), b.double())
      tensor([ True,  True, False, False])
      >>> torch.logical_xor(a.double(), b)
      tensor([ True,  True, False, False])
      >>> torch.logical_xor(a, b, out=torch.empty(4, dtype=torch.bool))
      tensor([ True,  True, False, False])
  """.format(**common_args))
  
  add_docstr(torch.logspace, """
  logspace(start, end, steps, base=10.0, *, \
           out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
  """ + r"""
  
  Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly
  spaced from :math:`{{\text{{base}}}}^{{\text{{start}}}}` to
  :math:`{{\text{{base}}}}^{{\text{{end}}}}`, inclusive, on a logarithmic scale
  with base :attr:`base`. That is, the values are:
  
  .. math::
      (\text{base}^{\text{start}},
      \text{base}^{(\text{start} + \frac{\text{end} - \text{start}}{ \text{steps} - 1})},
      \ldots,
      \text{base}^{(\text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{ \text{steps} - 1})},
      \text{base}^{\text{end}})
  """ + """
  
  .. warning::
      Not providing a value for :attr:`steps` is deprecated. For backwards
      compatibility, not providing a value for :attr:`steps` will create a tensor
      with 100 elements. Note that this behavior is not reflected in the
      documented function signature and should not be relied on. In a future
      PyTorch release, failing to provide a value for :attr:`steps` will throw a
      runtime error.
  
  Args:
      start (float): the starting value for the set of points
      end (float): the ending value for the set of points
      steps (int): size of the constructed tensor
      base (float, optional): base of the logarithm function. Default: ``10.0``.
  
  Keyword arguments:
      {out}
      dtype (torch.dtype, optional): the data type to perform the computation in.
          Default: if None, uses the global default dtype (see torch.get_default_dtype())
          when both :attr:`start` and :attr:`end` are real,
          and corresponding complex dtype when either is complex.
      {layout}
      {device}
      {requires_grad}
  
  Example::
  
      >>> torch.logspace(start=-10, end=10, steps=5)
      tensor([ 1.0000e-10,  1.0000e-05,  1.0000e+00,  1.0000e+05,  1.0000e+10])
      >>> torch.logspace(start=0.1, end=1.0, steps=5)
      tensor([  1.2589,   2.1135,   3.5481,   5.9566,  10.0000])
      >>> torch.logspace(start=0.1, end=1.0, steps=1)
      tensor([1.2589])
      >>> torch.logspace(start=2, end=2, steps=1, base=2)
      tensor([4.0])
  """.format(**factory_common_args))
  
  add_docstr(torch.logsumexp,
             r"""
  logsumexp(input, dim, keepdim=False, *, out=None)
  
  Returns the log of summed exponentials of each row of the :attr:`input`
  tensor in the given dimension :attr:`dim`. The computation is numerically
  stabilized.
  
  For summation index :math:`j` given by `dim` and other indices :math:`i`, the result is
  
      .. math::
          \text{{logsumexp}}(x)_{{i}} = \log \sum_j \exp(x_{{ij}})
  
  {keepdim_details}
  
  Args:
      {input}
      {dim}
      {keepdim}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(3, 3)
      >>> torch.logsumexp(a, 1)
      tensor([1.4907, 1.0593, 1.5696])
      >>> torch.dist(torch.logsumexp(a, 1), torch.log(torch.sum(torch.exp(a), 1)))
      tensor(1.6859e-07)
  """.format(**multi_dim_common))
  
  add_docstr(torch.lstsq,
             r"""
  lstsq(input, A, *, out=None) -> (Tensor, Tensor)
  
  Computes the solution to the least squares and least norm problems for a full
  rank matrix :math:`A` of size :math:`(m \times n)` and a matrix :math:`B` of
  size :math:`(m \times k)`.
  
  If :math:`m \geq n`, :func:`lstsq` solves the least-squares problem:
  
  .. math::
  
     \begin{array}{ll}
     \min_X & \|AX-B\|_2.
     \end{array}
  
  If :math:`m < n`, :func:`lstsq` solves the least-norm problem:
  
  .. math::
  
     \begin{array}{llll}
     \min_X & \|X\|_2 & \text{subject to} & AX = B.
     \end{array}
  
  Returned tensor :math:`X` has shape :math:`(\max(m, n) \times k)`. The first :math:`n`
  rows of :math:`X` contains the solution. If :math:`m \geq n`, the residual sum of squares
  for the solution in each column is given by the sum of squares of elements in the
  remaining :math:`m - n` rows of that column.
  
  .. warning::
  
      :func:`torch.lstsq` is deprecated in favor of :func:`torch.linalg.lstsq`
      and will be removed in a future PyTorch release. :func:`torch.linalg.lstsq`
      has reversed arguments and does not return the QR decomposition in the returned tuple,
      (it returns other information about the problem).
      The returned `solution` in :func:`torch.lstsq` stores the residuals of the solution in the
      last `m - n` columns in the case `m > n`. In :func:`torch.linalg.lstsq`, the residuals
      are in the field 'residuals' of the returned named tuple.
  
      Unpacking the solution as ``X = torch.lstsq(B, A).solution[:A.size(1)]`` should be replaced with
  
      .. code:: python
  
          X = torch.linalg.lstsq(A, B).solution
  
  .. note::
      The case when :math:`m < n` is not supported on the GPU.
  
  Args:
      input (Tensor): the matrix :math:`B`
      A (Tensor): the :math:`m` by :math:`n` matrix :math:`A`
  
  Keyword args:
      out (tuple, optional): the optional destination tensor
  
  Returns:
      (Tensor, Tensor): A namedtuple (solution, QR) containing:
  
          - **solution** (*Tensor*): the least squares solution
          - **QR** (*Tensor*): the details of the QR factorization
  
  .. note::
  
      The returned matrices will always be transposed, irrespective of the strides
      of the input matrices. That is, they will have stride `(1, m)` instead of
      `(m, 1)`.
  
  Example::
  
      >>> A = torch.tensor([[1., 1, 1],
      ...                   [2, 3, 4],
      ...                   [3, 5, 2],
      ...                   [4, 2, 5],
      ...                   [5, 4, 3]])
      >>> B = torch.tensor([[-10., -3],
      ...                   [ 12, 14],
      ...                   [ 14, 12],
      ...                   [ 16, 16],
      ...                   [ 18, 16]])
      >>> X, _ = torch.lstsq(B, A)
      >>> X
      tensor([[  2.0000,   1.0000],
              [  1.0000,   1.0000],
              [  1.0000,   2.0000],
              [ 10.9635,   4.8501],
              [  8.9332,   5.2418]])
  """)
  
  add_docstr(torch.lt, r"""
  lt(input, other, *, out=None) -> Tensor
  
  Computes :math:`\text{input} < \text{other}` element-wise.
  """ + r"""
  
  The second argument can be a number or a tensor whose shape is
  :ref:`broadcastable <broadcasting-semantics>` with the first argument.
  
  Args:
      input (Tensor): the tensor to compare
      other (Tensor or float): the tensor or value to compare
  
  Keyword args:
      {out}
  
  Returns:
      A boolean tensor that is True where :attr:`input` is less than :attr:`other` and False elsewhere
  
  Example::
  
      >>> torch.lt(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]]))
      tensor([[False, False], [True, False]])
  """.format(**common_args))
  
  add_docstr(torch.lu_unpack, r"""
  lu_unpack(LU_data, LU_pivots, unpack_data=True, unpack_pivots=True, *, out=None) -> (Tensor, Tensor, Tensor)
  
  Unpacks the data and pivots from a LU factorization of a tensor into tensors ``L`` and ``U`` and a permutation tensor ``P``
  such that ``LU_data, LU_pivots = (P @ L @ U).lu()``.
  
  Returns a tuple of tensors as ``(the P tensor (permutation matrix), the L tensor, the U tensor)``.
  
  .. note:: ``P.dtype == LU_data.dtype`` and ``P.dtype`` is not an integer type so that matrix products with ``P``
            are possible without casting it to a floating type.
  
  Args:
      LU_data (Tensor): the packed LU factorization data
      LU_pivots (Tensor): the packed LU factorization pivots
      unpack_data (bool): flag indicating if the data should be unpacked.
                          If ``False``, then the returned ``L`` and ``U`` are ``None``.
                          Default: ``True``
      unpack_pivots (bool): flag indicating if the pivots should be unpacked into a permutation matrix ``P``.
                            If ``False``, then the returned ``P`` is  ``None``.
                            Default: ``True``
      out (tuple, optional): a tuple of three tensors to use for the outputs ``(P, L, U)``.
  
  Examples::
  
      >>> A = torch.randn(2, 3, 3)
      >>> A_LU, pivots = A.lu()
      >>> P, A_L, A_U = torch.lu_unpack(A_LU, pivots)
      >>>
      >>> # can recover A from factorization
      >>> A_ = torch.bmm(P, torch.bmm(A_L, A_U))
  
      >>> # LU factorization of a rectangular matrix:
      >>> A = torch.randn(2, 3, 2)
      >>> A_LU, pivots = A.lu()
      >>> P, A_L, A_U = torch.lu_unpack(A_LU, pivots)
      >>> P
      tensor([[[1., 0., 0.],
               [0., 1., 0.],
               [0., 0., 1.]],
  
              [[0., 0., 1.],
               [0., 1., 0.],
               [1., 0., 0.]]])
      >>> A_L
      tensor([[[ 1.0000,  0.0000],
               [ 0.4763,  1.0000],
               [ 0.3683,  0.1135]],
  
              [[ 1.0000,  0.0000],
               [ 0.2957,  1.0000],
               [-0.9668, -0.3335]]])
      >>> A_U
      tensor([[[ 2.1962,  1.0881],
               [ 0.0000, -0.8681]],
  
              [[-1.0947,  0.3736],
               [ 0.0000,  0.5718]]])
      >>> A_ = torch.bmm(P, torch.bmm(A_L, A_U))
      >>> torch.norm(A_ - A)
      tensor(2.9802e-08)
  """.format(**common_args))
  
  add_docstr(torch.less, r"""
  less(input, other, *, out=None) -> Tensor
  
  Alias for :func:`torch.lt`.
  """)
  
  add_docstr(torch.lu_solve,
             r"""
  lu_solve(b, LU_data, LU_pivots, *, out=None) -> Tensor
  
  Returns the LU solve of the linear system :math:`Ax = b` using the partially pivoted
  LU factorization of A from :meth:`torch.lu`.
  
  This function supports ``float``, ``double``, ``cfloat`` and ``cdouble`` dtypes for :attr:`input`.
  
  Arguments:
      b (Tensor): the RHS tensor of size :math:`(*, m, k)`, where :math:`*`
                  is zero or more batch dimensions.
      LU_data (Tensor): the pivoted LU factorization of A from :meth:`torch.lu` of size :math:`(*, m, m)`,
                         where :math:`*` is zero or more batch dimensions.
      LU_pivots (IntTensor): the pivots of the LU factorization from :meth:`torch.lu` of size :math:`(*, m)`,
                             where :math:`*` is zero or more batch dimensions.
                             The batch dimensions of :attr:`LU_pivots` must be equal to the batch dimensions of
                             :attr:`LU_data`.
  
  Keyword args:
      {out}
  
  Example::
  
      >>> A = torch.randn(2, 3, 3)
      >>> b = torch.randn(2, 3, 1)
      >>> A_LU = torch.lu(A)
      >>> x = torch.lu_solve(b, *A_LU)
      >>> torch.norm(torch.bmm(A, x) - b)
      tensor(1.00000e-07 *
             2.8312)
  """.format(**common_args))
  
  add_docstr(torch.masked_select,
             r"""
  masked_select(input, mask, *, out=None) -> Tensor
  
  Returns a new 1-D tensor which indexes the :attr:`input` tensor according to
  the boolean mask :attr:`mask` which is a `BoolTensor`.
  
  The shapes of the :attr:`mask` tensor and the :attr:`input` tensor don't need
  to match, but they must be :ref:`broadcastable <broadcasting-semantics>`.
  
  .. note:: The returned tensor does **not** use the same storage
            as the original tensor
  
  Args:
      {input}
      mask  (BoolTensor): the tensor containing the binary mask to index with
  
  Keyword args:
      {out}
  
  Example::
  
      >>> x = torch.randn(3, 4)
      >>> x
      tensor([[ 0.3552, -2.3825, -0.8297,  0.3477],
              [-1.2035,  1.2252,  0.5002,  0.6248],
              [ 0.1307, -2.0608,  0.1244,  2.0139]])
      >>> mask = x.ge(0.5)
      >>> mask
      tensor([[False, False, False, False],
              [False, True, True, True],
              [False, False, False, True]])
      >>> torch.masked_select(x, mask)
      tensor([ 1.2252,  0.5002,  0.6248,  2.0139])
  """.format(**common_args))
  
  add_docstr(torch.matrix_rank, r"""
  matrix_rank(input, tol=None, symmetric=False, *, out=None) -> Tensor
  
  Returns the numerical rank of a 2-D tensor. The method to compute the
  matrix rank is done using SVD by default. If :attr:`symmetric` is ``True``,
  then :attr:`input` is assumed to be symmetric, and the computation of the
  rank is done by obtaining the eigenvalues.
  
  :attr:`tol` is the threshold below which the singular values (or the eigenvalues
  when :attr:`symmetric` is ``True``) are considered to be 0. If :attr:`tol` is not
  specified, :attr:`tol` is set to ``S.max() * max(S.size()) * eps`` where `S` is the
  singular values (or the eigenvalues when :attr:`symmetric` is ``True``), and ``eps``
  is the epsilon value for the datatype of :attr:`input`.
  
  .. warning::
  
      :func:`torch.matrix_rank` is deprecated in favor of :func:`torch.linalg.matrix_rank`
      and will be removed in a future PyTorch release. The parameter :attr:`symmetric` was
      renamed in :func:`torch.linalg.matrix_rank` to :attr:`hermitian`.
  
  Args:
      input (Tensor): the input 2-D tensor
      tol (float, optional): the tolerance value. Default: ``None``
      symmetric(bool, optional): indicates whether :attr:`input` is symmetric.
                                 Default: ``False``
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.eye(10)
      >>> torch.matrix_rank(a)
      tensor(10)
      >>> b = torch.eye(10)
      >>> b[0, 0] = 0
      >>> torch.matrix_rank(b)
      tensor(9)
  """.format(**common_args))
  
  add_docstr(torch.matrix_power, r"""
  matrix_power(input, n, *, out=None) -> Tensor
  
  Alias for :func:`torch.linalg.matrix_power`
  """)
  
  add_docstr(torch.matrix_exp, r"""
  matrix_exp(input) -> Tensor
  
  Computes the matrix exponential of a square matrix or of each square matrix in a batch.
  For a matrix :attr:`input`, the matrix exponential is defined as
  
  .. math::
      \mathrm{e}^\text{input} = \sum_{k=0}^\infty \text{input}^k / k!
  
  """ + r"""
  The implementation is based on:
  
  Bader, P.; Blanes, S.; Casas, F.
  Computing the Matrix Exponential with an Optimized Taylor Polynomial Approximation.
  Mathematics 2019, 7, 1174.
  
  Args:
      {input}
  
  Example::
  
      >>> a = torch.randn(2, 2, 2)
      >>> a[0, :, :] = torch.eye(2, 2)
      >>> a[1, :, :] = 2 * torch.eye(2, 2)
      >>> a
      tensor([[[1., 0.],
               [0., 1.]],
  
              [[2., 0.],
               [0., 2.]]])
      >>> torch.matrix_exp(a)
      tensor([[[2.7183, 0.0000],
               [0.0000, 2.7183]],
  
               [[7.3891, 0.0000],
                [0.0000, 7.3891]]])
  
      >>> import math
      >>> x = torch.tensor([[0, math.pi/3], [-math.pi/3, 0]])
      >>> x.matrix_exp() # should be [[cos(pi/3), sin(pi/3)], [-sin(pi/3), cos(pi/3)]]
      tensor([[ 0.5000,  0.8660],
              [-0.8660,  0.5000]])
  """.format(**common_args))
  
  add_docstr(torch.max,
             r"""
  max(input) -> Tensor
  
  Returns the maximum value of all elements in the ``input`` tensor.
  
  .. warning::
      This function produces deterministic (sub)gradients unlike ``max(dim=0)``
  
  Args:
      {input}
  
  Example::
  
      >>> a = torch.randn(1, 3)
      >>> a
      tensor([[ 0.6763,  0.7445, -2.2369]])
      >>> torch.max(a)
      tensor(0.7445)
  
  .. function:: max(input, dim, keepdim=False, *, out=None) -> (Tensor, LongTensor)
     :noindex:
  
  Returns a namedtuple ``(values, indices)`` where ``values`` is the maximum
  value of each row of the :attr:`input` tensor in the given dimension
  :attr:`dim`. And ``indices`` is the index location of each maximum value found
  (argmax).
  
  If ``keepdim`` is ``True``, the output tensors are of the same size
  as ``input`` except in the dimension ``dim`` where they are of size 1.
  Otherwise, ``dim`` is squeezed (see :func:`torch.squeeze`), resulting
  in the output tensors having 1 fewer dimension than ``input``.
  
  .. note:: If there are multiple maximal values in a reduced row then
            the indices of the first maximal value are returned.
  
  Args:
      {input}
      {dim}
      {keepdim} Default: ``False``.
  
  Keyword args:
      out (tuple, optional): the result tuple of two output tensors (max, max_indices)
  
  Example::
  
      >>> a = torch.randn(4, 4)
      >>> a
      tensor([[-1.2360, -0.2942, -0.1222,  0.8475],
              [ 1.1949, -1.1127, -2.2379, -0.6702],
              [ 1.5717, -0.9207,  0.1297, -1.8768],
              [-0.6172,  1.0036, -0.6060, -0.2432]])
      >>> torch.max(a, 1)
      torch.return_types.max(values=tensor([0.8475, 1.1949, 1.5717, 1.0036]), indices=tensor([3, 0, 0, 1]))
  
  .. function:: max(input, other, *, out=None) -> Tensor
     :noindex:
  
  See :func:`torch.maximum`.
  
  """.format(**single_dim_common))
  
  add_docstr(torch.maximum, r"""
  maximum(input, other, *, out=None) -> Tensor
  
  Computes the element-wise maximum of :attr:`input` and :attr:`other`.
  
  .. note::
      If one of the elements being compared is a NaN, then that element is returned.
      :func:`maximum` is not supported for tensors with complex dtypes.
  
  Args:
      {input}
      other (Tensor): the second input tensor
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.tensor((1, 2, -1))
      >>> b = torch.tensor((3, 0, 4))
      >>> torch.maximum(a, b)
      tensor([3, 2, 4])
  """.format(**common_args))
  
  add_docstr(torch.fmax, r"""
  fmax(input, other, *, out=None) -> Tensor
  
  Computes the element-wise maximum of :attr:`input` and :attr:`other`.
  
  This is like :func:`torch.maximum` except it handles NaNs differently:
  if exactly one of the two elements being compared is a NaN then the non-NaN element is taken as the maximum.
  Only if both elements are NaN is NaN propagated.
  
  This function is a wrapper around C++'s ``std::fmax`` and is similar to NumPy's ``fmax`` function.
  
  Supports :ref:`broadcasting to a common shape <broadcasting-semantics>`,
  :ref:`type promotion <type-promotion-doc>`, and integer and floating-point inputs.
  
  Args:
      {input}
      other (Tensor): the second input tensor
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.tensor([9.7, float('nan'), 3.1, float('nan')])
      >>> b = torch.tensor([-2.2, 0.5, float('nan'), float('nan')])
      >>> torch.fmax(a, b)
      tensor([9.7000, 0.5000, 3.1000,    nan])
  """.format(**common_args))
  
  add_docstr(torch.amax,
             r"""
  amax(input, dim, keepdim=False, *, out=None) -> Tensor
  
  Returns the maximum value of each slice of the :attr:`input` tensor in the given
  dimension(s) :attr:`dim`.
  
  .. note::
      The difference between ``max``/``min`` and ``amax``/``amin`` is:
          - ``amax``/``amin`` supports reducing on multiple dimensions,
          - ``amax``/``amin`` does not return indices,
          - ``amax``/``amin`` evenly distributes gradient between equal values,
            while ``max(dim)``/``min(dim)`` propagates gradient only to a single
            index in the source tensor.
  
  {keepdim_details}
  
  Args:
      {input}
      {dim}
      {keepdim}
  
  Keyword args:
    {out}
  
  Example::
  
      >>> a = torch.randn(4, 4)
      >>> a
      tensor([[ 0.8177,  1.4878, -0.2491,  0.9130],
              [-0.7158,  1.1775,  2.0992,  0.4817],
              [-0.0053,  0.0164, -1.3738, -0.0507],
              [ 1.9700,  1.1106, -1.0318, -1.0816]])
      >>> torch.amax(a, 1)
      tensor([1.4878, 2.0992, 0.0164, 1.9700])
  """.format(**multi_dim_common))
  
  add_docstr(torch.argmax,
             r"""
  argmax(input) -> LongTensor
  
  Returns the indices of the maximum value of all elements in the :attr:`input` tensor.
  
  This is the second value returned by :meth:`torch.max`. See its
  documentation for the exact semantics of this method.
  
  .. note:: If there are multiple maximal values then the indices of the first maximal value are returned.
  
  Args:
      {input}
  
  Example::
  
      >>> a = torch.randn(4, 4)
      >>> a
      tensor([[ 1.3398,  0.2663, -0.2686,  0.2450],
              [-0.7401, -0.8805, -0.3402, -1.1936],
              [ 0.4907, -1.3948, -1.0691, -0.3132],
              [-1.6092,  0.5419, -0.2993,  0.3195]])
      >>> torch.argmax(a)
      tensor(0)
  
  .. function:: argmax(input, dim, keepdim=False) -> LongTensor
     :noindex:
  
  Returns the indices of the maximum values of a tensor across a dimension.
  
  This is the second value returned by :meth:`torch.max`. See its
  documentation for the exact semantics of this method.
  
  Args:
      {input}
      {dim} If ``None``, the argmax of the flattened input is returned.
      {keepdim} Ignored if ``dim=None``.
  
  Example::
  
      >>> a = torch.randn(4, 4)
      >>> a
      tensor([[ 1.3398,  0.2663, -0.2686,  0.2450],
              [-0.7401, -0.8805, -0.3402, -1.1936],
              [ 0.4907, -1.3948, -1.0691, -0.3132],
              [-1.6092,  0.5419, -0.2993,  0.3195]])
      >>> torch.argmax(a, dim=1)
      tensor([ 0,  2,  0,  1])
  """.format(**single_dim_common))
  
  add_docstr(torch.mean, r"""
  mean(input, *, dtype=None) -> Tensor
  
  Returns the mean value of all elements in the :attr:`input` tensor.
  
  Args:
      {input}
  
  Keyword args:
      {dtype}
  
  Example::
  
      >>> a = torch.randn(1, 3)
      >>> a
      tensor([[ 0.2294, -0.5481,  1.3288]])
      >>> torch.mean(a)
      tensor(0.3367)
  
  .. function:: mean(input, dim, keepdim=False, *, dtype=None, out=None) -> Tensor
     :noindex:
  
  Returns the mean value of each row of the :attr:`input` tensor in the given
  dimension :attr:`dim`. If :attr:`dim` is a list of dimensions,
  reduce over all of them.
  
  {keepdim_details}
  
  Args:
      {input}
      {dim}
      {keepdim}
  
  Keyword args:
      {dtype}
      {out}
  
  .. seealso::
  
      :func:`torch.nanmean` computes the mean value of `non-NaN` elements.
  
  Example::
  
      >>> a = torch.randn(4, 4)
      >>> a
      tensor([[-0.3841,  0.6320,  0.4254, -0.7384],
              [-0.9644,  1.0131, -0.6549, -1.4279],
              [-0.2951, -1.3350, -0.7694,  0.5600],
              [ 1.0842, -0.9580,  0.3623,  0.2343]])
      >>> torch.mean(a, 1)
      tensor([-0.0163, -0.5085, -0.4599,  0.1807])
      >>> torch.mean(a, 1, True)
      tensor([[-0.0163],
              [-0.5085],
              [-0.4599],
              [ 0.1807]])
  """.format(**multi_dim_common))
  
  add_docstr(torch.nanmean, r"""
  nanmean(input, dim=None, keepdim=False, *, dtype=None, out=None) -> Tensor
  
  Computes the mean of all `non-NaN` elements along the specified dimensions.
  
  This function is identical to :func:`torch.mean` when there are no `NaN` values
  in the :attr:`input` tensor. In the presence of `NaN`, :func:`torch.mean` will
  propagate the `NaN` to the output whereas :func:`torch.nanmean` will ignore the
  `NaN` values (`torch.nanmean(a)` is equivalent to `torch.mean(a[~a.isnan()])`).
  
  {keepdim_details}
  
  Args:
      {input}
      {dim} If `None`, reduces all dimensions. Default is `None`.
      {keepdim}
  
  Keyword args:
      {dtype}
      {out}
  
  .. seealso::
  
      :func:`torch.mean` computes the mean value, propagating `NaN`.
  
  Example::
  
      >>> x = torch.tensor([[torch.nan, 1, 2], [1, 2, 3]])
      >>> x.mean()
      tensor(nan)
      >>> x.nanmean()
      tensor(1.8000)
      >>> x.mean(dim=0)
      tensor([   nan, 1.5000, 2.5000])
      >>> x.nanmean(dim=0)
      tensor([1.0000, 1.5000, 2.5000])
  
      # If all elements in the reduced dimensions are NaN then the result is NaN
      >>> torch.tensor([torch.nan]).nanmean()
      tensor(nan)
  """.format(**multi_dim_common))
  
  add_docstr(torch.median,
             r"""
  median(input) -> Tensor
  
  Returns the median of the values in :attr:`input`.
  
  .. note::
      The median is not unique for :attr:`input` tensors with an even number
      of elements. In this case the lower of the two medians is returned. To
      compute the mean of both medians, use :func:`torch.quantile` with ``q=0.5`` instead.
  
  .. warning::
      This function produces deterministic (sub)gradients unlike ``median(dim=0)``
  
  Args:
      {input}
  
  Example::
  
      >>> a = torch.randn(1, 3)
      >>> a
      tensor([[ 1.5219, -1.5212,  0.2202]])
      >>> torch.median(a)
      tensor(0.2202)
  
  .. function:: median(input, dim=-1, keepdim=False, *, out=None) -> (Tensor, LongTensor)
     :noindex:
  
  Returns a namedtuple ``(values, indices)`` where ``values`` contains the median of each row of :attr:`input`
  in the dimension :attr:`dim`, and ``indices`` contains the index of the median values found in the dimension :attr:`dim`.
  
  By default, :attr:`dim` is the last dimension of the :attr:`input` tensor.
  
  If :attr:`keepdim` is ``True``, the output tensors are of the same size
  as :attr:`input` except in the dimension :attr:`dim` where they are of size 1.
  Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in
  the outputs tensor having 1 fewer dimension than :attr:`input`.
  
  .. note::
      The median is not unique for :attr:`input` tensors with an even number
      of elements in the dimension :attr:`dim`. In this case the lower of the
      two medians is returned. To compute the mean of both medians in
      :attr:`input`, use :func:`torch.quantile` with ``q=0.5`` instead.
  
  .. warning::
      ``indices`` does not necessarily contain the first occurrence of each
      median value found, unless it is unique.
      The exact implementation details are device-specific.
      Do not expect the same result when run on CPU and GPU in general.
      For the same reason do not expect the gradients to be deterministic.
  
  Args:
      {input}
      {dim}
      {keepdim}
  
  Keyword args:
      out ((Tensor, Tensor), optional): The first tensor will be populated with the median values and the second
                                        tensor, which must have dtype long, with their indices in the dimension
                                        :attr:`dim` of :attr:`input`.
  
  Example::
  
      >>> a = torch.randn(4, 5)
      >>> a
      tensor([[ 0.2505, -0.3982, -0.9948,  0.3518, -1.3131],
              [ 0.3180, -0.6993,  1.0436,  0.0438,  0.2270],
              [-0.2751,  0.7303,  0.2192,  0.3321,  0.2488],
              [ 1.0778, -1.9510,  0.7048,  0.4742, -0.7125]])
      >>> torch.median(a, 1)
      torch.return_types.median(values=tensor([-0.3982,  0.2270,  0.2488,  0.4742]), indices=tensor([1, 4, 4, 3]))
  """.format(**single_dim_common))
  
  add_docstr(torch.nanmedian,
             r"""
  nanmedian(input) -> Tensor
  
  Returns the median of the values in :attr:`input`, ignoring ``NaN`` values.
  
  This function is identical to :func:`torch.median` when there are no ``NaN`` values in :attr:`input`.
  When :attr:`input` has one or more ``NaN`` values, :func:`torch.median` will always return ``NaN``,
  while this function will return the median of the non-``NaN`` elements in :attr:`input`.
  If all the elements in :attr:`input` are ``NaN`` it will also return ``NaN``.
  
  Args:
      {input}
  
  Example::
  
      >>> a = torch.tensor([1, float('nan'), 3, 2])
      >>> a.median()
      tensor(nan)
      >>> a.nanmedian()
      tensor(2.)
  
  .. function:: nanmedian(input, dim=-1, keepdim=False, *, out=None) -> (Tensor, LongTensor)
     :noindex:
  
  Returns a namedtuple ``(values, indices)`` where ``values`` contains the median of each row of :attr:`input`
  in the dimension :attr:`dim`, ignoring ``NaN`` values, and ``indices`` contains the index of the median values
  found in the dimension :attr:`dim`.
  
  This function is identical to :func:`torch.median` when there are no ``NaN`` values in a reduced row. When a reduced row has
  one or more ``NaN`` values, :func:`torch.median` will always reduce it to ``NaN``, while this function will reduce it to the
  median of the non-``NaN`` elements. If all the elements in a reduced row are ``NaN`` then it will be reduced to ``NaN``, too.
  
  Args:
      {input}
      {dim}
      {keepdim}
  
  Keyword args:
      out ((Tensor, Tensor), optional): The first tensor will be populated with the median values and the second
                                        tensor, which must have dtype long, with their indices in the dimension
                                        :attr:`dim` of :attr:`input`.
  
  Example::
  
      >>> a = torch.tensor([[2, 3, 1], [float('nan'), 1, float('nan')]])
      >>> a
      tensor([[2., 3., 1.],
              [nan, 1., nan]])
      >>> a.median(0)
      torch.return_types.median(values=tensor([nan, 1., nan]), indices=tensor([1, 1, 1]))
      >>> a.nanmedian(0)
      torch.return_types.nanmedian(values=tensor([2., 1., 1.]), indices=tensor([0, 1, 0]))
  """.format(**single_dim_common))
  
  add_docstr(torch.quantile, r"""
  quantile(input, q, dim=None, keepdim=False, *, out=None) -> Tensor
  
  Computes the q-th quantiles of each row of the :attr:`input` tensor
  along the dimension :attr:`dim`.
  
  To compute the quantile, we map q in [0, 1] to the range of indices [0, n] to find the location
  of the quantile in the sorted input. If the quantile lies between two data points ``a < b`` with
  indices ``i`` and ``j`` in the sorted order, result is computed using linear interpolation as follows:
  
  ``a + (b - a) * fraction``, where ``fraction`` is the fractional part of the computed quantile index.
  
  If :attr:`q` is a 1D tensor, the first dimension of the output represents the quantiles and has size
  equal to the size of :attr:`q`, the remaining dimensions are what remains from the reduction.
  
  .. note::
      By default :attr:`dim` is ``None`` resulting in the :attr:`input` tensor being flattened before computation.
  
  Args:
      {input}
      q (float or Tensor): a scalar or 1D tensor of values in the range [0, 1].
      {dim}
      {keepdim}
  
  Keyword arguments:
      {out}
  
  Example::
  
      >>> a = torch.randn(2, 3)
      >>> a
      tensor([[ 0.0795, -1.2117,  0.9765],
              [ 1.1707,  0.6706,  0.4884]])
      >>> q = torch.tensor([0.25, 0.5, 0.75])
      >>> torch.quantile(a, q, dim=1, keepdim=True)
      tensor([[[-0.5661],
              [ 0.5795]],
  
              [[ 0.0795],
              [ 0.6706]],
  
              [[ 0.5280],
              [ 0.9206]]])
      >>> torch.quantile(a, q, dim=1, keepdim=True).shape
      torch.Size([3, 2, 1])
      >>> a = torch.arange(4.)
      >>> a
      tensor([0., 1., 2., 3.])
  """.format(**single_dim_common))
  
  add_docstr(torch.nanquantile, r"""
  nanquantile(input, q, dim=None, keepdim=False, *, out=None) -> Tensor
  
  This is a variant of :func:`torch.quantile` that "ignores" ``NaN`` values,
  computing the quantiles :attr:`q` as if ``NaN`` values in :attr:`input` did
  not exist. If all values in a reduced row are ``NaN`` then the quantiles for
  that reduction will be ``NaN``. See the documentation for :func:`torch.quantile`.
  
  Args:
      {input}
      q (float or Tensor): a scalar or 1D tensor of quantile values in the range [0, 1]
      {dim}
      {keepdim}
  
  Keyword arguments:
      {out}
  
  Example::
  
      >>> t = torch.tensor([float('nan'), 1, 2])
      >>> t.quantile(0.5)
      tensor(nan)
      >>> t.nanquantile(0.5)
      tensor(1.5000)
      >>> t = torch.tensor([[float('nan'), float('nan')], [1, 2]])
      >>> t
      tensor([[nan, nan],
              [1., 2.]])
      >>> t.nanquantile(0.5, dim=0)
      tensor([1., 2.])
      >>> t.nanquantile(0.5, dim=1)
      tensor([   nan, 1.5000])
  """.format(**single_dim_common))
  
  add_docstr(torch.min,
             r"""
  min(input) -> Tensor
  
  Returns the minimum value of all elements in the :attr:`input` tensor.
  
  .. warning::
      This function produces deterministic (sub)gradients unlike ``min(dim=0)``
  
  Args:
      {input}
  
  Example::
  
      >>> a = torch.randn(1, 3)
      >>> a
      tensor([[ 0.6750,  1.0857,  1.7197]])
      >>> torch.min(a)
      tensor(0.6750)
  
  .. function:: min(input, dim, keepdim=False, *, out=None) -> (Tensor, LongTensor)
     :noindex:
  
  Returns a namedtuple ``(values, indices)`` where ``values`` is the minimum
  value of each row of the :attr:`input` tensor in the given dimension
  :attr:`dim`. And ``indices`` is the index location of each minimum value found
  (argmin).
  
  If :attr:`keepdim` is ``True``, the output tensors are of the same size as
  :attr:`input` except in the dimension :attr:`dim` where they are of size 1.
  Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in
  the output tensors having 1 fewer dimension than :attr:`input`.
  
  .. note:: If there are multiple minimal values in a reduced row then
            the indices of the first minimal value are returned.
  
  Args:
      {input}
      {dim}
      {keepdim}
  
  Keyword args:
      out (tuple, optional): the tuple of two output tensors (min, min_indices)
  
  Example::
  
      >>> a = torch.randn(4, 4)
      >>> a
      tensor([[-0.6248,  1.1334, -1.1899, -0.2803],
              [-1.4644, -0.2635, -0.3651,  0.6134],
              [ 0.2457,  0.0384,  1.0128,  0.7015],
              [-0.1153,  2.9849,  2.1458,  0.5788]])
      >>> torch.min(a, 1)
      torch.return_types.min(values=tensor([-1.1899, -1.4644,  0.0384, -0.1153]), indices=tensor([2, 0, 1, 0]))
  
  .. function:: min(input, other, *, out=None) -> Tensor
     :noindex:
  
  See :func:`torch.minimum`.
  """.format(**single_dim_common))
  
  add_docstr(torch.minimum, r"""
  minimum(input, other, *, out=None) -> Tensor
  
  Computes the element-wise minimum of :attr:`input` and :attr:`other`.
  
  .. note::
      If one of the elements being compared is a NaN, then that element is returned.
      :func:`minimum` is not supported for tensors with complex dtypes.
  
  Args:
      {input}
      other (Tensor): the second input tensor
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.tensor((1, 2, -1))
      >>> b = torch.tensor((3, 0, 4))
      >>> torch.minimum(a, b)
      tensor([1, 0, -1])
  """.format(**common_args))
  
  add_docstr(torch.fmin, r"""
  fmin(input, other, *, out=None) -> Tensor
  
  Computes the element-wise minimum of :attr:`input` and :attr:`other`.
  
  This is like :func:`torch.minimum` except it handles NaNs differently:
  if exactly one of the two elements being compared is a NaN then the non-NaN element is taken as the minimum.
  Only if both elements are NaN is NaN propagated.
  
  This function is a wrapper around C++'s ``std::fmin`` and is similar to NumPy's ``fmin`` function.
  
  Supports :ref:`broadcasting to a common shape <broadcasting-semantics>`,
  :ref:`type promotion <type-promotion-doc>`, and integer and floating-point inputs.
  
  Args:
      {input}
      other (Tensor): the second input tensor
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.tensor([2.2, float('nan'), 2.1, float('nan')])
      >>> b = torch.tensor([-9.3, 0.1, float('nan'), float('nan')])
      >>> torch.fmin(a, b)
      tensor([-9.3000, 0.1000, 2.1000,    nan])
  """.format(**common_args))
  
  add_docstr(torch.amin,
             r"""
  amin(input, dim, keepdim=False, *, out=None) -> Tensor
  
  Returns the minimum value of each slice of the :attr:`input` tensor in the given
  dimension(s) :attr:`dim`.
  
  .. note::
      The difference between ``max``/``min`` and ``amax``/``amin`` is:
          - ``amax``/``amin`` supports reducing on multiple dimensions,
          - ``amax``/``amin`` does not return indices,
          - ``amax``/``amin`` evenly distributes gradient between equal values,
            while ``max(dim)``/``min(dim)`` propagates gradient only to a single
            index in the source tensor.
  
  {keepdim_details}
  
  Args:
      {input}
      {dim}
      {keepdim}
  
  Keyword args:
    {out}
  
  Example::
  
      >>> a = torch.randn(4, 4)
      >>> a
      tensor([[ 0.6451, -0.4866,  0.2987, -1.3312],
              [-0.5744,  1.2980,  1.8397, -0.2713],
              [ 0.9128,  0.9214, -1.7268, -0.2995],
              [ 0.9023,  0.4853,  0.9075, -1.6165]])
      >>> torch.amin(a, 1)
      tensor([-1.3312, -0.5744, -1.7268, -1.6165])
  """.format(**multi_dim_common))
  
  add_docstr(torch.aminmax, r"""
  aminmax(input, *, dim=None, keepdim=False, out=None) -> (Tensor min, Tensor max)
  
  Computes the minimum and maximum values of the :attr:`input` tensor.
  
  Args:
      input (Tensor):
          The input tensor
  
  Keyword Args:
      dim (Optional[int]):
          The dimension along which to compute the values. If `None`,
          computes the values over the entire :attr:`input` tensor.
          Default is `None`.
      keepdim (bool):
          If `True`, the reduced dimensions will be kept in the output
          tensor as dimensions with size 1 for broadcasting, otherwise
          they will be removed, as if calling (:func:`torch.squeeze`).
          Default is `False`.
      out (Optional[Tuple[Tensor, Tensor]]):
          Optional tensors on which to write the result. Must have the same
          shape and dtype as the expected output.
          Default is `None`.
  
  Returns:
      A named tuple `(min, max)` containing the minimum and maximum values.
  
  Raises:
      RuntimeError
          If any of the dimensions to compute the values over has size 0.
  
  .. note::
      NaN values are propagated to the output if at least one value is NaN.
  
  .. seealso::
      :func:`torch.amin` computes just the minimum value
      :func:`torch.amax` computes just the maximum value
  
  Example::
  
      >>> torch.aminmax(torch.tensor([1, -3, 5]))
      torch.return_types.aminmax(
      min=tensor(-3),
      max=tensor(5))
  
      >>> # aminmax propagates NaNs
      >>> torch.aminmax(torch.tensor([1, -3, 5, torch.nan]))
      torch.return_types.aminmax(
      min=tensor(nan),
      max=tensor(nan))
  
      >>> t = torch.arange(10).view(2, 5)
      >>> t
      tensor([[0, 1, 2, 3, 4],
              [5, 6, 7, 8, 9]])
      >>> t.aminmax(dim=0, keepdim=True)
      torch.return_types.aminmax(
      min=tensor([[0, 1, 2, 3, 4]]),
      max=tensor([[5, 6, 7, 8, 9]]))
  """)
  
  add_docstr(torch.argmin,
             r"""
  argmin(input, dim=None, keepdim=False) -> LongTensor
  
  Returns the indices of the minimum value(s) of the flattened tensor or along a dimension
  
  This is the second value returned by :meth:`torch.min`. See its
  documentation for the exact semantics of this method.
  
  .. note:: If there are multiple minimal values then the indices of the first minimal value are returned.
  
  Args:
      {input}
      {dim} If ``None``, the argmin of the flattened input is returned.
      {keepdim} Ignored if ``dim=None``.
  
  Example::
  
      >>> a = torch.randn(4, 4)
      >>> a
      tensor([[ 0.1139,  0.2254, -0.1381,  0.3687],
              [ 1.0100, -1.1975, -0.0102, -0.4732],
              [-0.9240,  0.1207, -0.7506, -1.0213],
              [ 1.7809, -1.2960,  0.9384,  0.1438]])
      >>> torch.argmin(a)
      tensor(13)
      >>> torch.argmin(a, dim=1)
      tensor([ 2,  1,  3,  1])
      >>> torch.argmin(a, dim=1, keepdim=True)
      tensor([[2],
              [1],
              [3],
              [1]])
  """.format(**single_dim_common))
  
  add_docstr(torch.mm,
             r"""
  mm(input, mat2, *, out=None) -> Tensor
  
  Performs a matrix multiplication of the matrices :attr:`input` and :attr:`mat2`.
  
  If :attr:`input` is a :math:`(n \times m)` tensor, :attr:`mat2` is a
  :math:`(m \times p)` tensor, :attr:`out` will be a :math:`(n \times p)` tensor.
  
  .. note:: This function does not :ref:`broadcast <broadcasting-semantics>`.
            For broadcasting matrix products, see :func:`torch.matmul`.
  
  Supports strided and sparse 2-D tensors as inputs, autograd with
  respect to strided inputs.
  
  {tf32_note}
  
  Args:
      input (Tensor): the first matrix to be matrix multiplied
      mat2 (Tensor): the second matrix to be matrix multiplied
  
  Keyword args:
      {out}
  
  Example::
  
      >>> mat1 = torch.randn(2, 3)
      >>> mat2 = torch.randn(3, 3)
      >>> torch.mm(mat1, mat2)
      tensor([[ 0.4851,  0.5037, -0.3633],
              [-0.0760, -3.6705,  2.4784]])
  """.format(**common_args, **tf32_notes))
  
  add_docstr(torch.hspmm,
             r"""
  hspmm(mat1, mat2, *, out=None) -> Tensor
  
  Performs a matrix multiplication of a :ref:`sparse COO matrix
  <sparse-coo-docs>` :attr:`mat1` and a strided matrix :attr:`mat2`. The
  result is a (1 + 1)-dimensional :ref:`hybrid COO matrix
  <sparse-hybrid-coo-docs>`.
  
  Args:
      mat1 (Tensor): the first sparse matrix to be matrix multiplied
      mat2 (Tensor): the second strided matrix to be matrix multiplied
  
  Keyword args:
      {out}
  """.format(**common_args))
  
  add_docstr(torch.matmul,
             r"""
  matmul(input, other, *, out=None) -> Tensor
  
  Matrix product of two tensors.
  
  The behavior depends on the dimensionality of the tensors as follows:
  
  - If both tensors are 1-dimensional, the dot product (scalar) is returned.
  - If both arguments are 2-dimensional, the matrix-matrix product is returned.
  - If the first argument is 1-dimensional and the second argument is 2-dimensional,
    a 1 is prepended to its dimension for the purpose of the matrix multiply.
    After the matrix multiply, the prepended dimension is removed.
  - If the first argument is 2-dimensional and the second argument is 1-dimensional,
    the matrix-vector product is returned.
  - If both arguments are at least 1-dimensional and at least one argument is
    N-dimensional (where N > 2), then a batched matrix multiply is returned.  If the first
    argument is 1-dimensional, a 1 is prepended to its dimension for the purpose of the
    batched matrix multiply and removed after.  If the second argument is 1-dimensional, a
    1 is appended to its dimension for the purpose of the batched matrix multiple and removed after.
    The non-matrix (i.e. batch) dimensions are :ref:`broadcasted <broadcasting-semantics>` (and thus
    must be broadcastable).  For example, if :attr:`input` is a
    :math:`(j \times 1 \times n \times n)` tensor and :attr:`other` is a :math:`(k \times n \times n)`
    tensor, :attr:`out` will be a :math:`(j \times k \times n \times n)` tensor.
  
    Note that the broadcasting logic only looks at the batch dimensions when determining if the inputs
    are broadcastable, and not the matrix dimensions. For example, if :attr:`input` is a
    :math:`(j \times 1 \times n \times m)` tensor and :attr:`other` is a :math:`(k \times m \times p)`
    tensor, these inputs are valid for broadcasting even though the final two dimensions (i.e. the
    matrix dimensions) are different. :attr:`out` will be a :math:`(j \times k \times n \times p)` tensor.
  
  {tf32_note}
  
  .. note::
  
      The 1-dimensional dot product version of this function does not support an :attr:`out` parameter.
  
  Arguments:
      input (Tensor): the first tensor to be multiplied
      other (Tensor): the second tensor to be multiplied
  
  Keyword args:
      {out}
  
  Example::
  
      >>> # vector x vector
      >>> tensor1 = torch.randn(3)
      >>> tensor2 = torch.randn(3)
      >>> torch.matmul(tensor1, tensor2).size()
      torch.Size([])
      >>> # matrix x vector
      >>> tensor1 = torch.randn(3, 4)
      >>> tensor2 = torch.randn(4)
      >>> torch.matmul(tensor1, tensor2).size()
      torch.Size([3])
      >>> # batched matrix x broadcasted vector
      >>> tensor1 = torch.randn(10, 3, 4)
      >>> tensor2 = torch.randn(4)
      >>> torch.matmul(tensor1, tensor2).size()
      torch.Size([10, 3])
      >>> # batched matrix x batched matrix
      >>> tensor1 = torch.randn(10, 3, 4)
      >>> tensor2 = torch.randn(10, 4, 5)
      >>> torch.matmul(tensor1, tensor2).size()
      torch.Size([10, 3, 5])
      >>> # batched matrix x broadcasted matrix
      >>> tensor1 = torch.randn(10, 3, 4)
      >>> tensor2 = torch.randn(4, 5)
      >>> torch.matmul(tensor1, tensor2).size()
      torch.Size([10, 3, 5])
  
  """.format(**common_args, **tf32_notes))
  
  add_docstr(torch.mode,
             r"""
  mode(input, dim=-1, keepdim=False, *, out=None) -> (Tensor, LongTensor)
  
  Returns a namedtuple ``(values, indices)`` where ``values`` is the mode
  value of each row of the :attr:`input` tensor in the given dimension
  :attr:`dim`, i.e. a value which appears most often
  in that row, and ``indices`` is the index location of each mode value found.
  
  By default, :attr:`dim` is the last dimension of the :attr:`input` tensor.
  
  If :attr:`keepdim` is ``True``, the output tensors are of the same size as
  :attr:`input` except in the dimension :attr:`dim` where they are of size 1.
  Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting
  in the output tensors having 1 fewer dimension than :attr:`input`.
  
  .. note:: This function is not defined for ``torch.cuda.Tensor`` yet.
  
  Args:
      {input}
      {dim}
      {keepdim}
  
  Keyword args:
      out (tuple, optional): the result tuple of two output tensors (values, indices)
  
  Example::
  
      >>> a = torch.randint(10, (5,))
      >>> a
      tensor([6, 5, 1, 0, 2])
      >>> b = a + (torch.randn(50, 1) * 5).long()
      >>> torch.mode(b, 0)
      torch.return_types.mode(values=tensor([6, 5, 1, 0, 2]), indices=tensor([2, 2, 2, 2, 2]))
  """.format(**single_dim_common))
  
  add_docstr(torch.mul, r"""
  mul(input, other, *, out=None) -> Tensor
  
  Multiplies :attr:`input` by :attr:`other`.
  
  
  .. math::
      \text{out}_i = \text{input}_i \times \text{other}_i
  """ + r"""
  
  Supports :ref:`broadcasting to a common shape <broadcasting-semantics>`,
  :ref:`type promotion <type-promotion-doc>`, and integer, float, and complex inputs.
  
  Args:
      {input}
      other (Tensor or Number) - the tensor or number to multiply input by.
  
  Keyword args:
      {out}
  
  Examples::
  
      >>> a = torch.randn(3)
      >>> a
      tensor([ 0.2015, -0.4255,  2.6087])
      >>> torch.mul(a, 100)
      tensor([  20.1494,  -42.5491,  260.8663])
  
      >>> b = torch.randn(4, 1)
      >>> b
      tensor([[ 1.1207],
              [-0.3137],
              [ 0.0700],
              [ 0.8378]])
      >>> c = torch.randn(1, 4)
      >>> c
      tensor([[ 0.5146,  0.1216, -0.5244,  2.2382]])
      >>> torch.mul(b, c)
      tensor([[ 0.5767,  0.1363, -0.5877,  2.5083],
              [-0.1614, -0.0382,  0.1645, -0.7021],
              [ 0.0360,  0.0085, -0.0367,  0.1567],
              [ 0.4312,  0.1019, -0.4394,  1.8753]])
  """.format(**common_args))
  
  add_docstr(torch.multiply, r"""
  multiply(input, other, *, out=None)
  
  Alias for :func:`torch.mul`.
  """)
  
  add_docstr(torch.multinomial,
             r"""
  multinomial(input, num_samples, replacement=False, *, generator=None, out=None) -> LongTensor
  
  Returns a tensor where each row contains :attr:`num_samples` indices sampled
  from the multinomial probability distribution located in the corresponding row
  of tensor :attr:`input`.
  
  .. note::
      The rows of :attr:`input` do not need to sum to one (in which case we use
      the values as weights), but must be non-negative, finite and have
      a non-zero sum.
  
  Indices are ordered from left to right according to when each was sampled
  (first samples are placed in first column).
  
  If :attr:`input` is a vector, :attr:`out` is a vector of size :attr:`num_samples`.
  
  If :attr:`input` is a matrix with `m` rows, :attr:`out` is an matrix of shape
  :math:`(m \times \text{{num\_samples}})`.
  
  If replacement is ``True``, samples are drawn with replacement.
  
  If not, they are drawn without replacement, which means that when a
  sample index is drawn for a row, it cannot be drawn again for that row.
  
  .. note::
      When drawn without replacement, :attr:`num_samples` must be lower than
      number of non-zero elements in :attr:`input` (or the min number of non-zero
      elements in each row of :attr:`input` if it is a matrix).
  
  Args:
      input (Tensor): the input tensor containing probabilities
      num_samples (int): number of samples to draw
      replacement (bool, optional): whether to draw with replacement or not
  
  Keyword args:
      {generator}
      {out}
  
  Example::
  
      >>> weights = torch.tensor([0, 10, 3, 0], dtype=torch.float) # create a tensor of weights
      >>> torch.multinomial(weights, 2)
      tensor([1, 2])
      >>> torch.multinomial(weights, 4) # ERROR!
      RuntimeError: invalid argument 2: invalid multinomial distribution (with replacement=False,
      not enough non-negative category to sample) at ../aten/src/TH/generic/THTensorRandom.cpp:320
      >>> torch.multinomial(weights, 4, replacement=True)
      tensor([ 2,  1,  1,  1])
  """.format(**common_args))
  
  add_docstr(torch.mv,
             r"""
  mv(input, vec, *, out=None) -> Tensor
  
  Performs a matrix-vector product of the matrix :attr:`input` and the vector
  :attr:`vec`.
  
  If :attr:`input` is a :math:`(n \times m)` tensor, :attr:`vec` is a 1-D tensor of
  size :math:`m`, :attr:`out` will be 1-D of size :math:`n`.
  
  .. note:: This function does not :ref:`broadcast <broadcasting-semantics>`.
  
  Args:
      input (Tensor): matrix to be multiplied
      vec (Tensor): vector to be multiplied
  
  Keyword args:
      {out}
  
  Example::
  
      >>> mat = torch.randn(2, 3)
      >>> vec = torch.randn(3)
      >>> torch.mv(mat, vec)
      tensor([ 1.0404, -0.6361])
  """.format(**common_args))
  
  add_docstr(torch.mvlgamma,
             r"""
  mvlgamma(input, p, *, out=None) -> Tensor
  
  Alias for :func:`torch.special.multigammaln`.
  """)
  
  add_docstr(torch.movedim, r"""
  movedim(input, source, destination) -> Tensor
  
  Moves the dimension(s) of :attr:`input` at the position(s) in :attr:`source`
  to the position(s) in :attr:`destination`.
  
  Other dimensions of :attr:`input` that are not explicitly moved remain in
  their original order and appear at the positions not specified in :attr:`destination`.
  
  Args:
      {input}
      source (int or tuple of ints): Original positions of the dims to move. These must be unique.
      destination (int or tuple of ints): Destination positions for each of the original dims. These must also be unique.
  
  Examples::
  
      >>> t = torch.randn(3,2,1)
      >>> t
      tensor([[[-0.3362],
              [-0.8437]],
  
              [[-0.9627],
              [ 0.1727]],
  
              [[ 0.5173],
              [-0.1398]]])
      >>> torch.movedim(t, 1, 0).shape
      torch.Size([2, 3, 1])
      >>> torch.movedim(t, 1, 0)
      tensor([[[-0.3362],
              [-0.9627],
              [ 0.5173]],
  
              [[-0.8437],
              [ 0.1727],
              [-0.1398]]])
      >>> torch.movedim(t, (1, 2), (0, 1)).shape
      torch.Size([2, 1, 3])
      >>> torch.movedim(t, (1, 2), (0, 1))
      tensor([[[-0.3362, -0.9627,  0.5173]],
  
              [[-0.8437,  0.1727, -0.1398]]])
  """.format(**common_args))
  
  add_docstr(torch.moveaxis, r"""
  moveaxis(input, source, destination) -> Tensor
  
  Alias for :func:`torch.movedim`.
  
  This function is equivalent to NumPy's moveaxis function.
  
  Examples::
  
      >>> t = torch.randn(3,2,1)
      >>> t
      tensor([[[-0.3362],
              [-0.8437]],
  
              [[-0.9627],
              [ 0.1727]],
  
              [[ 0.5173],
              [-0.1398]]])
      >>> torch.moveaxis(t, 1, 0).shape
      torch.Size([2, 3, 1])
      >>> torch.moveaxis(t, 1, 0)
      tensor([[[-0.3362],
              [-0.9627],
              [ 0.5173]],
  
              [[-0.8437],
              [ 0.1727],
              [-0.1398]]])
      >>> torch.moveaxis(t, (1, 2), (0, 1)).shape
      torch.Size([2, 1, 3])
      >>> torch.moveaxis(t, (1, 2), (0, 1))
      tensor([[[-0.3362, -0.9627,  0.5173]],
  
              [[-0.8437,  0.1727, -0.1398]]])
  """.format(**common_args))
  
  add_docstr(torch.swapdims, r"""
  swapdims(input, dim0, dim1) -> Tensor
  
  Alias for :func:`torch.transpose`.
  
  This function is equivalent to NumPy's swapaxes function.
  
  Examples::
  
      >>> x = torch.tensor([[[0,1],[2,3]],[[4,5],[6,7]]])
      >>> x
      tensor([[[0, 1],
              [2, 3]],
  
              [[4, 5],
              [6, 7]]])
      >>> torch.swapdims(x, 0, 1)
      tensor([[[0, 1],
              [4, 5]],
  
              [[2, 3],
              [6, 7]]])
      >>> torch.swapdims(x, 0, 2)
      tensor([[[0, 4],
              [2, 6]],
  
              [[1, 5],
              [3, 7]]])
  """.format(**common_args))
  
  add_docstr(torch.swapaxes, r"""
  swapaxes(input, axis0, axis1) -> Tensor
  
  Alias for :func:`torch.transpose`.
  
  This function is equivalent to NumPy's swapaxes function.
  
  Examples::
  
      >>> x = torch.tensor([[[0,1],[2,3]],[[4,5],[6,7]]])
      >>> x
      tensor([[[0, 1],
              [2, 3]],
  
              [[4, 5],
              [6, 7]]])
      >>> torch.swapaxes(x, 0, 1)
      tensor([[[0, 1],
              [4, 5]],
  
              [[2, 3],
              [6, 7]]])
      >>> torch.swapaxes(x, 0, 2)
      tensor([[[0, 4],
              [2, 6]],
  
              [[1, 5],
              [3, 7]]])
  """.format(**common_args))
  
  add_docstr(torch.narrow,
             r"""
  narrow(input, dim, start, length) -> Tensor
  
  Returns a new tensor that is a narrowed version of :attr:`input` tensor. The
  dimension :attr:`dim` is input from :attr:`start` to ``start + length``. The
  returned tensor and :attr:`input` tensor share the same underlying storage.
  
  Args:
      input (Tensor): the tensor to narrow
      dim (int): the dimension along which to narrow
      start (int): the starting dimension
      length (int): the distance to the ending dimension
  
  Example::
  
      >>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
      >>> torch.narrow(x, 0, 0, 2)
      tensor([[ 1,  2,  3],
              [ 4,  5,  6]])
      >>> torch.narrow(x, 1, 1, 2)
      tensor([[ 2,  3],
              [ 5,  6],
              [ 8,  9]])
  """)
  
  add_docstr(torch.nan_to_num,
             r"""
  nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None) -> Tensor
  
  Replaces :literal:`NaN`, positive infinity, and negative infinity values in :attr:`input`
  with the values specified by :attr:`nan`, :attr:`posinf`, and :attr:`neginf`, respectively.
  By default, :literal:`NaN`\ s are replaced with zero, positive infinity is replaced with the
  greatest finite value representable by :attr:`input`'s dtype, and negative infinity
  is replaced with the least finite value representable by :attr:`input`'s dtype.
  
  Args:
      {input}
      nan (Number, optional): the value to replace :literal:`NaN`\s with. Default is zero.
      posinf (Number, optional): if a Number, the value to replace positive infinity values with.
          If None, positive infinity values are replaced with the greatest finite value representable by :attr:`input`'s dtype.
          Default is None.
      neginf (Number, optional): if a Number, the value to replace negative infinity values with.
          If None, negative infinity values are replaced with the lowest finite value representable by :attr:`input`'s dtype.
          Default is None.
  
  Keyword args:
      {out}
  
  Example::
  
      >>> x = torch.tensor([float('nan'), float('inf'), -float('inf'), 3.14])
      >>> torch.nan_to_num(x)
      tensor([ 0.0000e+00,  3.4028e+38, -3.4028e+38,  3.1400e+00])
      >>> torch.nan_to_num(x, nan=2.0)
      tensor([ 2.0000e+00,  3.4028e+38, -3.4028e+38,  3.1400e+00])
      >>> torch.nan_to_num(x, nan=2.0, posinf=1.0)
      tensor([ 2.0000e+00,  1.0000e+00, -3.4028e+38,  3.1400e+00])
  
  """.format(**common_args))
  
  add_docstr(torch.ne, r"""
  ne(input, other, *, out=None) -> Tensor
  
  Computes :math:`\text{input} \neq \text{other}` element-wise.
  """ + r"""
  
  The second argument can be a number or a tensor whose shape is
  :ref:`broadcastable <broadcasting-semantics>` with the first argument.
  
  Args:
      input (Tensor): the tensor to compare
      other (Tensor or float): the tensor or value to compare
  
  Keyword args:
      {out}
  
  Returns:
      A boolean tensor that is True where :attr:`input` is not equal to :attr:`other` and False elsewhere
  
  Example::
  
      >>> torch.ne(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]]))
      tensor([[False, True], [True, False]])
  """.format(**common_args))
  
  add_docstr(torch.not_equal, r"""
  not_equal(input, other, *, out=None) -> Tensor
  
  Alias for :func:`torch.ne`.
  """)
  
  add_docstr(torch.neg,
             r"""
  neg(input, *, out=None) -> Tensor
  
  Returns a new tensor with the negative of the elements of :attr:`input`.
  
  .. math::
      \text{out} = -1 \times \text{input}
  """ + r"""
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(5)
      >>> a
      tensor([ 0.0090, -0.2262, -0.0682, -0.2866,  0.3940])
      >>> torch.neg(a)
      tensor([-0.0090,  0.2262,  0.0682,  0.2866, -0.3940])
  """.format(**common_args))
  
  add_docstr(torch.negative,
             r"""
  negative(input, *, out=None) -> Tensor
  
  Alias for :func:`torch.neg`
  """)
  
  add_docstr(torch.nextafter,
             r"""
  nextafter(input, other, *, out=None) -> Tensor
  
  Return the next floating-point value after :attr:`input` towards :attr:`other`, elementwise.
  
  The shapes of ``input`` and ``other`` must be
  :ref:`broadcastable <broadcasting-semantics>`.
  
  Args:
      input (Tensor): the first input tensor
      other (Tensor): the second input tensor
  
  Keyword args:
      {out}
  
  Example::
  
      >>> eps = torch.finfo(torch.float32).eps
      >>> torch.nextafter(torch.tensor([1.0, 2.0]), torch.tensor([2.0, 1.0])) == torch.tensor([eps + 1, 2 - eps])
      tensor([True, True])
  
  """.format(**common_args))
  
  add_docstr(torch.nonzero,
             r"""
  nonzero(input, *, out=None, as_tuple=False) -> LongTensor or tuple of LongTensors
  
  .. note::
      :func:`torch.nonzero(..., as_tuple=False) <torch.nonzero>` (default) returns a
      2-D tensor where each row is the index for a nonzero value.
  
      :func:`torch.nonzero(..., as_tuple=True) <torch.nonzero>` returns a tuple of 1-D
      index tensors, allowing for advanced indexing, so ``x[x.nonzero(as_tuple=True)]``
      gives all nonzero values of tensor ``x``. Of the returned tuple, each index tensor
      contains nonzero indices for a certain dimension.
  
      See below for more details on the two behaviors.
  
      When :attr:`input` is on CUDA, :func:`torch.nonzero() <torch.nonzero>` causes
      host-device synchronization.
  
  **When** :attr:`as_tuple` **is** ``False`` **(default)**:
  
  Returns a tensor containing the indices of all non-zero elements of
  :attr:`input`.  Each row in the result contains the indices of a non-zero
  element in :attr:`input`. The result is sorted lexicographically, with
  the last index changing the fastest (C-style).
  
  If :attr:`input` has :math:`n` dimensions, then the resulting indices tensor
  :attr:`out` is of size :math:`(z \times n)`, where :math:`z` is the total number of
  non-zero elements in the :attr:`input` tensor.
  
  **When** :attr:`as_tuple` **is** ``True``:
  
  Returns a tuple of 1-D tensors, one for each dimension in :attr:`input`,
  each containing the indices (in that dimension) of all non-zero elements of
  :attr:`input` .
  
  If :attr:`input` has :math:`n` dimensions, then the resulting tuple contains :math:`n`
  tensors of size :math:`z`, where :math:`z` is the total number of
  non-zero elements in the :attr:`input` tensor.
  
  As a special case, when :attr:`input` has zero dimensions and a nonzero scalar
  value, it is treated as a one-dimensional tensor with one element.
  
  Args:
      {input}
  
  Keyword args:
      out (LongTensor, optional): the output tensor containing indices
  
  Returns:
      LongTensor or tuple of LongTensor: If :attr:`as_tuple` is ``False``, the output
      tensor containing indices. If :attr:`as_tuple` is ``True``, one 1-D tensor for
      each dimension, containing the indices of each nonzero element along that
      dimension.
  
  Example::
  
      >>> torch.nonzero(torch.tensor([1, 1, 1, 0, 1]))
      tensor([[ 0],
              [ 1],
              [ 2],
              [ 4]])
      >>> torch.nonzero(torch.tensor([[0.6, 0.0, 0.0, 0.0],
      ...                             [0.0, 0.4, 0.0, 0.0],
      ...                             [0.0, 0.0, 1.2, 0.0],
      ...                             [0.0, 0.0, 0.0,-0.4]]))
      tensor([[ 0,  0],
              [ 1,  1],
              [ 2,  2],
              [ 3,  3]])
      >>> torch.nonzero(torch.tensor([1, 1, 1, 0, 1]), as_tuple=True)
      (tensor([0, 1, 2, 4]),)
      >>> torch.nonzero(torch.tensor([[0.6, 0.0, 0.0, 0.0],
      ...                             [0.0, 0.4, 0.0, 0.0],
      ...                             [0.0, 0.0, 1.2, 0.0],
      ...                             [0.0, 0.0, 0.0,-0.4]]), as_tuple=True)
      (tensor([0, 1, 2, 3]), tensor([0, 1, 2, 3]))
      >>> torch.nonzero(torch.tensor(5), as_tuple=True)
      (tensor([0]),)
  """.format(**common_args))
  
  add_docstr(torch.normal,
             r"""
  normal(mean, std, *, generator=None, out=None) -> Tensor
  
  Returns a tensor of random numbers drawn from separate normal distributions
  whose mean and standard deviation are given.
  
  The :attr:`mean` is a tensor with the mean of
  each output element's normal distribution
  
  The :attr:`std` is a tensor with the standard deviation of
  each output element's normal distribution
  
  The shapes of :attr:`mean` and :attr:`std` don't need to match, but the
  total number of elements in each tensor need to be the same.
  
  .. note:: When the shapes do not match, the shape of :attr:`mean`
            is used as the shape for the returned output tensor
  
  .. note:: When :attr:`std` is a CUDA tensor, this function synchronizes
            its device with the CPU.
  
  Args:
      mean (Tensor): the tensor of per-element means
      std (Tensor): the tensor of per-element standard deviations
  
  Keyword args:
      {generator}
      {out}
  
  Example::
  
      >>> torch.normal(mean=torch.arange(1., 11.), std=torch.arange(1, 0, -0.1))
      tensor([  1.0425,   3.5672,   2.7969,   4.2925,   4.7229,   6.2134,
                8.0505,   8.1408,   9.0563,  10.0566])
  
  .. function:: normal(mean=0.0, std, *, out=None) -> Tensor
     :noindex:
  
  Similar to the function above, but the means are shared among all drawn
  elements.
  
  Args:
      mean (float, optional): the mean for all distributions
      std (Tensor): the tensor of per-element standard deviations
  
  Keyword args:
      {out}
  
  Example::
  
      >>> torch.normal(mean=0.5, std=torch.arange(1., 6.))
      tensor([-1.2793, -1.0732, -2.0687,  5.1177, -1.2303])
  
  .. function:: normal(mean, std=1.0, *, out=None) -> Tensor
     :noindex:
  
  Similar to the function above, but the standard deviations are shared among
  all drawn elements.
  
  Args:
      mean (Tensor): the tensor of per-element means
      std (float, optional): the standard deviation for all distributions
  
  Keyword args:
      out (Tensor, optional): the output tensor
  
  Example::
  
      >>> torch.normal(mean=torch.arange(1., 6.))
      tensor([ 1.1552,  2.6148,  2.6535,  5.8318,  4.2361])
  
  .. function:: normal(mean, std, size, *, out=None) -> Tensor
     :noindex:
  
  Similar to the function above, but the means and standard deviations are shared
  among all drawn elements. The resulting tensor has size given by :attr:`size`.
  
  Args:
      mean (float): the mean for all distributions
      std (float): the standard deviation for all distributions
      size (int...): a sequence of integers defining the shape of the output tensor.
  
  Keyword args:
      {out}
  
  Example::
  
      >>> torch.normal(2, 3, size=(1, 4))
      tensor([[-1.3987, -1.9544,  3.6048,  0.7909]])
  """.format(**common_args))
  
  add_docstr(torch.numel,
             r"""
  numel(input) -> int
  
  Returns the total number of elements in the :attr:`input` tensor.
  
  Args:
      {input}
  
  Example::
  
      >>> a = torch.randn(1, 2, 3, 4, 5)
      >>> torch.numel(a)
      120
      >>> a = torch.zeros(4,4)
      >>> torch.numel(a)
      16
  
  """.format(**common_args))
  
  add_docstr(torch.ones,
             r"""
  ones(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
  
  Returns a tensor filled with the scalar value `1`, with the shape defined
  by the variable argument :attr:`size`.
  
  Args:
      size (int...): a sequence of integers defining the shape of the output tensor.
          Can be a variable number of arguments or a collection like a list or tuple.
  
  Keyword arguments:
      {out}
      {dtype}
      {layout}
      {device}
      {requires_grad}
  
  Example::
  
      >>> torch.ones(2, 3)
      tensor([[ 1.,  1.,  1.],
              [ 1.,  1.,  1.]])
  
      >>> torch.ones(5)
      tensor([ 1.,  1.,  1.,  1.,  1.])
  
  """.format(**factory_common_args))
  
  add_docstr(torch.ones_like,
             r"""
  ones_like(input, *, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor
  
  Returns a tensor filled with the scalar value `1`, with the same size as
  :attr:`input`. ``torch.ones_like(input)`` is equivalent to
  ``torch.ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``.
  
  .. warning::
      As of 0.4, this function does not support an :attr:`out` keyword. As an alternative,
      the old ``torch.ones_like(input, out=output)`` is equivalent to
      ``torch.ones(input.size(), out=output)``.
  
  Args:
      {input}
  
  Keyword arguments:
      {dtype}
      {layout}
      {device}
      {requires_grad}
      {memory_format}
  
  Example::
  
      >>> input = torch.empty(2, 3)
      >>> torch.ones_like(input)
      tensor([[ 1.,  1.,  1.],
              [ 1.,  1.,  1.]])
  """.format(**factory_like_common_args))
  
  add_docstr(torch.orgqr,
             r"""
  orgqr(input, tau) -> Tensor
  
  Alias for :func:`torch.linalg.householder_product`.
  """)
  
  add_docstr(torch.ormqr,
             r"""
  ormqr(input, tau, other, left=True, transpose=False, *, out=None) -> Tensor
  
  Computes the matrix-matrix multiplication of a product of Householder matrices with a general matrix.
  
  Multiplies a :math:`m \times n` matrix `C` (given by :attr:`other`) with a matrix `Q`,
  where `Q` is represented using Householder reflectors `(input, tau)`.
  See `Representation of Orthogonal or Unitary Matrices`_ for further details.
  
  If :attr:`left` is `True` then `op(Q)` times `C` is computed, otherwise the result is `C` times `op(Q)`.
  When :attr:`left` is `True`, the implicit matrix `Q` has size :math:`m \times m`.
  It has size :math:`n \times n` otherwise.
  If :attr:`transpose` is `True` then `op` is the conjugate transpose operation, otherwise it's a no-op.
  
  Supports inputs of float, double, cfloat and cdouble dtypes.
  Also supports batched inputs, and, if the input is batched, the output is batched with the same dimensions.
  
  .. seealso::
  
          :func:`torch.geqrf` can be used to form the Householder representation `(input, tau)` of matrix `Q`
          from the QR decomposition.
  
  Args:
      input (Tensor): tensor of shape `(*, mn, k)` where `*` is zero or more batch dimensions
                      and `mn` equals to `m` or `n` depending on the :attr:`left`.
      tau (Tensor): tensor of shape `(*, min(mn, k))` where `*` is zero or more batch dimensions.
      other (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
      left (bool): controls the order of multiplication.
      transpose (bool): controls whether the matrix `Q` is conjugate transposed or not.
  
  Keyword args:
      out (Tensor, optional): the output Tensor. Ignored if `None`. Default: `None`.
  
  .. _Representation of Orthogonal or Unitary Matrices:
      https://www.netlib.org/lapack/lug/node128.html
  """)
  
  add_docstr(torch.permute,
             r"""
  permute(input, dims) -> Tensor
  
  Returns a view of the original tensor :attr:`input` with its dimensions permuted.
  
  Args:
      {input}
      dims (tuple of ints): The desired ordering of dimensions
  
  Example:
      >>> x = torch.randn(2, 3, 5)
      >>> x.size()
      torch.Size([2, 3, 5])
      >>> torch.permute(x, (2, 0, 1)).size()
      torch.Size([5, 2, 3])
  """.format(**common_args))
  
  add_docstr(torch.poisson,
             r"""
  poisson(input, generator=None) -> Tensor
  
  Returns a tensor of the same size as :attr:`input` with each element
  sampled from a Poisson distribution with rate parameter given by the corresponding
  element in :attr:`input` i.e.,
  
  .. math::
      \text{{out}}_i \sim \text{{Poisson}}(\text{{input}}_i)
  
  Args:
      input (Tensor): the input tensor containing the rates of the Poisson distribution
  
  Keyword args:
      {generator}
  
  Example::
  
      >>> rates = torch.rand(4, 4) * 5  # rate parameter between 0 and 5
      >>> torch.poisson(rates)
      tensor([[9., 1., 3., 5.],
              [8., 6., 6., 0.],
              [0., 4., 5., 3.],
              [2., 1., 4., 2.]])
  """.format(**common_args))
  
  add_docstr(torch.polygamma,
             r"""
  polygamma(n, input, *, out=None) -> Tensor
  
  Alias for :func:`torch.special.polygamma`.
  """)
  
  add_docstr(torch.positive,
             r"""
  positive(input) -> Tensor
  
  Returns :attr:`input`.
  Throws a runtime error if :attr:`input` is a bool tensor.
  """ + r"""
  Args:
      {input}
  
  Example::
  
      >>> t = torch.randn(5)
      >>> t
      tensor([ 0.0090, -0.2262, -0.0682, -0.2866,  0.3940])
      >>> torch.positive(t)
      tensor([ 0.0090, -0.2262, -0.0682, -0.2866,  0.3940])
  """.format(**common_args))
  
  add_docstr(torch.pow,
             r"""
  pow(input, exponent, *, out=None) -> Tensor
  
  Takes the power of each element in :attr:`input` with :attr:`exponent` and
  returns a tensor with the result.
  
  :attr:`exponent` can be either a single ``float`` number or a `Tensor`
  with the same number of elements as :attr:`input`.
  
  When :attr:`exponent` is a scalar value, the operation applied is:
  
  .. math::
      \text{out}_i = x_i ^ \text{exponent}
  
  When :attr:`exponent` is a tensor, the operation applied is:
  
  .. math::
      \text{out}_i = x_i ^ {\text{exponent}_i}
  """ + r"""
  When :attr:`exponent` is a tensor, the shapes of :attr:`input`
  and :attr:`exponent` must be :ref:`broadcastable <broadcasting-semantics>`.
  
  Args:
      {input}
      exponent (float or tensor): the exponent value
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([ 0.4331,  1.2475,  0.6834, -0.2791])
      >>> torch.pow(a, 2)
      tensor([ 0.1875,  1.5561,  0.4670,  0.0779])
      >>> exp = torch.arange(1., 5.)
  
      >>> a = torch.arange(1., 5.)
      >>> a
      tensor([ 1.,  2.,  3.,  4.])
      >>> exp
      tensor([ 1.,  2.,  3.,  4.])
      >>> torch.pow(a, exp)
      tensor([   1.,    4.,   27.,  256.])
  
  .. function:: pow(self, exponent, *, out=None) -> Tensor
     :noindex:
  
  :attr:`self` is a scalar ``float`` value, and :attr:`exponent` is a tensor.
  The returned tensor :attr:`out` is of the same shape as :attr:`exponent`
  
  The operation applied is:
  
  .. math::
      \text{{out}}_i = \text{{self}} ^ {{\text{{exponent}}_i}}
  
  Args:
      self (float): the scalar base value for the power operation
      exponent (Tensor): the exponent tensor
  
  Keyword args:
      {out}
  
  Example::
  
      >>> exp = torch.arange(1., 5.)
      >>> base = 2
      >>> torch.pow(base, exp)
      tensor([  2.,   4.,   8.,  16.])
  """.format(**common_args))
  
  add_docstr(torch.float_power,
             r"""
  float_power(input, exponent, *, out=None) -> Tensor
  
  Raises :attr:`input` to the power of :attr:`exponent`, elementwise, in double precision.
  If neither input is complex returns a ``torch.float64`` tensor,
  and if one or more inputs is complex returns a ``torch.complex128`` tensor.
  
  .. note::
      This function always computes in double precision, unlike :func:`torch.pow`,
      which implements more typical :ref:`type promotion <type-promotion-doc>`.
      This is useful when the computation needs to be performed in a wider or more precise dtype,
      or the results of the computation may contain fractional values not representable in the input dtypes,
      like when an integer base is raised to a negative integer exponent.
  
  Args:
      input (Tensor or Number): the base value(s)
      exponent (Tensor or Number): the exponent value(s)
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randint(10, (4,))
      >>> a
      tensor([6, 4, 7, 1])
      >>> torch.float_power(a, 2)
      tensor([36., 16., 49.,  1.], dtype=torch.float64)
  
      >>> a = torch.arange(1, 5)
      >>> a
      tensor([ 1,  2,  3,  4])
      >>> exp = torch.tensor([2, -3, 4, -5])
      >>> exp
      tensor([ 2, -3,  4, -5])
      >>> torch.float_power(a, exp)
      tensor([1.0000e+00, 1.2500e-01, 8.1000e+01, 9.7656e-04], dtype=torch.float64)
  """.format(**common_args))
  
  add_docstr(torch.prod,
             r"""
  prod(input, *, dtype=None) -> Tensor
  
  Returns the product of all elements in the :attr:`input` tensor.
  
  Args:
      {input}
  
  Keyword args:
      {dtype}
  
  Example::
  
      >>> a = torch.randn(1, 3)
      >>> a
      tensor([[-0.8020,  0.5428, -1.5854]])
      >>> torch.prod(a)
      tensor(0.6902)
  
  .. function:: prod(input, dim, keepdim=False, *, dtype=None) -> Tensor
     :noindex:
  
  Returns the product of each row of the :attr:`input` tensor in the given
  dimension :attr:`dim`.
  
  {keepdim_details}
  
  Args:
      {input}
      {dim}
      {keepdim}
  
  Keyword args:
      {dtype}
  
  Example::
  
      >>> a = torch.randn(4, 2)
      >>> a
      tensor([[ 0.5261, -0.3837],
              [ 1.1857, -0.2498],
              [-1.1646,  0.0705],
              [ 1.1131, -1.0629]])
      >>> torch.prod(a, 1)
      tensor([-0.2018, -0.2962, -0.0821, -1.1831])
  """.format(**single_dim_common))
  
  add_docstr(torch.promote_types,
             r"""
  promote_types(type1, type2) -> dtype
  
  Returns the :class:`torch.dtype` with the smallest size and scalar kind that is
  not smaller nor of lower kind than either `type1` or `type2`. See type promotion
  :ref:`documentation <type-promotion-doc>` for more information on the type
  promotion logic.
  
  Args:
      type1 (:class:`torch.dtype`)
      type2 (:class:`torch.dtype`)
  
  Example::
  
      >>> torch.promote_types(torch.int32, torch.float32)
      torch.float32
      >>> torch.promote_types(torch.uint8, torch.long)
      torch.long
  """)
  
  add_docstr(torch.qr,
             r"""
  qr(input, some=True, *, out=None) -> (Tensor, Tensor)
  
  Computes the QR decomposition of a matrix or a batch of matrices :attr:`input`,
  and returns a namedtuple (Q, R) of tensors such that :math:`\text{input} = Q R`
  with :math:`Q` being an orthogonal matrix or batch of orthogonal matrices and
  :math:`R` being an upper triangular matrix or batch of upper triangular matrices.
  
  If :attr:`some` is ``True``, then this function returns the thin (reduced) QR factorization.
  Otherwise, if :attr:`some` is ``False``, this function returns the complete QR factorization.
  
  .. warning::
  
      :func:`torch.qr` is deprecated in favor of :func:`torch.linalg.qr`
      and will be removed in a future PyTorch release. The boolean parameter :attr:`some` has been
      replaced with a string parameter :attr:`mode`.
  
      ``Q, R = torch.qr(A)`` should be replaced with
  
      .. code:: python
  
          Q, R = torch.linalg.qr(A)
  
      ``Q, R = torch.qr(A, some=False)`` should be replaced with
  
      .. code:: python
  
          Q, R = torch.linalg.qr(A, mode="complete")
  
  .. warning::
            If you plan to backpropagate through QR, note that the current backward implementation
            is only well-defined when the first :math:`\min(input.size(-1), input.size(-2))`
            columns of :attr:`input` are linearly independent.
            This behavior will propably change once QR supports pivoting.
  
  .. note:: This function uses LAPACK for CPU inputs and MAGMA for CUDA inputs,
            and may produce different (valid) decompositions on different device types
            or different platforms.
  
  Args:
      input (Tensor): the input tensor of size :math:`(*, m, n)` where `*` is zero or more
                  batch dimensions consisting of matrices of dimension :math:`m \times n`.
      some (bool, optional): Set to ``True`` for reduced QR decomposition and ``False`` for
                  complete QR decomposition. If `k = min(m, n)` then:
  
                    * ``some=True`` : returns `(Q, R)` with dimensions (m, k), (k, n) (default)
  
                    * ``'some=False'``: returns `(Q, R)` with dimensions (m, m), (m, n)
  
  Keyword args:
      out (tuple, optional): tuple of `Q` and `R` tensors.
                  The dimensions of `Q` and `R` are detailed in the description of :attr:`some` above.
  
  Example::
  
      >>> a = torch.tensor([[12., -51, 4], [6, 167, -68], [-4, 24, -41]])
      >>> q, r = torch.qr(a)
      >>> q
      tensor([[-0.8571,  0.3943,  0.3314],
              [-0.4286, -0.9029, -0.0343],
              [ 0.2857, -0.1714,  0.9429]])
      >>> r
      tensor([[ -14.0000,  -21.0000,   14.0000],
              [   0.0000, -175.0000,   70.0000],
              [   0.0000,    0.0000,  -35.0000]])
      >>> torch.mm(q, r).round()
      tensor([[  12.,  -51.,    4.],
              [   6.,  167.,  -68.],
              [  -4.,   24.,  -41.]])
      >>> torch.mm(q.t(), q).round()
      tensor([[ 1.,  0.,  0.],
              [ 0.,  1., -0.],
              [ 0., -0.,  1.]])
      >>> a = torch.randn(3, 4, 5)
      >>> q, r = torch.qr(a, some=False)
      >>> torch.allclose(torch.matmul(q, r), a)
      True
      >>> torch.allclose(torch.matmul(q.transpose(-2, -1), q), torch.eye(5))
      True
  """)
  
  add_docstr(torch.rad2deg,
             r"""
  rad2deg(input, *, out=None) -> Tensor
  
  Returns a new tensor with each of the elements of :attr:`input`
  converted from angles in radians to degrees.
  
  Args:
      {input}
  
  Keyword arguments:
      {out}
  
  Example::
  
      >>> a = torch.tensor([[3.142, -3.142], [6.283, -6.283], [1.570, -1.570]])
      >>> torch.rad2deg(a)
      tensor([[ 180.0233, -180.0233],
              [ 359.9894, -359.9894],
              [  89.9544,  -89.9544]])
  
  """.format(**common_args))
  
  add_docstr(torch.deg2rad,
             r"""
  deg2rad(input, *, out=None) -> Tensor
  
  Returns a new tensor with each of the elements of :attr:`input`
  converted from angles in degrees to radians.
  
  Args:
      {input}
  
  Keyword arguments:
      {out}
  
  Example::
  
      >>> a = torch.tensor([[180.0, -180.0], [360.0, -360.0], [90.0, -90.0]])
      >>> torch.deg2rad(a)
      tensor([[ 3.1416, -3.1416],
              [ 6.2832, -6.2832],
              [ 1.5708, -1.5708]])
  
  """.format(**common_args))
  
  add_docstr(torch.heaviside,
             r"""
  heaviside(input, values, *, out=None) -> Tensor
  
  Computes the Heaviside step function for each element in :attr:`input`.
  The Heaviside step function is defined as:
  
  .. math::
      \text{{heaviside}}(input, values) = \begin{cases}
          0, & \text{if input < 0}\\
          values, & \text{if input == 0}\\
          1, & \text{if input > 0}
      \end{cases}
  """ + r"""
  
  Args:
      {input}
      values (Tensor): The values to use where :attr:`input` is zero.
  
  Keyword arguments:
      {out}
  
  Example::
  
      >>> input = torch.tensor([-1.5, 0, 2.0])
      >>> values = torch.tensor([0.5])
      >>> torch.heaviside(input, values)
      tensor([0.0000, 0.5000, 1.0000])
      >>> values = torch.tensor([1.2, -2.0, 3.5])
      >>> torch.heaviside(input, values)
      tensor([0., -2., 1.])
  
  """.format(**common_args))
  
  add_docstr(torch.rand,
             r"""
  rand(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
  
  Returns a tensor filled with random numbers from a uniform distribution
  on the interval :math:`[0, 1)`
  
  The shape of the tensor is defined by the variable argument :attr:`size`.
  
  Args:
      size (int...): a sequence of integers defining the shape of the output tensor.
          Can be a variable number of arguments or a collection like a list or tuple.
  
  Keyword args:
      {generator}
      {out}
      {dtype}
      {layout}
      {device}
      {requires_grad}
  
  Example::
  
      >>> torch.rand(4)
      tensor([ 0.5204,  0.2503,  0.3525,  0.5673])
      >>> torch.rand(2, 3)
      tensor([[ 0.8237,  0.5781,  0.6879],
              [ 0.3816,  0.7249,  0.0998]])
  """.format(**factory_common_args))
  
  add_docstr(torch.rand_like,
             r"""
  rand_like(input, *, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor
  
  Returns a tensor with the same size as :attr:`input` that is filled with
  random numbers from a uniform distribution on the interval :math:`[0, 1)`.
  ``torch.rand_like(input)`` is equivalent to
  ``torch.rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``.
  
  Args:
      {input}
  
  Keyword args:
      {dtype}
      {layout}
      {device}
      {requires_grad}
      {memory_format}
  
  """.format(**factory_like_common_args))
  
  add_docstr(torch.randint,
             """
  randint(low=0, high, size, \\*, generator=None, out=None, \
  dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
  
  Returns a tensor filled with random integers generated uniformly
  between :attr:`low` (inclusive) and :attr:`high` (exclusive).
  
  The shape of the tensor is defined by the variable argument :attr:`size`.
  
  .. note::
      With the global dtype default (``torch.float32``), this function returns
      a tensor with dtype ``torch.int64``.
  
  Args:
      low (int, optional): Lowest integer to be drawn from the distribution. Default: 0.
      high (int): One above the highest integer to be drawn from the distribution.
      size (tuple): a tuple defining the shape of the output tensor.
  
  Keyword args:
      {generator}
      {out}
      dtype (`torch.dtype`, optional) - the desired data type of returned tensor. Default: if ``None``,
          this function returns a tensor with dtype ``torch.int64``.
      {layout}
      {device}
      {requires_grad}
  
  Example::
  
      >>> torch.randint(3, 5, (3,))
      tensor([4, 3, 4])
  
  
      >>> torch.randint(10, (2, 2))
      tensor([[0, 2],
              [5, 5]])
  
  
      >>> torch.randint(3, 10, (2, 2))
      tensor([[4, 5],
              [6, 7]])
  
  
  """.format(**factory_common_args))
  
  add_docstr(torch.randint_like,
             """
  randint_like(input, low=0, high, \\*, dtype=None, layout=torch.strided, device=None, requires_grad=False, \
  memory_format=torch.preserve_format) -> Tensor
  
  Returns a tensor with the same shape as Tensor :attr:`input` filled with
  random integers generated uniformly between :attr:`low` (inclusive) and
  :attr:`high` (exclusive).
  
  .. note:
      With the global dtype default (``torch.float32``), this function returns
      a tensor with dtype ``torch.int64``.
  
  Args:
      {input}
      low (int, optional): Lowest integer to be drawn from the distribution. Default: 0.
      high (int): One above the highest integer to be drawn from the distribution.
  
  Keyword args:
      {dtype}
      {layout}
      {device}
      {requires_grad}
      {memory_format}
  
  """.format(**factory_like_common_args))
  
  add_docstr(torch.randn,
             r"""
  randn(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
  
  Returns a tensor filled with random numbers from a normal distribution
  with mean `0` and variance `1` (also called the standard normal
  distribution).
  
  .. math::
      \text{{out}}_{{i}} \sim \mathcal{{N}}(0, 1)
  
  The shape of the tensor is defined by the variable argument :attr:`size`.
  
  Args:
      size (int...): a sequence of integers defining the shape of the output tensor.
          Can be a variable number of arguments or a collection like a list or tuple.
  
  Keyword args:
      {generator}
      {out}
      {dtype}
      {layout}
      {device}
      {requires_grad}
  
  Example::
  
      >>> torch.randn(4)
      tensor([-2.1436,  0.9966,  2.3426, -0.6366])
      >>> torch.randn(2, 3)
      tensor([[ 1.5954,  2.8929, -1.0923],
              [ 1.1719, -0.4709, -0.1996]])
  """.format(**factory_common_args))
  
  add_docstr(torch.randn_like,
             r"""
  randn_like(input, *, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor
  
  Returns a tensor with the same size as :attr:`input` that is filled with
  random numbers from a normal distribution with mean 0 and variance 1.
  ``torch.randn_like(input)`` is equivalent to
  ``torch.randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``.
  
  Args:
      {input}
  
  Keyword args:
      {dtype}
      {layout}
      {device}
      {requires_grad}
      {memory_format}
  
  """.format(**factory_like_common_args))
  
  add_docstr(torch.randperm,
             """
  randperm(n, *, generator=None, out=None, dtype=torch.int64,layout=torch.strided, \
  device=None, requires_grad=False, pin_memory=False) -> Tensor
  """ + r"""
  Returns a random permutation of integers from ``0`` to ``n - 1``.
  
  Args:
      n (int): the upper bound (exclusive)
  
  Keyword args:
      {generator}
      {out}
      dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
          Default: ``torch.int64``.
      {layout}
      {device}
      {requires_grad}
      {pin_memory}
  
  Example::
  
      >>> torch.randperm(4)
      tensor([2, 1, 0, 3])
  """.format(**factory_common_args))
  
  add_docstr(torch.tensor,
             r"""
  tensor(data, *, dtype=None, device=None, requires_grad=False, pin_memory=False) -> Tensor
  
  Constructs a tensor with :attr:`data`.
  
  .. warning::
  
      :func:`torch.tensor` always copies :attr:`data`. If you have a Tensor
      ``data`` and want to avoid a copy, use :func:`torch.Tensor.requires_grad_`
      or :func:`torch.Tensor.detach`.
      If you have a NumPy ``ndarray`` and want to avoid a copy, use
      :func:`torch.as_tensor`.
  
  .. warning::
  
      When data is a tensor `x`, :func:`torch.tensor` reads out 'the data' from whatever it is passed,
      and constructs a leaf variable. Therefore ``torch.tensor(x)`` is equivalent to ``x.clone().detach()``
      and ``torch.tensor(x, requires_grad=True)`` is equivalent to ``x.clone().detach().requires_grad_(True)``.
      The equivalents using ``clone()`` and ``detach()`` are recommended.
  
  Args:
      {data}
  
  Keyword args:
      {dtype}
      {device}
      {requires_grad}
      {pin_memory}
  
  
  Example::
  
      >>> torch.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]])
      tensor([[ 0.1000,  1.2000],
              [ 2.2000,  3.1000],
              [ 4.9000,  5.2000]])
  
      >>> torch.tensor([0, 1])  # Type inference on data
      tensor([ 0,  1])
  
      >>> torch.tensor([[0.11111, 0.222222, 0.3333333]],
      ...              dtype=torch.float64,
      ...              device=torch.device('cuda:0'))  # creates a torch.cuda.DoubleTensor
      tensor([[ 0.1111,  0.2222,  0.3333]], dtype=torch.float64, device='cuda:0')
  
      >>> torch.tensor(3.14159)  # Create a scalar (zero-dimensional tensor)
      tensor(3.1416)
  
      >>> torch.tensor([])  # Create an empty tensor (of size (0,))
      tensor([])
  """.format(**factory_data_common_args))
  
  add_docstr(torch.range,
             r"""
  range(start=0, end, step=1, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
  
  Returns a 1-D tensor of size :math:`\left\lfloor \frac{\text{end} - \text{start}}{\text{step}} \right\rfloor + 1`
  with values from :attr:`start` to :attr:`end` with step :attr:`step`. Step is
  the gap between two values in the tensor.
  
  .. math::
      \text{out}_{i+1} = \text{out}_i + \text{step}.
  """ + r"""
  .. warning::
      This function is deprecated and will be removed in a future release because its behavior is inconsistent with
      Python's range builtin. Instead, use :func:`torch.arange`, which produces values in [start, end).
  
  Args:
      start (float): the starting value for the set of points. Default: ``0``.
      end (float): the ending value for the set of points
      step (float): the gap between each pair of adjacent points. Default: ``1``.
  
  Keyword args:
      {out}
      {dtype} If `dtype` is not given, infer the data type from the other input
          arguments. If any of `start`, `end`, or `stop` are floating-point, the
          `dtype` is inferred to be the default dtype, see
          :meth:`~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to
          be `torch.int64`.
      {layout}
      {device}
      {requires_grad}
  
  Example::
  
      >>> torch.range(1, 4)
      tensor([ 1.,  2.,  3.,  4.])
      >>> torch.range(1, 4, 0.5)
      tensor([ 1.0000,  1.5000,  2.0000,  2.5000,  3.0000,  3.5000,  4.0000])
  """.format(**factory_common_args))
  
  add_docstr(torch.arange,
             r"""
  arange(start=0, end, step=1, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
  
  Returns a 1-D tensor of size :math:`\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil`
  with values from the interval ``[start, end)`` taken with common difference
  :attr:`step` beginning from `start`.
  
  Note that non-integer :attr:`step` is subject to floating point rounding errors when
  comparing against :attr:`end`; to avoid inconsistency, we advise adding a small epsilon to :attr:`end`
  in such cases.
  
  .. math::
      \text{out}_{{i+1}} = \text{out}_{i} + \text{step}
  """ + r"""
  Args:
      start (Number): the starting value for the set of points. Default: ``0``.
      end (Number): the ending value for the set of points
      step (Number): the gap between each pair of adjacent points. Default: ``1``.
  
  Keyword args:
      {out}
      {dtype} If `dtype` is not given, infer the data type from the other input
          arguments. If any of `start`, `end`, or `stop` are floating-point, the
          `dtype` is inferred to be the default dtype, see
          :meth:`~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to
          be `torch.int64`.
      {layout}
      {device}
      {requires_grad}
  
  Example::
  
      >>> torch.arange(5)
      tensor([ 0,  1,  2,  3,  4])
      >>> torch.arange(1, 4)
      tensor([ 1,  2,  3])
      >>> torch.arange(1, 2.5, 0.5)
      tensor([ 1.0000,  1.5000,  2.0000])
  """.format(**factory_common_args))
  
  add_docstr(torch.ravel,
             r"""
  ravel(input) -> Tensor
  
  Return a contiguous flattened tensor. A copy is made only if needed.
  
  Args:
      {input}
  
  Example::
  
      >>> t = torch.tensor([[[1, 2],
      ...                    [3, 4]],
      ...                   [[5, 6],
      ...                    [7, 8]]])
      >>> torch.ravel(t)
      tensor([1, 2, 3, 4, 5, 6, 7, 8])
  """.format(**common_args))
  
  add_docstr(torch.remainder,
             r"""
  remainder(input, other, *, out=None) -> Tensor
  
  Like :func:`torch.fmod` this applies C++'s `std::fmod <https://en.cppreference.com/w/cpp/numeric/math/fmod>`_
  for floating point tensors and the modulus operation for integer tensors.
  Unlike :func:`torch.fmod`, however, if the sign of the modulus is different
  than the sign of the divisor :attr:`other` then the divisor is added to the modulus.
  
  Supports :ref:`broadcasting to a common shape <broadcasting-semantics>`,
  :ref:`type promotion <type-promotion-doc>`, and integer and float inputs.
  
  .. note::
      Complex inputs are not supported. In some cases, it is not mathematically
      possible to satisfy the definition of a modulo operation with complex numbers.
      See :func:`torch.fmod` for how division by zero is handled.
  
  .. note::
      This op, like NumPy's `remainder <https://numpy.org/doc/stable/reference/generated/numpy.remainder.html>`_,
      is equivalent to Python's modulus operation, and different from Python's
      `math.remainder <https://docs.python.org/dev/library/math.html#math.remainder>`_ and
      C++'s `std::remainder <https://en.cppreference.com/w/cpp/numeric/math/remainder>`_ which implement
      the IEEE remainder.
  
  Args:
      input (Tensor or Scalar): the dividend
      other (Tensor or Scalar): the divisor
  
  Keyword args:
      {out}
  
  Example::
  
      >>> torch.remainder(torch.tensor([-3., -2, -1, 1, 2, 3]), 2)
      tensor([ 1.,  0.,  1.,  1.,  0.,  1.])
      >>> torch.remainder(torch.tensor([1, 2, 3, 4, 5]), -1.5)
      tensor([ -0.5000, -1.0000,  0.0000, -0.5000, -1.0000 ])
  
  .. seealso::
  
      :func:`torch.fmod` which just computes the modulus for integer inputs and
      applies C++'s `std::fmod <https://en.cppreference.com/w/cpp/numeric/math/fmod>`_
      for floating point inputs.
  """.format(**common_args))
  
  add_docstr(torch.renorm,
             r"""
  renorm(input, p, dim, maxnorm, *, out=None) -> Tensor
  
  Returns a tensor where each sub-tensor of :attr:`input` along dimension
  :attr:`dim` is normalized such that the `p`-norm of the sub-tensor is lower
  than the value :attr:`maxnorm`
  
  .. note:: If the norm of a row is lower than `maxnorm`, the row is unchanged
  
  Args:
      {input}
      p (float): the power for the norm computation
      dim (int): the dimension to slice over to get the sub-tensors
      maxnorm (float): the maximum norm to keep each sub-tensor under
  
  Keyword args:
      {out}
  
  Example::
  
      >>> x = torch.ones(3, 3)
      >>> x[1].fill_(2)
      tensor([ 2.,  2.,  2.])
      >>> x[2].fill_(3)
      tensor([ 3.,  3.,  3.])
      >>> x
      tensor([[ 1.,  1.,  1.],
              [ 2.,  2.,  2.],
              [ 3.,  3.,  3.]])
      >>> torch.renorm(x, 1, 0, 5)
      tensor([[ 1.0000,  1.0000,  1.0000],
              [ 1.6667,  1.6667,  1.6667],
              [ 1.6667,  1.6667,  1.6667]])
  """.format(**common_args))
  
  add_docstr(torch.reshape,
             r"""
  reshape(input, shape) -> Tensor
  
  Returns a tensor with the same data and number of elements as :attr:`input`,
  but with the specified shape. When possible, the returned tensor will be a view
  of :attr:`input`. Otherwise, it will be a copy. Contiguous inputs and inputs
  with compatible strides can be reshaped without copying, but you should not
  depend on the copying vs. viewing behavior.
  
  See :meth:`torch.Tensor.view` on when it is possible to return a view.
  
  A single dimension may be -1, in which case it's inferred from the remaining
  dimensions and the number of elements in :attr:`input`.
  
  Args:
      input (Tensor): the tensor to be reshaped
      shape (tuple of ints): the new shape
  
  Example::
  
      >>> a = torch.arange(4.)
      >>> torch.reshape(a, (2, 2))
      tensor([[ 0.,  1.],
              [ 2.,  3.]])
      >>> b = torch.tensor([[0, 1], [2, 3]])
      >>> torch.reshape(b, (-1,))
      tensor([ 0,  1,  2,  3])
  """)
  
  
  add_docstr(torch.result_type,
             r"""
  result_type(tensor1, tensor2) -> dtype
  
  Returns the :class:`torch.dtype` that would result from performing an arithmetic
  operation on the provided input tensors. See type promotion :ref:`documentation <type-promotion-doc>`
  for more information on the type promotion logic.
  
  Args:
      tensor1 (Tensor or Number): an input tensor or number
      tensor2 (Tensor or Number): an input tensor or number
  
  Example::
  
      >>> torch.result_type(torch.tensor([1, 2], dtype=torch.int), 1.0)
      torch.float32
      >>> torch.result_type(torch.tensor([1, 2], dtype=torch.uint8), torch.tensor(1))
      torch.uint8
  """)
  
  add_docstr(torch.row_stack,
             r"""
  row_stack(tensors, *, out=None) -> Tensor
  
  Alias of :func:`torch.vstack`.
  """)
  
  add_docstr(torch.round,
             r"""
  round(input, *, out=None) -> Tensor
  
  Returns a new tensor with each of the elements of :attr:`input` rounded
  to the closest integer.
  
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([ 0.9920,  0.6077,  0.9734, -1.0362])
      >>> torch.round(a)
      tensor([ 1.,  1.,  1., -1.])
  """.format(**common_args))
  
  add_docstr(torch.rsqrt,
             r"""
  rsqrt(input, *, out=None) -> Tensor
  
  Returns a new tensor with the reciprocal of the square-root of each of
  the elements of :attr:`input`.
  
  .. math::
      \text{out}_{i} = \frac{1}{\sqrt{\text{input}_{i}}}
  """ + r"""
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([-0.0370,  0.2970,  1.5420, -0.9105])
      >>> torch.rsqrt(a)
      tensor([    nan,  1.8351,  0.8053,     nan])
  """.format(**common_args))
  
  add_docstr(torch.scatter,
             r"""
  scatter(input, dim, index, src) -> Tensor
  
  Out-of-place version of :meth:`torch.Tensor.scatter_`
  """)
  
  add_docstr(torch.scatter_add,
             r"""
  scatter_add(input, dim, index, src) -> Tensor
  
  Out-of-place version of :meth:`torch.Tensor.scatter_add_`
  """)
  
  add_docstr(torch.set_flush_denormal,
             r"""
  set_flush_denormal(mode) -> bool
  
  Disables denormal floating numbers on CPU.
  
  Returns ``True`` if your system supports flushing denormal numbers and it
  successfully configures flush denormal mode.  :meth:`~torch.set_flush_denormal`
  is only supported on x86 architectures supporting SSE3.
  
  Args:
      mode (bool): Controls whether to enable flush denormal mode or not
  
  Example::
  
      >>> torch.set_flush_denormal(True)
      True
      >>> torch.tensor([1e-323], dtype=torch.float64)
      tensor([ 0.], dtype=torch.float64)
      >>> torch.set_flush_denormal(False)
      True
      >>> torch.tensor([1e-323], dtype=torch.float64)
      tensor(9.88131e-324 *
             [ 1.0000], dtype=torch.float64)
  """)
  
  add_docstr(torch.set_num_threads, r"""
  set_num_threads(int)
  
  Sets the number of threads used for intraop parallelism on CPU.
  
  .. warning::
      To ensure that the correct number of threads is used, set_num_threads
      must be called before running eager, JIT or autograd code.
  """)
  
  add_docstr(torch.set_num_interop_threads, r"""
  set_num_interop_threads(int)
  
  Sets the number of threads used for interop parallelism
  (e.g. in JIT interpreter) on CPU.
  
  .. warning::
      Can only be called once and before any inter-op parallel work
      is started (e.g. JIT execution).
  """)
  
  add_docstr(torch.sigmoid, r"""
  sigmoid(input, *, out=None) -> Tensor
  
  Alias for :func:`torch.special.expit`.
  """)
  
  add_docstr(torch.logit,
             r"""
  logit(input, eps=None, *, out=None) -> Tensor
  
  Alias for :func:`torch.special.logit`.
  """)
  
  add_docstr(torch.sign,
             r"""
  sign(input, *, out=None) -> Tensor
  
  Returns a new tensor with the signs of the elements of :attr:`input`.
  
  .. math::
      \text{out}_{i} = \operatorname{sgn}(\text{input}_{i})
  """ + r"""
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.tensor([0.7, -1.2, 0., 2.3])
      >>> a
      tensor([ 0.7000, -1.2000,  0.0000,  2.3000])
      >>> torch.sign(a)
      tensor([ 1., -1.,  0.,  1.])
  """.format(**common_args))
  
  add_docstr(torch.signbit,
             r"""
  signbit(input, *, out=None) -> Tensor
  
  Tests if each element of :attr:`input` has its sign bit set (is less than zero) or not.
  
  Args:
    {input}
  
  Keyword args:
    {out}
  
  Example::
  
      >>> a = torch.tensor([0.7, -1.2, 0., 2.3])
      >>> torch.signbit(a)
      tensor([ False, True,  False,  False])
  """.format(**common_args))
  
  add_docstr(torch.sgn,
             r"""
  sgn(input, *, out=None) -> Tensor
  
  This function is an extension of torch.sign() to complex tensors.
  It computes a new tensor whose elements have
  the same angles as the corresponding elements of :attr:`input` and
  absolute values (i.e. magnitudes) of one for complex tensors and
  is equivalent to torch.sign() for non-complex tensors.
  
  .. math::
      \text{out}_{i} = \begin{cases}
                      0 & |\text{{input}}_i| == 0 \\
                      \frac{{\text{{input}}_i}}{|{\text{{input}}_i}|} & \text{otherwise}
                      \end{cases}
  
  """ + r"""
  Args:
      {input}
  
  Keyword args:
    {out}
  
  Example::
  
      >>> t = torch.tensor([3+4j, 7-24j, 0, 1+2j])
      >>> t.sgn()
      tensor([0.6000+0.8000j, 0.2800-0.9600j, 0.0000+0.0000j, 0.4472+0.8944j])
  """.format(**common_args))
  
  add_docstr(torch.sin,
             r"""
  sin(input, *, out=None) -> Tensor
  
  Returns a new tensor with the sine of the elements of :attr:`input`.
  
  .. math::
      \text{out}_{i} = \sin(\text{input}_{i})
  """ + r"""
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([-0.5461,  0.1347, -2.7266, -0.2746])
      >>> torch.sin(a)
      tensor([-0.5194,  0.1343, -0.4032, -0.2711])
  """.format(**common_args))
  
  add_docstr(torch.sinc,
             r"""
  sinc(input, *, out=None) -> Tensor
  
  Alias for :func:`torch.special.sinc`.
  """)
  
  add_docstr(torch.sinh,
             r"""
  sinh(input, *, out=None) -> Tensor
  
  Returns a new tensor with the hyperbolic sine of the elements of
  :attr:`input`.
  
  .. math::
      \text{out}_{i} = \sinh(\text{input}_{i})
  """ + r"""
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([ 0.5380, -0.8632, -0.1265,  0.9399])
      >>> torch.sinh(a)
      tensor([ 0.5644, -0.9744, -0.1268,  1.0845])
  
  .. note::
     When :attr:`input` is on the CPU, the implementation of torch.sinh may use
     the Sleef library, which rounds very large results to infinity or negative
     infinity. See `here <https://sleef.org/purec.xhtml>`_ for details.
  """.format(**common_args))
  
  add_docstr(torch.sort,
             r"""
  sort(input, dim=-1, descending=False, stable=False, *, out=None) -> (Tensor, LongTensor)
  
  Sorts the elements of the :attr:`input` tensor along a given dimension
  in ascending order by value.
  
  If :attr:`dim` is not given, the last dimension of the `input` is chosen.
  
  If :attr:`descending` is ``True`` then the elements are sorted in descending
  order by value.
  
  If :attr:`stable` is ``True`` then the sorting routine becomes stable, preserving
  the order of equivalent elements.
  
  A namedtuple of (values, indices) is returned, where the `values` are the
  sorted values and `indices` are the indices of the elements in the original
  `input` tensor.
  
  Args:
      {input}
      dim (int, optional): the dimension to sort along
      descending (bool, optional): controls the sorting order (ascending or descending)
      stable (bool, optional): makes the sorting routine stable, which guarantees that the order
         of equivalent elements is preserved.
  
  Keyword args:
      out (tuple, optional): the output tuple of (`Tensor`, `LongTensor`) that can
          be optionally given to be used as output buffers
  
  Example::
  
      >>> x = torch.randn(3, 4)
      >>> sorted, indices = torch.sort(x)
      >>> sorted
      tensor([[-0.2162,  0.0608,  0.6719,  2.3332],
              [-0.5793,  0.0061,  0.6058,  0.9497],
              [-0.5071,  0.3343,  0.9553,  1.0960]])
      >>> indices
      tensor([[ 1,  0,  2,  3],
              [ 3,  1,  0,  2],
              [ 0,  3,  1,  2]])
  
      >>> sorted, indices = torch.sort(x, 0)
      >>> sorted
      tensor([[-0.5071, -0.2162,  0.6719, -0.5793],
              [ 0.0608,  0.0061,  0.9497,  0.3343],
              [ 0.6058,  0.9553,  1.0960,  2.3332]])
      >>> indices
      tensor([[ 2,  0,  0,  1],
              [ 0,  1,  1,  2],
              [ 1,  2,  2,  0]])
      >>> x = torch.tensor([0, 1] * 9)
      >>> x.sort()
      torch.return_types.sort(
          values=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]),
          indices=tensor([ 2, 16,  4,  6, 14,  8,  0, 10, 12,  9, 17, 15, 13, 11,  7,  5,  3,  1]))
      >>> x.sort(stable=True)
      torch.return_types.sort(
          values=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]),
          indices=tensor([ 0,  2,  4,  6,  8, 10, 12, 14, 16,  1,  3,  5,  7,  9, 11, 13, 15, 17]))
  """.format(**common_args))
  
  add_docstr(torch.argsort,
             r"""
  argsort(input, dim=-1, descending=False) -> LongTensor
  
  Returns the indices that sort a tensor along a given dimension in ascending
  order by value.
  
  This is the second value returned by :meth:`torch.sort`.  See its documentation
  for the exact semantics of this method.
  
  Args:
      {input}
      dim (int, optional): the dimension to sort along
      descending (bool, optional): controls the sorting order (ascending or descending)
  
  Example::
  
      >>> a = torch.randn(4, 4)
      >>> a
      tensor([[ 0.0785,  1.5267, -0.8521,  0.4065],
              [ 0.1598,  0.0788, -0.0745, -1.2700],
              [ 1.2208,  1.0722, -0.7064,  1.2564],
              [ 0.0669, -0.2318, -0.8229, -0.9280]])
  
  
      >>> torch.argsort(a, dim=1)
      tensor([[2, 0, 3, 1],
              [3, 2, 1, 0],
              [2, 1, 0, 3],
              [3, 2, 1, 0]])
  """.format(**common_args))
  
  add_docstr(torch.msort,
             r"""
  msort(input, *, out=None) -> Tensor
  
  Sorts the elements of the :attr:`input` tensor along its first dimension
  in ascending order by value.
  
  .. note:: `torch.msort(t)` is equivalent to `torch.sort(t, dim=0)[0]`.
            See also :func:`torch.sort`.
  
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> t = torch.randn(3, 4)
      >>> t
      tensor([[-0.1321,  0.4370, -1.2631, -1.1289],
              [-2.0527, -1.1250,  0.2275,  0.3077],
              [-0.0881, -0.1259, -0.5495,  1.0284]])
      >>> torch.msort(t)
      tensor([[-2.0527, -1.1250, -1.2631, -1.1289],
              [-0.1321, -0.1259, -0.5495,  0.3077],
              [-0.0881,  0.4370,  0.2275,  1.0284]])
  """.format(**common_args))
  
  add_docstr(torch.sparse_csr_tensor,
             r"""
  sparse_csr_tensor(crow_indices, col_indices, values, size=None, *, dtype=None, device=None, requires_grad=False) -> Tensor
  
  Constructs a :ref:`sparse tensor in CSR (Compressed Sparse Row) <sparse-csr-docs>` with specified
  values at the given :attr:`crow_indices` and :attr:`col_indices`. Sparse matrix multiplication operations
  in CSR format are typically faster than that for sparse tensors in COO format. Make you have a look
  at :ref:`the note on the data type of the indices <sparse-csr-docs>`.
  
  Args:
      crow_indices (array_like): One-dimensional array of size size[0] + 1. The last element
          is the number of non-zeros. This tensor encodes the index in values and col_indices
          depending on where the given row starts. Each successive number in the tensor
          subtracted by the number before it denotes the number of elements in a given row.
      col_indices (array_like): Column co-ordinates of each element in values. Strictly one
          dimensional tensor with the same length as values.
      values (array_list): Initial values for the tensor. Can be a list, tuple, NumPy ``ndarray``, scalar,
          and other types.
      size (list, tuple, :class:`torch.Size`, optional): Size of the sparse tensor. If not provided, the
          size will be inferred as the minimum size big enough to hold all non-zero elements.
  
  Keyword args:
      dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
          Default: if None, infers data type from :attr:`values`.
      device (:class:`torch.device`, optional): the desired device of returned tensor.
          Default: if None, uses the current device for the default tensor type
          (see :func:`torch.set_default_tensor_type`). :attr:`device` will be the CPU
          for CPU tensor types and the current CUDA device for CUDA tensor types.
      {requires_grad}
  
  Example ::
      >>> crow_indices = [0, 2, 4]
      >>> col_indices = [0, 1, 0, 1]
      >>> values = [1, 2, 3, 4]
      >>> torch.sparse_csr_tensor(torch.tensor(crow_indices, dtype=torch.int64),
      ...                         torch.tensor(col_indices, dtype=torch.int64),
      ...                         torch.tensor(values), dtype=torch.double)
      tensor(crow_indices=tensor([0, 2, 4]),
             col_indices=tensor([0, 1, 0, 1]),
             values=tensor([1., 2., 3., 4.]), size=(2, 2), nnz=4,
             dtype=torch.float64, layout=torch.sparse_csr)
  """.format(**factory_common_args))
  
  add_docstr(torch.sparse_coo_tensor,
             r"""
  sparse_coo_tensor(indices, values, size=None, *, dtype=None, device=None, requires_grad=False) -> Tensor
  
  Constructs a :ref:`sparse tensor in COO(rdinate) format
  <sparse-coo-docs>` with specified values at the given
  :attr:`indices`.
  
  .. note::
  
     This function returns an :ref:`uncoalesced tensor <sparse-uncoalesced-coo-docs>`.
  
  Args:
      indices (array_like): Initial data for the tensor. Can be a list, tuple,
          NumPy ``ndarray``, scalar, and other types. Will be cast to a :class:`torch.LongTensor`
          internally. The indices are the coordinates of the non-zero values in the matrix, and thus
          should be two-dimensional where the first dimension is the number of tensor dimensions and
          the second dimension is the number of non-zero values.
      values (array_like): Initial values for the tensor. Can be a list, tuple,
          NumPy ``ndarray``, scalar, and other types.
      size (list, tuple, or :class:`torch.Size`, optional): Size of the sparse tensor. If not
          provided the size will be inferred as the minimum size big enough to hold all non-zero
          elements.
  
  Keyword args:
      dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
          Default: if None, infers data type from :attr:`values`.
      device (:class:`torch.device`, optional): the desired device of returned tensor.
          Default: if None, uses the current device for the default tensor type
          (see :func:`torch.set_default_tensor_type`). :attr:`device` will be the CPU
          for CPU tensor types and the current CUDA device for CUDA tensor types.
      {requires_grad}
  
  
  Example::
  
      >>> i = torch.tensor([[0, 1, 1],
      ...                   [2, 0, 2]])
      >>> v = torch.tensor([3, 4, 5], dtype=torch.float32)
      >>> torch.sparse_coo_tensor(i, v, [2, 4])
      tensor(indices=tensor([[0, 1, 1],
                             [2, 0, 2]]),
             values=tensor([3., 4., 5.]),
             size=(2, 4), nnz=3, layout=torch.sparse_coo)
  
      >>> torch.sparse_coo_tensor(i, v)  # Shape inference
      tensor(indices=tensor([[0, 1, 1],
                             [2, 0, 2]]),
             values=tensor([3., 4., 5.]),
             size=(2, 3), nnz=3, layout=torch.sparse_coo)
  
      >>> torch.sparse_coo_tensor(i, v, [2, 4],
      ...                         dtype=torch.float64,
      ...                         device=torch.device('cuda:0'))
      tensor(indices=tensor([[0, 1, 1],
                             [2, 0, 2]]),
             values=tensor([3., 4., 5.]),
             device='cuda:0', size=(2, 4), nnz=3, dtype=torch.float64,
             layout=torch.sparse_coo)
  
      # Create an empty sparse tensor with the following invariants:
      #   1. sparse_dim + dense_dim = len(SparseTensor.shape)
      #   2. SparseTensor._indices().shape = (sparse_dim, nnz)
      #   3. SparseTensor._values().shape = (nnz, SparseTensor.shape[sparse_dim:])
      #
      # For instance, to create an empty sparse tensor with nnz = 0, dense_dim = 0 and
      # sparse_dim = 1 (hence indices is a 2D tensor of shape = (1, 0))
      >>> S = torch.sparse_coo_tensor(torch.empty([1, 0]), [], [1])
      tensor(indices=tensor([], size=(1, 0)),
             values=tensor([], size=(0,)),
             size=(1,), nnz=0, layout=torch.sparse_coo)
  
      # and to create an empty sparse tensor with nnz = 0, dense_dim = 1 and
      # sparse_dim = 1
      >>> S = torch.sparse_coo_tensor(torch.empty([1, 0]), torch.empty([0, 2]), [1, 2])
      tensor(indices=tensor([], size=(1, 0)),
             values=tensor([], size=(0, 2)),
             size=(1, 2), nnz=0, layout=torch.sparse_coo)
  
  .. _torch.sparse: https://pytorch.org/docs/stable/sparse.html
  """.format(**factory_common_args))
  
  add_docstr(torch.sqrt,
             r"""
  sqrt(input, *, out=None) -> Tensor
  
  Returns a new tensor with the square-root of the elements of :attr:`input`.
  
  .. math::
      \text{out}_{i} = \sqrt{\text{input}_{i}}
  """ + r"""
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([-2.0755,  1.0226,  0.0831,  0.4806])
      >>> torch.sqrt(a)
      tensor([    nan,  1.0112,  0.2883,  0.6933])
  """.format(**common_args))
  
  add_docstr(torch.square,
             r"""
  square(input, *, out=None) -> Tensor
  
  Returns a new tensor with the square of the elements of :attr:`input`.
  
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([-2.0755,  1.0226,  0.0831,  0.4806])
      >>> torch.square(a)
      tensor([ 4.3077,  1.0457,  0.0069,  0.2310])
  """.format(**common_args))
  
  add_docstr(torch.squeeze,
             r"""
  squeeze(input, dim=None, *, out=None) -> Tensor
  
  Returns a tensor with all the dimensions of :attr:`input` of size `1` removed.
  
  For example, if `input` is of shape:
  :math:`(A \times 1 \times B \times C \times 1 \times D)` then the `out` tensor
  will be of shape: :math:`(A \times B \times C \times D)`.
  
  When :attr:`dim` is given, a squeeze operation is done only in the given
  dimension. If `input` is of shape: :math:`(A \times 1 \times B)`,
  ``squeeze(input, 0)`` leaves the tensor unchanged, but ``squeeze(input, 1)``
  will squeeze the tensor to the shape :math:`(A \times B)`.
  
  .. note:: The returned tensor shares the storage with the input tensor,
            so changing the contents of one will change the contents of the other.
  
  .. warning:: If the tensor has a batch dimension of size 1, then `squeeze(input)`
            will also remove the batch dimension, which can lead to unexpected
            errors.
  
  Args:
      {input}
      dim (int, optional): if given, the input will be squeezed only in
             this dimension
  
  Keyword args:
      {out}
  
  Example::
  
      >>> x = torch.zeros(2, 1, 2, 1, 2)
      >>> x.size()
      torch.Size([2, 1, 2, 1, 2])
      >>> y = torch.squeeze(x)
      >>> y.size()
      torch.Size([2, 2, 2])
      >>> y = torch.squeeze(x, 0)
      >>> y.size()
      torch.Size([2, 1, 2, 1, 2])
      >>> y = torch.squeeze(x, 1)
      >>> y.size()
      torch.Size([2, 2, 1, 2])
  """.format(**common_args))
  
  add_docstr(torch.std, r"""
  std(input, dim, unbiased, keepdim=False, *, out=None) -> Tensor
  
  If :attr:`unbiased` is ``True``, Bessel's correction will be used.
  Otherwise, the sample deviation is calculated, without any correction.
  
  Args:
      {input}
      {dim}
  
  Keyword args:
      unbiased (bool): whether to use Bessel's correction (:math:`\delta N = 1`).
      {keepdim}
      {out}
  
  
  .. function:: std(input, unbiased) -> Tensor
     :noindex:
  
  Calculates the standard deviation of all elements in the :attr:`input` tensor.
  
  If :attr:`unbiased` is ``True``, Bessel's correction will be used.
  Otherwise, the sample deviation is calculated, without any correction.
  
  Args:
      {input}
      unbiased (bool): whether to use Bessel's correction (:math:`\delta N = 1`).
  
  Example::
  
      >>> a = torch.tensor([[-0.8166, -1.3802, -0.3560]])
      >>> torch.std(a, unbiased=False)
      tensor(0.4188)
  """.format(**multi_dim_common))
  
  add_docstr(torch.std_mean,
             r"""
  std_mean(input, dim, unbiased, keepdim=False, *, out=None) -> (Tensor, Tensor)
  
  If :attr:`unbiased` is ``True``, Bessel's correction will be used to calculate
  the standard deviation. Otherwise, the sample deviation is calculated, without
  any correction.
  
  Args:
      {input}
      {dim}
  
  Keyword args:
      unbiased (bool): whether to use Bessel's correction (:math:`\delta N = 1`).
      {keepdim}
      {out}
  
  Returns:
      A tuple (std, mean) containing the standard deviation and mean.
  
  .. function:: std_mean(input, unbiased) -> (Tensor, Tensor)
     :noindex:
  
  Calculates the standard deviation and mean of all elements in the :attr:`input`
  tensor.
  
  If :attr:`unbiased` is ``True``, Bessel's correction will be used.
  Otherwise, the sample deviation is calculated, without any correction.
  
  Args:
      {input}
      unbiased (bool): whether to use Bessel's correction (:math:`\delta N = 1`).
  
  Returns:
      A tuple (std, mean) containing the standard deviation and mean.
  
  Example::
  
      >>> a = torch.tensor([[-0.8166, -1.3802, -0.3560]])
      >>> torch.std_mean(a, unbiased=False)
      (tensor(0.4188), tensor(-0.8509))
  """.format(**multi_dim_common))
  
  add_docstr(torch.sub, r"""
  sub(input, other, *, alpha=1, out=None) -> Tensor
  
  Subtracts :attr:`other`, scaled by :attr:`alpha`, from :attr:`input`.
  
  .. math::
      \text{{out}}_i = \text{{input}}_i - \text{{alpha}} \times \text{{other}}_i
  """ + r"""
  
  Supports :ref:`broadcasting to a common shape <broadcasting-semantics>`,
  :ref:`type promotion <type-promotion-doc>`, and integer, float, and complex inputs.
  
  Args:
      {input}
      other (Tensor or Number): the tensor or number to subtract from :attr:`input`.
  
  Keyword args:
      alpha (Number): the multiplier for :attr:`other`.
      {out}
  
  Example::
  
      >>> a = torch.tensor((1, 2))
      >>> b = torch.tensor((0, 1))
      >>> torch.sub(a, b, alpha=2)
      tensor([1, 0])
  """.format(**common_args))
  
  add_docstr(torch.subtract, r"""
  subtract(input, other, *, alpha=1, out=None) -> Tensor
  
  Alias for :func:`torch.sub`.
  """)
  
  add_docstr(torch.sum,
             r"""
  sum(input, *, dtype=None) -> Tensor
  
  Returns the sum of all elements in the :attr:`input` tensor.
  
  Args:
      {input}
  
  Keyword args:
      {dtype}
  
  Example::
  
      >>> a = torch.randn(1, 3)
      >>> a
      tensor([[ 0.1133, -0.9567,  0.2958]])
      >>> torch.sum(a)
      tensor(-0.5475)
  
  .. function:: sum(input, dim, keepdim=False, *, dtype=None) -> Tensor
     :noindex:
  
  Returns the sum of each row of the :attr:`input` tensor in the given
  dimension :attr:`dim`. If :attr:`dim` is a list of dimensions,
  reduce over all of them.
  
  {keepdim_details}
  
  Args:
      {input}
      {dim}
      {keepdim}
  
  Keyword args:
      {dtype}
  
  Example::
  
      >>> a = torch.randn(4, 4)
      >>> a
      tensor([[ 0.0569, -0.2475,  0.0737, -0.3429],
              [-0.2993,  0.9138,  0.9337, -1.6864],
              [ 0.1132,  0.7892, -0.1003,  0.5688],
              [ 0.3637, -0.9906, -0.4752, -1.5197]])
      >>> torch.sum(a, 1)
      tensor([-0.4598, -0.1381,  1.3708, -2.6217])
      >>> b = torch.arange(4 * 5 * 6).view(4, 5, 6)
      >>> torch.sum(b, (2, 1))
      tensor([  435.,  1335.,  2235.,  3135.])
  """.format(**multi_dim_common))
  
  add_docstr(torch.nansum,
             r"""
  nansum(input, *, dtype=None) -> Tensor
  
  Returns the sum of all elements, treating Not a Numbers (NaNs) as zero.
  
  Args:
      {input}
  
  Keyword args:
      {dtype}
  
  Example::
  
      >>> a = torch.tensor([1., 2., float('nan'), 4.])
      >>> torch.nansum(a)
      tensor(7.)
  
  .. function:: nansum(input, dim, keepdim=False, *, dtype=None) -> Tensor
     :noindex:
  
  Returns the sum of each row of the :attr:`input` tensor in the given
  dimension :attr:`dim`, treating Not a Numbers (NaNs) as zero.
  If :attr:`dim` is a list of dimensions, reduce over all of them.
  
  {keepdim_details}
  
  Args:
      {input}
      {dim}
      {keepdim}
  
  Keyword args:
      {dtype}
  
  Example::
  
      >>> torch.nansum(torch.tensor([1., float("nan")]))
      1.0
      >>> a = torch.tensor([[1, 2], [3., float("nan")]])
      >>> torch.nansum(a)
      tensor(6.)
      >>> torch.nansum(a, dim=0)
      tensor([4., 2.])
      >>> torch.nansum(a, dim=1)
      tensor([3., 3.])
  """.format(**multi_dim_common))
  
  add_docstr(torch.svd,
             r"""
  svd(input, some=True, compute_uv=True, *, out=None) -> (Tensor, Tensor, Tensor)
  
  Computes the singular value decomposition of either a matrix or batch of
  matrices :attr:`input`. The singular value decomposition is represented as a
  namedtuple `(U, S, V)`, such that :attr:`input` :math:`= U \text{diag}(S) V^{\text{H}}`.
  where :math:`V^{\text{H}}` is the transpose of `V` for real inputs,
  and the conjugate transpose of `V` for complex inputs.
  If :attr:`input` is a batch of matrices, then `U`, `S`, and `V` are also
  batched with the same batch dimensions as :attr:`input`.
  
  If :attr:`some` is `True` (default), the method returns the reduced singular
  value decomposition. In this case, if the last two dimensions of :attr:`input` are
  `m` and `n`, then the returned `U` and `V` matrices will contain only
  `min(n, m)` orthonormal columns.
  
  If :attr:`compute_uv` is `False`, the returned `U` and `V` will be
  zero-filled matrices of shape `(m, m)` and `(n, n)`
  respectively, and the same device as :attr:`input`. The argument :attr:`some`
  has no effect when :attr:`compute_uv` is `False`.
  
  Supports :attr:`input` of float, double, cfloat and cdouble data types.
  The dtypes of `U` and `V` are the same as :attr:`input`'s. `S` will
  always be real-valued, even if :attr:`input` is complex.
  
  .. warning::
  
      :func:`torch.svd` is deprecated in favor of :func:`torch.linalg.svd`
      and will be removed in a future PyTorch release.
  
      ``U, S, V = torch.svd(A, some=some, compute_uv=True)`` (default) should be replaced with
  
      .. code:: python
  
          U, S, Vh = torch.linalg.svd(A, full_matrices=not some)
          V = Vh.transpose(-2, -1).conj()
  
      ``_, S, _ = torch.svd(A, some=some, compute_uv=False)`` should be replaced with
  
      .. code:: python
  
          S = torch.svdvals(A)
  
  .. note:: Differences with :func:`torch.linalg.svd`:
  
               * :attr:`some` is the opposite of
                 :func:`torch.linalg.svd`'s :attr:`full_matrices`. Note that
                 default value for both is `True`, so the default behavior is
                 effectively the opposite.
               * :func:`torch.svd` returns `V`, whereas :func:`torch.linalg.svd` returns
                 `Vh`, that is, :math:`V^{\text{H}}`.
               * If :attr:`compute_uv` is `False`, :func:`torch.svd` returns zero-filled
                 tensors for `U` and `Vh`, whereas :func:`torch.linalg.svd` returns
                 empty tensors.
  
  .. note:: The singular values are returned in descending order. If :attr:`input` is a batch of matrices,
            then the singular values of each matrix in the batch are returned in descending order.
  
  .. note:: The `S` tensor can only be used to compute gradients if :attr:`compute_uv` is `True`.
  
  .. note:: When :attr:`some` is `False`, the gradients on `U[..., :, min(m, n):]`
            and `V[..., :, min(m, n):]` will be ignored in the backward pass, as those vectors
            can be arbitrary bases of the corresponding subspaces.
  
  .. note:: The implementation of :func:`torch.linalg.svd` on CPU uses LAPACK's routine `?gesdd`
            (a divide-and-conquer algorithm) instead of `?gesvd` for speed. Analogously,
            on GPU, it uses cuSOLVER's routines `gesvdj` and `gesvdjBatched` on CUDA 10.1.243
            and later, and MAGMA's routine `gesdd` on earlier versions of CUDA.
  
  .. note:: The returned `U` will not be contiguous. The matrix (or batch of matrices) will
            be represented as a column-major matrix (i.e. Fortran-contiguous).
  
  .. warning:: The gradients with respect to `U` and `V` will only be finite when the input does not
               have zero nor repeated singular values.
  
  .. warning:: If the distance between any two singular values is close to zero, the gradients with respect to
               `U` and `V` will be numerically unstable, as they depends on
               :math:`\frac{1}{\min_{i \neq j} \sigma_i^2 - \sigma_j^2}`. The same happens when the matrix
               has small singular values, as these gradients also depend on `S⁻¹`.
  
  .. warning:: For complex-valued :attr:`input` the singular value decomposition is not unique,
               as `U` and `V` may be multiplied by an arbitrary phase factor :math:`e^{i \phi}` on every column.
               The same happens when :attr:`input` has repeated singular values, where one may multiply
               the columns of the spanning subspace in `U` and `V` by a rotation matrix
               and `the resulting vectors will span the same subspace`_.
               Different platforms, like NumPy, or inputs on different device types,
               may produce different `U` and `V` tensors.
  
  Args:
      input (Tensor): the input tensor of size `(*, m, n)` where `*` is zero or more
                      batch dimensions consisting of `(m, n)` matrices.
      some (bool, optional): controls whether to compute the reduced or full decomposition, and
                             consequently, the shape of returned `U` and `V`. Default: `True`.
      compute_uv (bool, optional): controls whether to compute `U` and `V`. Default: `True`.
  
  Keyword args:
      out (tuple, optional): the output tuple of tensors
  
  Example::
  
      >>> a = torch.randn(5, 3)
      >>> a
      tensor([[ 0.2364, -0.7752,  0.6372],
              [ 1.7201,  0.7394, -0.0504],
              [-0.3371, -1.0584,  0.5296],
              [ 0.3550, -0.4022,  1.5569],
              [ 0.2445, -0.0158,  1.1414]])
      >>> u, s, v = torch.svd(a)
      >>> u
      tensor([[ 0.4027,  0.0287,  0.5434],
              [-0.1946,  0.8833,  0.3679],
              [ 0.4296, -0.2890,  0.5261],
              [ 0.6604,  0.2717, -0.2618],
              [ 0.4234,  0.2481, -0.4733]])
      >>> s
      tensor([2.3289, 2.0315, 0.7806])
      >>> v
      tensor([[-0.0199,  0.8766,  0.4809],
              [-0.5080,  0.4054, -0.7600],
              [ 0.8611,  0.2594, -0.4373]])
      >>> torch.dist(a, torch.mm(torch.mm(u, torch.diag(s)), v.t()))
      tensor(8.6531e-07)
      >>> a_big = torch.randn(7, 5, 3)
      >>> u, s, v = torch.svd(a_big)
      >>> torch.dist(a_big, torch.matmul(torch.matmul(u, torch.diag_embed(s)), v.transpose(-2, -1)))
      tensor(2.6503e-06)
  
  .. _the resulting vectors will span the same subspace:
         (https://en.wikipedia.org/wiki/Singular_value_decomposition#Singular_values,_singular_vectors,_and_their_relation_to_the_SVD)
  """)
  
  add_docstr(torch.symeig, r"""
  symeig(input, eigenvectors=False, upper=True, *, out=None) -> (Tensor, Tensor)
  
  This function returns eigenvalues and eigenvectors
  of a real symmetric or complex Hermitian matrix :attr:`input` or a batch thereof,
  represented by a namedtuple (eigenvalues, eigenvectors).
  
  This function calculates all eigenvalues (and vectors) of :attr:`input`
  such that :math:`\text{input} = V \text{diag}(e) V^T`.
  
  The boolean argument :attr:`eigenvectors` defines computation of
  both eigenvectors and eigenvalues or eigenvalues only.
  
  If it is ``False``, only eigenvalues are computed. If it is ``True``,
  both eigenvalues and eigenvectors are computed.
  
  Since the input matrix :attr:`input` is supposed to be symmetric or Hermitian,
  only the upper triangular portion is used by default.
  
  If :attr:`upper` is ``False``, then lower triangular portion is used.
  
  .. warning::
  
      :func:`torch.symeig` is deprecated in favor of :func:`torch.linalg.eigh`
      and will be removed in a future PyTorch release. The default behavior has changed
      from using the upper triangular portion of the matrix by default to using the
      lower triangular portion.
  
      ``L, _ = torch.symeig(A, upper=upper)`` should be replaced with
  
      .. code :: python
  
          UPLO = "U" if upper else "L"
          L = torch.linalg.eigvalsh(A, UPLO=UPLO)
  
      ``L, V = torch.symeig(A, eigenvectors=True, upper=upper)`` should be replaced with
  
      .. code :: python
  
          UPLO = "U" if upper else "L"
          L, V = torch.linalg.eigh(A, UPLO=UPLO)
  
  .. note:: The eigenvalues are returned in ascending order. If :attr:`input` is a batch of matrices,
            then the eigenvalues of each matrix in the batch is returned in ascending order.
  
  .. note:: Irrespective of the original strides, the returned matrix `V` will
            be transposed, i.e. with strides `V.contiguous().transpose(-1, -2).stride()`.
  
  .. warning:: Extra care needs to be taken when backward through outputs. Such
               operation is only stable when all eigenvalues are distinct and becomes
               less stable the smaller :math:`\min_{i \neq j} |\lambda_i - \lambda_j|` is.
  
  Args:
      input (Tensor): the input tensor of size :math:`(*, n, n)` where `*` is zero or more
                      batch dimensions consisting of symmetric or Hermitian matrices.
      eigenvectors(bool, optional): controls whether eigenvectors have to be computed
      upper(boolean, optional): controls whether to consider upper-triangular or lower-triangular region
  
  Keyword args:
      out (tuple, optional): the output tuple of (Tensor, Tensor)
  
  Returns:
      (Tensor, Tensor): A namedtuple (eigenvalues, eigenvectors) containing
  
          - **eigenvalues** (*Tensor*): Shape :math:`(*, m)`. The eigenvalues in ascending order.
          - **eigenvectors** (*Tensor*): Shape :math:`(*, m, m)`.
            If ``eigenvectors=False``, it's an empty tensor.
            Otherwise, this tensor contains the orthonormal eigenvectors of the ``input``.
  
  Examples::
  
  
      >>> a = torch.randn(5, 5)
      >>> a = a + a.t()  # To make a symmetric
      >>> a
      tensor([[-5.7827,  4.4559, -0.2344, -1.7123, -1.8330],
              [ 4.4559,  1.4250, -2.8636, -3.2100, -0.1798],
              [-0.2344, -2.8636,  1.7112, -5.5785,  7.1988],
              [-1.7123, -3.2100, -5.5785, -2.6227,  3.1036],
              [-1.8330, -0.1798,  7.1988,  3.1036, -5.1453]])
      >>> e, v = torch.symeig(a, eigenvectors=True)
      >>> e
      tensor([-13.7012,  -7.7497,  -2.3163,   5.2477,   8.1050])
      >>> v
      tensor([[ 0.1643,  0.9034, -0.0291,  0.3508,  0.1817],
              [-0.2417, -0.3071, -0.5081,  0.6534,  0.4026],
              [-0.5176,  0.1223, -0.0220,  0.3295, -0.7798],
              [-0.4850,  0.2695, -0.5773, -0.5840,  0.1337],
              [ 0.6415, -0.0447, -0.6381, -0.0193, -0.4230]])
      >>> a_big = torch.randn(5, 2, 2)
      >>> a_big = a_big + a_big.transpose(-2, -1)  # To make a_big symmetric
      >>> e, v = a_big.symeig(eigenvectors=True)
      >>> torch.allclose(torch.matmul(v, torch.matmul(e.diag_embed(), v.transpose(-2, -1))), a_big)
      True
  """)
  
  add_docstr(torch.t,
             r"""
  t(input) -> Tensor
  
  Expects :attr:`input` to be <= 2-D tensor and transposes dimensions 0
  and 1.
  
  0-D and 1-D tensors are returned as is. When input is a 2-D tensor this
  is equivalent to ``transpose(input, 0, 1)``.
  
  Args:
      {input}
  
  Example::
  
      >>> x = torch.randn(())
      >>> x
      tensor(0.1995)
      >>> torch.t(x)
      tensor(0.1995)
      >>> x = torch.randn(3)
      >>> x
      tensor([ 2.4320, -0.4608,  0.7702])
      >>> torch.t(x)
      tensor([ 2.4320, -0.4608,  0.7702])
      >>> x = torch.randn(2, 3)
      >>> x
      tensor([[ 0.4875,  0.9158, -0.5872],
              [ 0.3938, -0.6929,  0.6932]])
      >>> torch.t(x)
      tensor([[ 0.4875,  0.3938],
              [ 0.9158, -0.6929],
              [-0.5872,  0.6932]])
  
  See also :func:`torch.transpose`.
  """.format(**common_args))
  
  add_docstr(torch.flip,
             r"""
  flip(input, dims) -> Tensor
  
  Reverse the order of a n-D tensor along given axis in dims.
  
  .. note::
      `torch.flip` makes a copy of :attr:`input`'s data. This is different from NumPy's `np.flip`,
      which returns a view in constant time. Since copying a tensor's data is more work than viewing that data,
      `torch.flip` is expected to be slower than `np.flip`.
  
  Args:
      {input}
      dims (a list or tuple): axis to flip on
  
  Example::
  
      >>> x = torch.arange(8).view(2, 2, 2)
      >>> x
      tensor([[[ 0,  1],
               [ 2,  3]],
  
              [[ 4,  5],
               [ 6,  7]]])
      >>> torch.flip(x, [0, 1])
      tensor([[[ 6,  7],
               [ 4,  5]],
  
              [[ 2,  3],
               [ 0,  1]]])
  """.format(**common_args))
  
  add_docstr(torch.fliplr,
             r"""
  fliplr(input) -> Tensor
  
  Flip tensor in the left/right direction, returning a new tensor.
  
  Flip the entries in each row in the left/right direction.
  Columns are preserved, but appear in a different order than before.
  
  Note:
      Requires the tensor to be at least 2-D.
  
  .. note::
      `torch.fliplr` makes a copy of :attr:`input`'s data. This is different from NumPy's `np.fliplr`,
      which returns a view in constant time. Since copying a tensor's data is more work than viewing that data,
      `torch.fliplr` is expected to be slower than `np.fliplr`.
  
  Args:
      input (Tensor): Must be at least 2-dimensional.
  
  Example::
  
      >>> x = torch.arange(4).view(2, 2)
      >>> x
      tensor([[0, 1],
              [2, 3]])
      >>> torch.fliplr(x)
      tensor([[1, 0],
              [3, 2]])
  """.format(**common_args))
  
  add_docstr(torch.flipud,
             r"""
  flipud(input) -> Tensor
  
  Flip tensor in the up/down direction, returning a new tensor.
  
  Flip the entries in each column in the up/down direction.
  Rows are preserved, but appear in a different order than before.
  
  Note:
      Requires the tensor to be at least 1-D.
  
  .. note::
      `torch.flipud` makes a copy of :attr:`input`'s data. This is different from NumPy's `np.flipud`,
      which returns a view in constant time. Since copying a tensor's data is more work than viewing that data,
      `torch.flipud` is expected to be slower than `np.flipud`.
  
  Args:
      input (Tensor): Must be at least 1-dimensional.
  
  Example::
  
      >>> x = torch.arange(4).view(2, 2)
      >>> x
      tensor([[0, 1],
              [2, 3]])
      >>> torch.flipud(x)
      tensor([[2, 3],
              [0, 1]])
  """.format(**common_args))
  
  add_docstr(torch.roll,
             r"""
  roll(input, shifts, dims=None) -> Tensor
  
  Roll the tensor along the given dimension(s). Elements that are shifted beyond the
  last position are re-introduced at the first position. If a dimension is not
  specified, the tensor will be flattened before rolling and then restored
  to the original shape.
  
  Args:
      {input}
      shifts (int or tuple of ints): The number of places by which the elements
          of the tensor are shifted. If shifts is a tuple, dims must be a tuple of
          the same size, and each dimension will be rolled by the corresponding
          value
      dims (int or tuple of ints): Axis along which to roll
  
  Example::
  
      >>> x = torch.tensor([1, 2, 3, 4, 5, 6, 7, 8]).view(4, 2)
      >>> x
      tensor([[1, 2],
              [3, 4],
              [5, 6],
              [7, 8]])
      >>> torch.roll(x, 1, 0)
      tensor([[7, 8],
              [1, 2],
              [3, 4],
              [5, 6]])
      >>> torch.roll(x, -1, 0)
      tensor([[3, 4],
              [5, 6],
              [7, 8],
              [1, 2]])
      >>> torch.roll(x, shifts=(2, 1), dims=(0, 1))
      tensor([[6, 5],
              [8, 7],
              [2, 1],
              [4, 3]])
  """.format(**common_args))
  
  add_docstr(torch.rot90,
             r"""
  rot90(input, k, dims) -> Tensor
  
  Rotate a n-D tensor by 90 degrees in the plane specified by dims axis.
  Rotation direction is from the first towards the second axis if k > 0, and from the second towards the first for k < 0.
  
  Args:
      {input}
      k (int): number of times to rotate
      dims (a list or tuple): axis to rotate
  
  Example::
  
      >>> x = torch.arange(4).view(2, 2)
      >>> x
      tensor([[0, 1],
              [2, 3]])
      >>> torch.rot90(x, 1, [0, 1])
      tensor([[1, 3],
              [0, 2]])
  
      >>> x = torch.arange(8).view(2, 2, 2)
      >>> x
      tensor([[[0, 1],
               [2, 3]],
  
              [[4, 5],
               [6, 7]]])
      >>> torch.rot90(x, 1, [1, 2])
      tensor([[[1, 3],
               [0, 2]],
  
              [[5, 7],
               [4, 6]]])
  """.format(**common_args))
  
  add_docstr(torch.take,
             r"""
  take(input, index) -> Tensor
  
  Returns a new tensor with the elements of :attr:`input` at the given indices.
  The input tensor is treated as if it were viewed as a 1-D tensor. The result
  takes the same shape as the indices.
  
  Args:
      {input}
      index (LongTensor): the indices into tensor
  
  Example::
  
      >>> src = torch.tensor([[4, 3, 5],
      ...                     [6, 7, 8]])
      >>> torch.take(src, torch.tensor([0, 2, 5]))
      tensor([ 4,  5,  8])
  """.format(**common_args))
  
  add_docstr(torch.take_along_dim,
             r"""
  take_along_dim(input, indices, dim, *, out=None) -> Tensor
  
  Selects values from :attr:`input` at the 1-dimensional indices from :attr:`indices` along the given :attr:`dim`.
  
  Functions that return indices along a dimension, like :func:`torch.argmax` and :func:`torch.argsort`,
  are designed to work with this function. See the examples below.
  
  .. note::
      This function is similar to NumPy's `take_along_axis`.
      See also :func:`torch.gather`.
  
  Args:
      {input}
      indices (tensor): the indices into :attr:`input`. Must have long dtype.
      dim (int): dimension to select along.
  
  Keyword args:
      {out}
  
  Example::
  
      >>> t = torch.tensor([[10, 30, 20], [60, 40, 50]])
      >>> max_idx = torch.argmax(t)
      >>> torch.take_along_dim(t, max_idx)
      tensor([60])
      >>> sorted_idx = torch.argsort(t, dim=1)
      >>> torch.take_along_dim(t, sorted_idx, dim=1)
      tensor([[10, 20, 30],
              [40, 50, 60]])
  """.format(**common_args))
  
  add_docstr(torch.tan,
             r"""
  tan(input, *, out=None) -> Tensor
  
  Returns a new tensor with the tangent of the elements of :attr:`input`.
  
  .. math::
      \text{out}_{i} = \tan(\text{input}_{i})
  """ + r"""
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([-1.2027, -1.7687,  0.4412, -1.3856])
      >>> torch.tan(a)
      tensor([-2.5930,  4.9859,  0.4722, -5.3366])
  """.format(**common_args))
  
  add_docstr(torch.tanh,
             r"""
  tanh(input, *, out=None) -> Tensor
  
  Returns a new tensor with the hyperbolic tangent of the elements
  of :attr:`input`.
  
  .. math::
      \text{out}_{i} = \tanh(\text{input}_{i})
  """ + r"""
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([ 0.8986, -0.7279,  1.1745,  0.2611])
      >>> torch.tanh(a)
      tensor([ 0.7156, -0.6218,  0.8257,  0.2553])
  """.format(**common_args))
  
  add_docstr(torch.topk,
             r"""
  topk(input, k, dim=None, largest=True, sorted=True, *, out=None) -> (Tensor, LongTensor)
  
  Returns the :attr:`k` largest elements of the given :attr:`input` tensor along
  a given dimension.
  
  If :attr:`dim` is not given, the last dimension of the `input` is chosen.
  
  If :attr:`largest` is ``False`` then the `k` smallest elements are returned.
  
  A namedtuple of `(values, indices)` is returned, where the `indices` are the indices
  of the elements in the original `input` tensor.
  
  The boolean option :attr:`sorted` if ``True``, will make sure that the returned
  `k` elements are themselves sorted
  
  Args:
      {input}
      k (int): the k in "top-k"
      dim (int, optional): the dimension to sort along
      largest (bool, optional): controls whether to return largest or
             smallest elements
      sorted (bool, optional): controls whether to return the elements
             in sorted order
  
  Keyword args:
      out (tuple, optional): the output tuple of (Tensor, LongTensor) that can be
          optionally given to be used as output buffers
  
  Example::
  
      >>> x = torch.arange(1., 6.)
      >>> x
      tensor([ 1.,  2.,  3.,  4.,  5.])
      >>> torch.topk(x, 3)
      torch.return_types.topk(values=tensor([5., 4., 3.]), indices=tensor([4, 3, 2]))
  """.format(**common_args))
  
  add_docstr(torch.trace,
             r"""
  trace(input) -> Tensor
  
  Returns the sum of the elements of the diagonal of the input 2-D matrix.
  
  Example::
  
      >>> x = torch.arange(1., 10.).view(3, 3)
      >>> x
      tensor([[ 1.,  2.,  3.],
              [ 4.,  5.,  6.],
              [ 7.,  8.,  9.]])
      >>> torch.trace(x)
      tensor(15.)
  """)
  
  add_docstr(torch.transpose,
             r"""
  transpose(input, dim0, dim1) -> Tensor
  
  Returns a tensor that is a transposed version of :attr:`input`.
  The given dimensions :attr:`dim0` and :attr:`dim1` are swapped.
  
  The resulting :attr:`out` tensor shares its underlying storage with the
  :attr:`input` tensor, so changing the content of one would change the content
  of the other.
  
  Args:
      {input}
      dim0 (int): the first dimension to be transposed
      dim1 (int): the second dimension to be transposed
  
  Example::
  
      >>> x = torch.randn(2, 3)
      >>> x
      tensor([[ 1.0028, -0.9893,  0.5809],
              [-0.1669,  0.7299,  0.4942]])
      >>> torch.transpose(x, 0, 1)
      tensor([[ 1.0028, -0.1669],
              [-0.9893,  0.7299],
              [ 0.5809,  0.4942]])
  
  See also :func:`torch.t`.
  """.format(**common_args))
  
  add_docstr(torch.triangular_solve,
             r"""
  triangular_solve(b, A, upper=True, transpose=False, unitriangular=False, *, out=None) -> (Tensor, Tensor)
  
  Solves a system of equations with a triangular coefficient matrix :math:`A`
  and multiple right-hand sides :math:`b`.
  
  In particular, solves :math:`AX = b` and assumes :math:`A` is upper-triangular
  with the default keyword arguments.
  
  `torch.triangular_solve(b, A)` can take in 2D inputs `b, A` or inputs that are
  batches of 2D matrices. If the inputs are batches, then returns
  batched outputs `X`
  
  If the diagonal of :attr:`A` contains zeros or elements that are very close to zero and
  :attr:`unitriangular`\ `= False` (default) or if the input matrix is badly conditioned,
  the result may contain `NaN` s.
  
  Supports input of float, double, cfloat and cdouble data types.
  
  Args:
      b (Tensor): multiple right-hand sides of size :math:`(*, m, k)` where
                  :math:`*` is zero of more batch dimensions
      A (Tensor): the input triangular coefficient matrix of size :math:`(*, m, m)`
                  where :math:`*` is zero or more batch dimensions
      upper (bool, optional): whether to solve the upper-triangular system
          of equations (default) or the lower-triangular system of equations. Default: ``True``.
      transpose (bool, optional): whether :math:`A` should be transposed before
          being sent into the solver. Default: ``False``.
      unitriangular (bool, optional): whether :math:`A` is unit triangular.
          If True, the diagonal elements of :math:`A` are assumed to be
          1 and not referenced from :math:`A`. Default: ``False``.
  
  Keyword args:
      out ((Tensor, Tensor), optional): tuple of two tensors to write
          the output to. Ignored if `None`. Default: `None`.
  
  Returns:
      A namedtuple `(solution, cloned_coefficient)` where `cloned_coefficient`
      is a clone of :math:`A` and `solution` is the solution :math:`X` to :math:`AX = b`
      (or whatever variant of the system of equations, depending on the keyword arguments.)
  
  Examples::
  
      >>> A = torch.randn(2, 2).triu()
      >>> A
      tensor([[ 1.1527, -1.0753],
              [ 0.0000,  0.7986]])
      >>> b = torch.randn(2, 3)
      >>> b
      tensor([[-0.0210,  2.3513, -1.5492],
              [ 1.5429,  0.7403, -1.0243]])
      >>> torch.triangular_solve(b, A)
      torch.return_types.triangular_solve(
      solution=tensor([[ 1.7841,  2.9046, -2.5405],
              [ 1.9320,  0.9270, -1.2826]]),
      cloned_coefficient=tensor([[ 1.1527, -1.0753],
              [ 0.0000,  0.7986]]))
  """)
  
  add_docstr(torch.tril,
             r"""
  tril(input, diagonal=0, *, out=None) -> Tensor
  
  Returns the lower triangular part of the matrix (2-D tensor) or batch of matrices
  :attr:`input`, the other elements of the result tensor :attr:`out` are set to 0.
  
  The lower triangular part of the matrix is defined as the elements on and
  below the diagonal.
  
  The argument :attr:`diagonal` controls which diagonal to consider. If
  :attr:`diagonal` = 0, all elements on and below the main diagonal are
  retained. A positive value includes just as many diagonals above the main
  diagonal, and similarly a negative value excludes just as many diagonals below
  the main diagonal. The main diagonal are the set of indices
  :math:`\lbrace (i, i) \rbrace` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` where
  :math:`d_{1}, d_{2}` are the dimensions of the matrix.
  """ + r"""
  Args:
      {input}
      diagonal (int, optional): the diagonal to consider
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(3, 3)
      >>> a
      tensor([[-1.0813, -0.8619,  0.7105],
              [ 0.0935,  0.1380,  2.2112],
              [-0.3409, -0.9828,  0.0289]])
      >>> torch.tril(a)
      tensor([[-1.0813,  0.0000,  0.0000],
              [ 0.0935,  0.1380,  0.0000],
              [-0.3409, -0.9828,  0.0289]])
  
      >>> b = torch.randn(4, 6)
      >>> b
      tensor([[ 1.2219,  0.5653, -0.2521, -0.2345,  1.2544,  0.3461],
              [ 0.4785, -0.4477,  0.6049,  0.6368,  0.8775,  0.7145],
              [ 1.1502,  3.2716, -1.1243, -0.5413,  0.3615,  0.6864],
              [-0.0614, -0.7344, -1.3164, -0.7648, -1.4024,  0.0978]])
      >>> torch.tril(b, diagonal=1)
      tensor([[ 1.2219,  0.5653,  0.0000,  0.0000,  0.0000,  0.0000],
              [ 0.4785, -0.4477,  0.6049,  0.0000,  0.0000,  0.0000],
              [ 1.1502,  3.2716, -1.1243, -0.5413,  0.0000,  0.0000],
              [-0.0614, -0.7344, -1.3164, -0.7648, -1.4024,  0.0000]])
      >>> torch.tril(b, diagonal=-1)
      tensor([[ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000],
              [ 0.4785,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000],
              [ 1.1502,  3.2716,  0.0000,  0.0000,  0.0000,  0.0000],
              [-0.0614, -0.7344, -1.3164,  0.0000,  0.0000,  0.0000]])
  """.format(**common_args))
  
  # docstr is split in two parts to avoid format mis-captureing :math: braces '{}'
  # as common args.
  add_docstr(torch.tril_indices,
             r"""
  tril_indices(row, col, offset=0, *, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor
  
  Returns the indices of the lower triangular part of a :attr:`row`-by-
  :attr:`col` matrix in a 2-by-N Tensor, where the first row contains row
  coordinates of all indices and the second row contains column coordinates.
  Indices are ordered based on rows and then columns.
  
  The lower triangular part of the matrix is defined as the elements on and
  below the diagonal.
  
  The argument :attr:`offset` controls which diagonal to consider. If
  :attr:`offset` = 0, all elements on and below the main diagonal are
  retained. A positive value includes just as many diagonals above the main
  diagonal, and similarly a negative value excludes just as many diagonals below
  the main diagonal. The main diagonal are the set of indices
  :math:`\lbrace (i, i) \rbrace` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]`
  where :math:`d_{1}, d_{2}` are the dimensions of the matrix.
  
  .. note::
      When running on CUDA, ``row * col`` must be less than :math:`2^{59}` to
      prevent overflow during calculation.
  """ + r"""
  Args:
      row (``int``): number of rows in the 2-D matrix.
      col (``int``): number of columns in the 2-D matrix.
      offset (``int``): diagonal offset from the main diagonal.
          Default: if not provided, 0.
  
  Keyword args:
      dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
          Default: if ``None``, ``torch.long``.
      {device}
      layout (:class:`torch.layout`, optional): currently only support ``torch.strided``.
  
  Example::
  
      >>> a = torch.tril_indices(3, 3)
      >>> a
      tensor([[0, 1, 1, 2, 2, 2],
              [0, 0, 1, 0, 1, 2]])
  
      >>> a = torch.tril_indices(4, 3, -1)
      >>> a
      tensor([[1, 2, 2, 3, 3, 3],
              [0, 0, 1, 0, 1, 2]])
  
      >>> a = torch.tril_indices(4, 3, 1)
      >>> a
      tensor([[0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3],
              [0, 1, 0, 1, 2, 0, 1, 2, 0, 1, 2]])
  """.format(**factory_common_args))
  
  add_docstr(torch.triu,
             r"""
  triu(input, diagonal=0, *, out=None) -> Tensor
  
  Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices
  :attr:`input`, the other elements of the result tensor :attr:`out` are set to 0.
  
  The upper triangular part of the matrix is defined as the elements on and
  above the diagonal.
  
  The argument :attr:`diagonal` controls which diagonal to consider. If
  :attr:`diagonal` = 0, all elements on and above the main diagonal are
  retained. A positive value excludes just as many diagonals above the main
  diagonal, and similarly a negative value includes just as many diagonals below
  the main diagonal. The main diagonal are the set of indices
  :math:`\lbrace (i, i) \rbrace` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` where
  :math:`d_{1}, d_{2}` are the dimensions of the matrix.
  """ + r"""
  Args:
      {input}
      diagonal (int, optional): the diagonal to consider
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(3, 3)
      >>> a
      tensor([[ 0.2309,  0.5207,  2.0049],
              [ 0.2072, -1.0680,  0.6602],
              [ 0.3480, -0.5211, -0.4573]])
      >>> torch.triu(a)
      tensor([[ 0.2309,  0.5207,  2.0049],
              [ 0.0000, -1.0680,  0.6602],
              [ 0.0000,  0.0000, -0.4573]])
      >>> torch.triu(a, diagonal=1)
      tensor([[ 0.0000,  0.5207,  2.0049],
              [ 0.0000,  0.0000,  0.6602],
              [ 0.0000,  0.0000,  0.0000]])
      >>> torch.triu(a, diagonal=-1)
      tensor([[ 0.2309,  0.5207,  2.0049],
              [ 0.2072, -1.0680,  0.6602],
              [ 0.0000, -0.5211, -0.4573]])
  
      >>> b = torch.randn(4, 6)
      >>> b
      tensor([[ 0.5876, -0.0794, -1.8373,  0.6654,  0.2604,  1.5235],
              [-0.2447,  0.9556, -1.2919,  1.3378, -0.1768, -1.0857],
              [ 0.4333,  0.3146,  0.6576, -1.0432,  0.9348, -0.4410],
              [-0.9888,  1.0679, -1.3337, -1.6556,  0.4798,  0.2830]])
      >>> torch.triu(b, diagonal=1)
      tensor([[ 0.0000, -0.0794, -1.8373,  0.6654,  0.2604,  1.5235],
              [ 0.0000,  0.0000, -1.2919,  1.3378, -0.1768, -1.0857],
              [ 0.0000,  0.0000,  0.0000, -1.0432,  0.9348, -0.4410],
              [ 0.0000,  0.0000,  0.0000,  0.0000,  0.4798,  0.2830]])
      >>> torch.triu(b, diagonal=-1)
      tensor([[ 0.5876, -0.0794, -1.8373,  0.6654,  0.2604,  1.5235],
              [-0.2447,  0.9556, -1.2919,  1.3378, -0.1768, -1.0857],
              [ 0.0000,  0.3146,  0.6576, -1.0432,  0.9348, -0.4410],
              [ 0.0000,  0.0000, -1.3337, -1.6556,  0.4798,  0.2830]])
  """.format(**common_args))
  
  # docstr is split in two parts to avoid format mis-capturing :math: braces '{}'
  # as common args.
  add_docstr(torch.triu_indices,
             r"""
  triu_indices(row, col, offset=0, *, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor
  
  Returns the indices of the upper triangular part of a :attr:`row` by
  :attr:`col` matrix in a 2-by-N Tensor, where the first row contains row
  coordinates of all indices and the second row contains column coordinates.
  Indices are ordered based on rows and then columns.
  
  The upper triangular part of the matrix is defined as the elements on and
  above the diagonal.
  
  The argument :attr:`offset` controls which diagonal to consider. If
  :attr:`offset` = 0, all elements on and above the main diagonal are
  retained. A positive value excludes just as many diagonals above the main
  diagonal, and similarly a negative value includes just as many diagonals below
  the main diagonal. The main diagonal are the set of indices
  :math:`\lbrace (i, i) \rbrace` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]`
  where :math:`d_{1}, d_{2}` are the dimensions of the matrix.
  
  .. note::
      When running on CUDA, ``row * col`` must be less than :math:`2^{59}` to
      prevent overflow during calculation.
  """ + r"""
  Args:
      row (``int``): number of rows in the 2-D matrix.
      col (``int``): number of columns in the 2-D matrix.
      offset (``int``): diagonal offset from the main diagonal.
          Default: if not provided, 0.
  
  Keyword args:
      dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
          Default: if ``None``, ``torch.long``.
      {device}
      layout (:class:`torch.layout`, optional): currently only support ``torch.strided``.
  
  Example::
  
      >>> a = torch.triu_indices(3, 3)
      >>> a
      tensor([[0, 0, 0, 1, 1, 2],
              [0, 1, 2, 1, 2, 2]])
  
      >>> a = torch.triu_indices(4, 3, -1)
      >>> a
      tensor([[0, 0, 0, 1, 1, 1, 2, 2, 3],
              [0, 1, 2, 0, 1, 2, 1, 2, 2]])
  
      >>> a = torch.triu_indices(4, 3, 1)
      >>> a
      tensor([[0, 0, 1],
              [1, 2, 2]])
  """.format(**factory_common_args))
  
  add_docstr(torch.true_divide, r"""
  true_divide(dividend, divisor, *, out) -> Tensor
  
  Alias for :func:`torch.div` with ``rounding_mode=None``.
  """)
  
  add_docstr(torch.trunc,
             r"""
  trunc(input, *, out=None) -> Tensor
  
  Returns a new tensor with the truncated integer values of
  the elements of :attr:`input`.
  
  Args:
      {input}
  
  Keyword args:
      {out}
  
  Example::
  
      >>> a = torch.randn(4)
      >>> a
      tensor([ 3.4742,  0.5466, -0.8008, -0.9079])
      >>> torch.trunc(a)
      tensor([ 3.,  0., -0., -0.])
  """.format(**common_args))
  
  add_docstr(torch.fake_quantize_per_tensor_affine,
             r"""
  fake_quantize_per_tensor_affine(input, scale, zero_point, quant_min, quant_max) -> Tensor
  
  Returns a new tensor with the data in :attr:`input` fake quantized using :attr:`scale`,
  :attr:`zero_point`, :attr:`quant_min` and :attr:`quant_max`.
  
  .. math::
      \text{output} = min(
          \text{quant\_max},
          max(
              \text{quant\_min},
              \text{std::nearby\_int}(\text{input} / \text{scale}) + \text{zero\_point}
          )
      )
  
  Args:
      input (Tensor): the input value(s), in ``torch.float32``.
      scale (double or Tensor): quantization scale
      zero_point (int64 or Tensor): quantization zero_point
      quant_min (int64): lower bound of the quantized domain
      quant_max (int64): upper bound of the quantized domain
  
  Returns:
      Tensor: A newly fake_quantized tensor
  
  Example::
  
      >>> x = torch.randn(4)
      >>> x
      tensor([ 0.0552,  0.9730,  0.3973, -1.0780])
      >>> torch.fake_quantize_per_tensor_affine(x, 0.1, 0, 0, 255)
      tensor([0.1000, 1.0000, 0.4000, 0.0000])
      >>> torch.fake_quantize_per_tensor_affine(x, torch.tensor(0.1), torch.tensor(0), 0, 255)
      tensor([0.6000, 0.4000, 0.0000, 0.0000])
  """)
  
  add_docstr(torch.fake_quantize_per_channel_affine,
             r"""
  fake_quantize_per_channel_affine(input, scale, zero_point, quant_min, quant_max) -> Tensor
  
  Returns a new tensor with the data in :attr:`input` fake quantized per channel using :attr:`scale`,
  :attr:`zero_point`, :attr:`quant_min` and :attr:`quant_max`, across the channel specified by :attr:`axis`.
  
  .. math::
      \text{output} = min(
          \text{quant\_max},
          max(
              \text{quant\_min},
              \text{std::nearby\_int}(\text{input} / \text{scale}) + \text{zero\_point}
          )
      )
  
  Args:
      input (Tensor): the input value(s), in ``torch.float32``.
      scale (Tensor): quantization scale, per channel
      zero_point (Tensor): quantization zero_point, per channel
      axis (int32): channel axis
      quant_min (int64): lower bound of the quantized domain
      quant_max (int64): upper bound of the quantized domain
  
  Returns:
      Tensor: A newly fake_quantized per channel tensor
  
  Example::
  
      >>> x = torch.randn(2, 2, 2)
      >>> x
      tensor([[[-0.2525, -0.0466],
               [ 0.3491, -0.2168]],
  
              [[-0.5906,  1.6258],
               [ 0.6444, -0.0542]]])
      >>> scales = (torch.randn(2) + 1) * 0.05
      >>> scales
      tensor([0.0475, 0.0486])
      >>> zero_points = torch.zeros(2).to(torch.long)
      >>> zero_points
      tensor([0, 0])
      >>> torch.fake_quantize_per_channel_affine(x, scales, zero_points, 1, 0, 255)
      tensor([[[0.0000, 0.0000],
               [0.3405, 0.0000]],
  
              [[0.0000, 1.6134],
              [0.6323, 0.0000]]])
  """)
  
  add_docstr(torch.fix,
             r"""
  fix(input, *, out=None) -> Tensor
  
  Alias for :func:`torch.trunc`
  """)
  
  add_docstr(torch.unsqueeze,
             r"""
  unsqueeze(input, dim) -> Tensor
  
  Returns a new tensor with a dimension of size one inserted at the
  specified position.
  
  The returned tensor shares the same underlying data with this tensor.
  
  A :attr:`dim` value within the range ``[-input.dim() - 1, input.dim() + 1)``
  can be used. Negative :attr:`dim` will correspond to :meth:`unsqueeze`
  applied at :attr:`dim` = ``dim + input.dim() + 1``.
  
  Args:
      {input}
      dim (int): the index at which to insert the singleton dimension
  
  Example::
  
      >>> x = torch.tensor([1, 2, 3, 4])
      >>> torch.unsqueeze(x, 0)
      tensor([[ 1,  2,  3,  4]])
      >>> torch.unsqueeze(x, 1)
      tensor([[ 1],
              [ 2],
              [ 3],
              [ 4]])
  """.format(**common_args))
  
  add_docstr(torch.var, r"""
  var(input, dim, unbiased, keepdim=False, *, out=None) -> Tensor
  
  If :attr:`unbiased` is ``True``, Bessel's correction will be used.
  Otherwise, the sample variance is calculated, without any correction.
  
  Args:
      {input}
      {dim}
  
  Keyword args:
      unbiased (bool): whether to use Bessel's correction (:math:`\delta N = 1`).
      {keepdim}
      {out}
  
  .. function:: var(input, unbiased) -> Tensor
     :noindex:
  
  Calculates the variance of all elements in the :attr:`input` tensor.
  
  If :attr:`unbiased` is ``True``, Bessel's correction will be used.
  Otherwise, the sample deviation is calculated, without any correction.
  
  Args:
      {input}
      unbiased (bool): whether to use Bessel's correction (:math:`\delta N = 1`).
  
  Example::
  
      >>> a = torch.tensor([[-0.8166, -1.3802, -0.3560]])
      >>> torch.var(a, unbiased=False)
      tensor(0.1754)
  """.format(**multi_dim_common))
  
  add_docstr(torch.var_mean,
             r"""
  var_mean(input, dim, unbiased, keepdim=False, *, out=None) -> (Tensor, Tensor)
  
  If :attr:`unbiased` is ``True``, Bessel's correction will be used to calculate
  the variance. Otherwise, the sample variance is calculated, without any
  correction.
  
  Args:
      {input}
      {dim}
  
  Keyword args:
      unbiased (bool): whether to use Bessel's correction (:math:`\delta N = 1`).
      {keepdim}
      {out}
  
  Returns:
      A tuple (var, mean) containing the variance and mean.
  
  .. function:: var_mean(input, unbiased) -> (Tensor, Tensor)
     :noindex:
  
  Calculates the variance and mean of all elements in the :attr:`input`
  tensor.
  
  If :attr:`unbiased` is ``True``, Bessel's correction will be used.
  Otherwise, the sample deviation is calculated, without any correction.
  
  Args:
      {input}
      unbiased (bool): whether to use Bessel's correction (:math:`\delta N = 1`).
  
  Returns:
      A tuple (var, mean) containing the variance and mean.
  
  Example::
  
      >>> a = torch.tensor([[-0.8166, -1.3802, -0.3560]])
      >>> torch.var_mean(a, unbiased=False)
      (tensor(0.1754), tensor(-0.8509))
  """.format(**multi_dim_common))
  
  add_docstr(torch.zeros,
             r"""
  zeros(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
  
  Returns a tensor filled with the scalar value `0`, with the shape defined
  by the variable argument :attr:`size`.
  
  Args:
      size (int...): a sequence of integers defining the shape of the output tensor.
          Can be a variable number of arguments or a collection like a list or tuple.
  
  Keyword args:
      {out}
      {dtype}
      {layout}
      {device}
      {requires_grad}
  
  Example::
  
      >>> torch.zeros(2, 3)
      tensor([[ 0.,  0.,  0.],
              [ 0.,  0.,  0.]])
  
      >>> torch.zeros(5)
      tensor([ 0.,  0.,  0.,  0.,  0.])
  """.format(**factory_common_args))
  
  add_docstr(torch.zeros_like,
             r"""
  zeros_like(input, *, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor
  
  Returns a tensor filled with the scalar value `0`, with the same size as
  :attr:`input`. ``torch.zeros_like(input)`` is equivalent to
  ``torch.zeros(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``.
  
  .. warning::
      As of 0.4, this function does not support an :attr:`out` keyword. As an alternative,
      the old ``torch.zeros_like(input, out=output)`` is equivalent to
      ``torch.zeros(input.size(), out=output)``.
  
  Args:
      {input}
  
  Keyword args:
      {dtype}
      {layout}
      {device}
      {requires_grad}
      {memory_format}
  
  Example::
  
      >>> input = torch.empty(2, 3)
      >>> torch.zeros_like(input)
      tensor([[ 0.,  0.,  0.],
              [ 0.,  0.,  0.]])
  """.format(**factory_like_common_args))
  
  add_docstr(torch.empty,
             """
  empty(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False, \
  memory_format=torch.contiguous_format) -> Tensor
  
  Returns a tensor filled with uninitialized data. The shape of the tensor is
  defined by the variable argument :attr:`size`.
  
  Args:
      size (int...): a sequence of integers defining the shape of the output tensor.
          Can be a variable number of arguments or a collection like a list or tuple.
  
  Keyword args:
      {out}
      {dtype}
      {layout}
      {device}
      {requires_grad}
      {pin_memory}
      {memory_format}
  
  Example::
  
      >>> a=torch.empty((2,3), dtype=torch.int32, device = 'cuda')
      >>> torch.empty_like(a)
      tensor([[0, 0, 0],
              [0, 0, 0]], device='cuda:0', dtype=torch.int32)
  """.format(**factory_common_args))
  
  add_docstr(torch.empty_like,
             r"""
  empty_like(input, *, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor
  
  Returns an uninitialized tensor with the same size as :attr:`input`.
  ``torch.empty_like(input)`` is equivalent to
  ``torch.empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``.
  
  Args:
      {input}
  
  Keyword args:
      {dtype}
      {layout}
      {device}
      {requires_grad}
      {memory_format}
  
  Example::
  
      >>> torch.empty((2,3), dtype=torch.int64)
      tensor([[ 9.4064e+13,  2.8000e+01,  9.3493e+13],
              [ 7.5751e+18,  7.1428e+18,  7.5955e+18]])
  """.format(**factory_like_common_args))
  
  add_docstr(torch.empty_strided,
             r"""
  empty_strided(size, stride, *, dtype=None, layout=None, device=None, requires_grad=False, pin_memory=False) -> Tensor
  
  Creates a tensor with the specified :attr:`size` and :attr:`stride` and filled with undefined data.
  
  .. warning::
      If the constructed tensor is "overlapped" (with multiple indices referring to the same element
      in memory) its behavior is undefined.
  
  Args:
      size (tuple of ints): the shape of the output tensor
      stride (tuple of ints): the strides of the output tensor
  
  Keyword args:
      {dtype}
      {layout}
      {device}
      {requires_grad}
      {pin_memory}
  
  Example::
  
      >>> a = torch.empty_strided((2, 3), (1, 2))
      >>> a
      tensor([[8.9683e-44, 4.4842e-44, 5.1239e+07],
              [0.0000e+00, 0.0000e+00, 3.0705e-41]])
      >>> a.stride()
      (1, 2)
      >>> a.size()
      torch.Size([2, 3])
  """.format(**factory_common_args))
  
  add_docstr(torch.full, r"""
  full(size, fill_value, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
  
  Creates a tensor of size :attr:`size` filled with :attr:`fill_value`. The
  tensor's dtype is inferred from :attr:`fill_value`.
  
  Args:
      size (int...): a list, tuple, or :class:`torch.Size` of integers defining the
          shape of the output tensor.
      fill_value (Scalar): the value to fill the output tensor with.
  
  Keyword args:
      {out}
      {dtype}
      {layout}
      {device}
      {requires_grad}
  
  Example::
  
      >>> torch.full((2, 3), 3.141592)
      tensor([[ 3.1416,  3.1416,  3.1416],
              [ 3.1416,  3.1416,  3.1416]])
  """.format(**factory_common_args))
  
  add_docstr(torch.full_like,
             """
  full_like(input, fill_value, \\*, dtype=None, layout=torch.strided, device=None, requires_grad=False, \
  memory_format=torch.preserve_format) -> Tensor
  
  Returns a tensor with the same size as :attr:`input` filled with :attr:`fill_value`.
  ``torch.full_like(input, fill_value)`` is equivalent to
  ``torch.full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device)``.
  
  Args:
      {input}
      fill_value: the number to fill the output tensor with.
  
  Keyword args:
      {dtype}
      {layout}
      {device}
      {requires_grad}
      {memory_format}
  """.format(**factory_like_common_args))
  
  add_docstr(torch.det, r"""
  det(input) -> Tensor
  
  Alias for :func:`torch.linalg.det`
  """)
  
  add_docstr(torch.where,
             r"""
  where(condition, x, y) -> Tensor
  
  Return a tensor of elements selected from either :attr:`x` or :attr:`y`, depending on :attr:`condition`.
  
  The operation is defined as:
  
  .. math::
      \text{out}_i = \begin{cases}
          \text{x}_i & \text{if } \text{condition}_i \\
          \text{y}_i & \text{otherwise} \\
      \end{cases}
  
  .. note::
      The tensors :attr:`condition`, :attr:`x`, :attr:`y` must be :ref:`broadcastable <broadcasting-semantics>`.
  
  .. note::
      Currently valid scalar and tensor combination are
      1. Scalar of floating dtype and torch.double
      2. Scalar of integral dtype and torch.long
      3. Scalar of complex dtype and torch.complex128
  
  Arguments:
      condition (BoolTensor): When True (nonzero), yield x, otherwise yield y
      x (Tensor or Scalar): value (if :attr:`x` is a scalar) or values selected at indices
                            where :attr:`condition` is ``True``
      y (Tensor or Scalar): value (if :attr:`y` is a scalar) or values selected at indices
                            where :attr:`condition` is ``False``
  
  Returns:
      Tensor: A tensor of shape equal to the broadcasted shape of :attr:`condition`, :attr:`x`, :attr:`y`
  
  Example::
  
      >>> x = torch.randn(3, 2)
      >>> y = torch.ones(3, 2)
      >>> x
      tensor([[-0.4620,  0.3139],
              [ 0.3898, -0.7197],
              [ 0.0478, -0.1657]])
      >>> torch.where(x > 0, x, y)
      tensor([[ 1.0000,  0.3139],
              [ 0.3898,  1.0000],
              [ 0.0478,  1.0000]])
      >>> x = torch.randn(2, 2, dtype=torch.double)
      >>> x
      tensor([[ 1.0779,  0.0383],
              [-0.8785, -1.1089]], dtype=torch.float64)
      >>> torch.where(x > 0, x, 0.)
      tensor([[1.0779, 0.0383],
              [0.0000, 0.0000]], dtype=torch.float64)
  
  .. function:: where(condition) -> tuple of LongTensor
     :noindex:
  
  ``torch.where(condition)`` is identical to
  ``torch.nonzero(condition, as_tuple=True)``.
  
  .. note::
      See also :func:`torch.nonzero`.
  """)
  
  add_docstr(torch.logdet,
             r"""
  logdet(input) -> Tensor
  
  Calculates log determinant of a square matrix or batches of square matrices.
  
  .. note::
      Result is ``-inf`` if :attr:`input` has zero log determinant, and is ``nan`` if
      :attr:`input` has negative determinant.
  
  .. note::
      Backward through :meth:`logdet` internally uses SVD results when :attr:`input`
      is not invertible. In this case, double backward through :meth:`logdet` will
      be unstable in when :attr:`input` doesn't have distinct singular values. See
      :meth:`~torch.svd` for details.
  
  Arguments:
      input (Tensor): the input tensor of size ``(*, n, n)`` where ``*`` is zero or more
                  batch dimensions.
  
  Example::
  
      >>> A = torch.randn(3, 3)
      >>> torch.det(A)
      tensor(0.2611)
      >>> torch.logdet(A)
      tensor(-1.3430)
      >>> A
      tensor([[[ 0.9254, -0.6213],
               [-0.5787,  1.6843]],
  
              [[ 0.3242, -0.9665],
               [ 0.4539, -0.0887]],
  
              [[ 1.1336, -0.4025],
               [-0.7089,  0.9032]]])
      >>> A.det()
      tensor([1.1990, 0.4099, 0.7386])
      >>> A.det().log()
      tensor([ 0.1815, -0.8917, -0.3031])
  """)
  
  add_docstr(torch.slogdet, r"""
  slogdet(input) -> (Tensor, Tensor)
  
  Alias for :func:`torch.linalg.slogdet`
  """)
  
  add_docstr(torch.pinverse, r"""
  pinverse(input, rcond=1e-15) -> Tensor
  
  Alias for :func:`torch.linalg.pinv`
  """)
  
  add_docstr(torch.hann_window,
             """
  hann_window(window_length, periodic=True, *, dtype=None, \
  layout=torch.strided, device=None, requires_grad=False) -> Tensor
  """ + r"""
  Hann window function.
  
  .. math::
      w[n] = \frac{1}{2}\ \left[1 - \cos \left( \frac{2 \pi n}{N - 1} \right)\right] =
              \sin^2 \left( \frac{\pi n}{N - 1} \right),
  
  where :math:`N` is the full window size.
  
  The input :attr:`window_length` is a positive integer controlling the
  returned window size. :attr:`periodic` flag determines whether the returned
  window trims off the last duplicate value from the symmetric window and is
  ready to be used as a periodic window with functions like
  :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in
  above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have
  ``torch.hann_window(L, periodic=True)`` equal to
  ``torch.hann_window(L + 1, periodic=False)[:-1])``.
  
  .. note::
      If :attr:`window_length` :math:`=1`, the returned window contains a single value 1.
  """ + r"""
  Arguments:
      window_length (int): the size of returned window
      periodic (bool, optional): If True, returns a window to be used as periodic
          function. If False, return a symmetric window.
  
  Keyword args:
      {dtype} Only floating point types are supported.
      layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only
            ``torch.strided`` (dense layout) is supported.
      {device}
      {requires_grad}
  
  Returns:
      Tensor: A 1-D tensor of size :math:`(\text{{window\_length}},)` containing the window
  
  """.format(**factory_common_args))
  
  
  add_docstr(torch.hamming_window,
             """
  hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, *, dtype=None, \
  layout=torch.strided, device=None, requires_grad=False) -> Tensor
  """ + r"""
  Hamming window function.
  
  .. math::
      w[n] = \alpha - \beta\ \cos \left( \frac{2 \pi n}{N - 1} \right),
  
  where :math:`N` is the full window size.
  
  The input :attr:`window_length` is a positive integer controlling the
  returned window size. :attr:`periodic` flag determines whether the returned
  window trims off the last duplicate value from the symmetric window and is
  ready to be used as a periodic window with functions like
  :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in
  above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have
  ``torch.hamming_window(L, periodic=True)`` equal to
  ``torch.hamming_window(L + 1, periodic=False)[:-1])``.
  
  .. note::
      If :attr:`window_length` :math:`=1`, the returned window contains a single value 1.
  
  .. note::
      This is a generalized version of :meth:`torch.hann_window`.
  """ + r"""
  Arguments:
      window_length (int): the size of returned window
      periodic (bool, optional): If True, returns a window to be used as periodic
          function. If False, return a symmetric window.
      alpha (float, optional): The coefficient :math:`\alpha` in the equation above
      beta (float, optional): The coefficient :math:`\beta` in the equation above
  
  Keyword args:
      {dtype} Only floating point types are supported.
      layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only
            ``torch.strided`` (dense layout) is supported.
      {device}
      {requires_grad}
  
  Returns:
      Tensor: A 1-D tensor of size :math:`(\text{{window\_length}},)` containing the window
  
  """.format(**factory_common_args))
  
  
  add_docstr(torch.bartlett_window,
             """
  bartlett_window(window_length, periodic=True, *, dtype=None, \
  layout=torch.strided, device=None, requires_grad=False) -> Tensor
  """ + r"""
  Bartlett window function.
  
  .. math::
      w[n] = 1 - \left| \frac{2n}{N-1} - 1 \right| = \begin{cases}
          \frac{2n}{N - 1} & \text{if } 0 \leq n \leq \frac{N - 1}{2} \\
          2 - \frac{2n}{N - 1} & \text{if } \frac{N - 1}{2} < n < N \\
      \end{cases},
  
  where :math:`N` is the full window size.
  
  The input :attr:`window_length` is a positive integer controlling the
  returned window size. :attr:`periodic` flag determines whether the returned
  window trims off the last duplicate value from the symmetric window and is
  ready to be used as a periodic window with functions like
  :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in
  above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have
  ``torch.bartlett_window(L, periodic=True)`` equal to
  ``torch.bartlett_window(L + 1, periodic=False)[:-1])``.
  
  .. note::
      If :attr:`window_length` :math:`=1`, the returned window contains a single value 1.
  """ + r"""
  Arguments:
      window_length (int): the size of returned window
      periodic (bool, optional): If True, returns a window to be used as periodic
          function. If False, return a symmetric window.
  
  Keyword args:
      {dtype} Only floating point types are supported.
      layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only
            ``torch.strided`` (dense layout) is supported.
      {device}
      {requires_grad}
  
  Returns:
      Tensor: A 1-D tensor of size :math:`(\text{{window\_length}},)` containing the window
  
  """.format(**factory_common_args))
  
  
  add_docstr(torch.blackman_window,
             """
  blackman_window(window_length, periodic=True, *, dtype=None, \
  layout=torch.strided, device=None, requires_grad=False) -> Tensor
  """ + r"""
  Blackman window function.
  
  .. math::
      w[n] = 0.42 - 0.5 \cos \left( \frac{2 \pi n}{N - 1} \right) + 0.08 \cos \left( \frac{4 \pi n}{N - 1} \right)
  
  where :math:`N` is the full window size.
  
  The input :attr:`window_length` is a positive integer controlling the
  returned window size. :attr:`periodic` flag determines whether the returned
  window trims off the last duplicate value from the symmetric window and is
  ready to be used as a periodic window with functions like
  :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in
  above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have
  ``torch.blackman_window(L, periodic=True)`` equal to
  ``torch.blackman_window(L + 1, periodic=False)[:-1])``.
  
  .. note::
      If :attr:`window_length` :math:`=1`, the returned window contains a single value 1.
  """ + r"""
  Arguments:
      window_length (int): the size of returned window
      periodic (bool, optional): If True, returns a window to be used as periodic
          function. If False, return a symmetric window.
  
  Keyword args:
      {dtype} Only floating point types are supported.
      layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only
            ``torch.strided`` (dense layout) is supported.
      {device}
      {requires_grad}
  
  Returns:
      Tensor: A 1-D tensor of size :math:`(\text{{window\_length}},)` containing the window
  
  """.format(**factory_common_args))
  
  
  add_docstr(torch.kaiser_window, """
  kaiser_window(window_length, periodic=True, beta=12.0, *, dtype=None, \
  layout=torch.strided, device=None, requires_grad=False) -> Tensor
  """ + r"""
  Computes the Kaiser window with window length :attr:`window_length` and shape parameter :attr:`beta`.
  
  Let I_0 be the zeroth order modified Bessel function of the first kind (see :func:`torch.i0`) and
  ``N = L - 1`` if :attr:`periodic` is False and ``L`` if :attr:`periodic` is True,
  where ``L`` is the :attr:`window_length`. This function computes:
  
  .. math::
      out_i = I_0 \left( \beta \sqrt{1 - \left( {\frac{i - N/2}{N/2}} \right) ^2 } \right) / I_0( \beta )
  
  Calling ``torch.kaiser_window(L, B, periodic=True)`` is equivalent to calling
  ``torch.kaiser_window(L + 1, B, periodic=False)[:-1])``.
  The :attr:`periodic` argument is intended as a helpful shorthand
  to produce a periodic window as input to functions like :func:`torch.stft`.
  
  .. note::
      If :attr:`window_length` is one, then the returned window is a single element tensor containing a one.
  
  """ + r"""
  Args:
      window_length (int): length of the window.
      periodic (bool, optional): If True, returns a periodic window suitable for use in spectral analysis.
          If False, returns a symmetric window suitable for use in filter design.
      beta (float, optional): shape parameter for the window.
  
  Keyword args:
      {dtype}
      layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only
            ``torch.strided`` (dense layout) is supported.
      {device}
      {requires_grad}
  
  """.format(**factory_common_args))
  
  
  add_docstr(torch.vander,
             """
  vander(x, N=None, increasing=False) -> Tensor
  """ + r"""
  Generates a Vandermonde matrix.
  
  The columns of the output matrix are elementwise powers of the input vector :math:`x^{{(N-1)}}, x^{{(N-2)}}, ..., x^0`.
  If increasing is True, the order of the columns is reversed :math:`x^0, x^1, ..., x^{{(N-1)}}`. Such a
  matrix with a geometric progression in each row is named for Alexandre-Theophile Vandermonde.
  
  Arguments:
      x (Tensor): 1-D input tensor.
      N (int, optional): Number of columns in the output. If N is not specified,
          a square array is returned :math:`(N = len(x))`.
      increasing (bool, optional): Order of the powers of the columns. If True,
          the powers increase from left to right, if False (the default) they are reversed.
  
  Returns:
      Tensor: Vandermonde matrix. If increasing is False, the first column is :math:`x^{{(N-1)}}`,
      the second :math:`x^{{(N-2)}}` and so forth. If increasing is True, the columns
      are :math:`x^0, x^1, ..., x^{{(N-1)}}`.
  
  Example::
  
      >>> x = torch.tensor([1, 2, 3, 5])
      >>> torch.vander(x)
      tensor([[  1,   1,   1,   1],
              [  8,   4,   2,   1],
              [ 27,   9,   3,   1],
              [125,  25,   5,   1]])
      >>> torch.vander(x, N=3)
      tensor([[ 1,  1,  1],
              [ 4,  2,  1],
              [ 9,  3,  1],
              [25,  5,  1]])
      >>> torch.vander(x, N=3, increasing=True)
      tensor([[ 1,  1,  1],
              [ 1,  2,  4],
              [ 1,  3,  9],
              [ 1,  5, 25]])
  
  """.format(**factory_common_args))
  
  
  add_docstr(torch.unbind,
             r"""
  unbind(input, dim=0) -> seq
  
  Removes a tensor dimension.
  
  Returns a tuple of all slices along a given dimension, already without it.
  
  Arguments:
      input (Tensor): the tensor to unbind
      dim (int): dimension to remove
  
  Example::
  
      >>> torch.unbind(torch.tensor([[1, 2, 3],
      >>>                            [4, 5, 6],
      >>>                            [7, 8, 9]]))
      (tensor([1, 2, 3]), tensor([4, 5, 6]), tensor([7, 8, 9]))
  """)
  
  
  add_docstr(torch.combinations,
             r"""
  combinations(input, r=2, with_replacement=False) -> seq
  
  Compute combinations of length :math:`r` of the given tensor. The behavior is similar to
  python's `itertools.combinations` when `with_replacement` is set to `False`, and
  `itertools.combinations_with_replacement` when `with_replacement` is set to `True`.
  
  Arguments:
      input (Tensor): 1D vector.
      r (int, optional): number of elements to combine
      with_replacement (boolean, optional): whether to allow duplication in combination
  
  Returns:
      Tensor: A tensor equivalent to converting all the input tensors into lists, do
      `itertools.combinations` or `itertools.combinations_with_replacement` on these
      lists, and finally convert the resulting list into tensor.
  
  Example::
  
      >>> a = [1, 2, 3]
      >>> list(itertools.combinations(a, r=2))
      [(1, 2), (1, 3), (2, 3)]
      >>> list(itertools.combinations(a, r=3))
      [(1, 2, 3)]
      >>> list(itertools.combinations_with_replacement(a, r=2))
      [(1, 1), (1, 2), (1, 3), (2, 2), (2, 3), (3, 3)]
      >>> tensor_a = torch.tensor(a)
      >>> torch.combinations(tensor_a)
      tensor([[1, 2],
              [1, 3],
              [2, 3]])
      >>> torch.combinations(tensor_a, r=3)
      tensor([[1, 2, 3]])
      >>> torch.combinations(tensor_a, with_replacement=True)
      tensor([[1, 1],
              [1, 2],
              [1, 3],
              [2, 2],
              [2, 3],
              [3, 3]])
  
  """)
  
  add_docstr(torch.trapezoid,
             r"""
  trapezoid(y, x=None, *, dx=None, dim=-1) -> Tensor
  
  Computes the `trapezoidal rule <https://en.wikipedia.org/wiki/Trapezoidal_rule>`_ along
  :attr:`dim`. By default the spacing between elements is assumed to be 1, but
  :attr:`dx` can be used to specify a different constant spacing, and :attr:`x` can be
  used to specify arbitrary spacing along :attr:`dim`.
  
  
  Assuming :attr:`y` is a one-dimensional tensor with elements :math:`{y_0, y_1, ..., y_n}`,
  the default computation is
  
  .. math::
      \begin{aligned}
          \sum_{i = 1}^{n-1} \frac{1}{2} (y_i + y_{i-1})
      \end{aligned}
  
  When :attr:`dx` is specified the computation becomes
  
  .. math::
      \begin{aligned}
          \sum_{i = 1}^{n-1} \frac{\Delta x}{2} (y_i + y_{i-1})
      \end{aligned}
  
  effectively multiplying the result by :attr:`dx`. When :attr:`x` is specified,
  assuming :attr:`x` is also a one-dimensional tensor with
  elements :math:`{x_0, x_1, ..., x_n}`, the computation becomes
  
  .. math::
      \begin{aligned}
          \sum_{i = 1}^{n-1} \frac{(x_i - x_{i-1})}{2} (y_i + y_{i-1})
      \end{aligned}
  
  When :attr:`x` and :attr:`y` have the same size, the computation is as described above and no broadcasting is needed.
  The broadcasting behavior of this function is as follows when their sizes are different. For both :attr:`x`
  and :attr:`y`, the function computes the difference between consecutive elements along
  dimension :attr:`dim`. This effectively creates two tensors, `x_diff` and `y_diff`, that have
  the same shape as the original tensors except their lengths along the dimension :attr:`dim` is reduced by 1.
  After that, those two tensors are broadcast together to compute final output as part of the trapezoidal rule.
  See the examples below for details.
  
  .. note::
      The trapezoidal rule is a technique for approximating the definite integral of a function
      by averaging its left and right Riemann sums. The approximation becomes more accurate as
      the resolution of the partition increases.
  
  Arguments:
      y (Tensor): Values to use when computing the trapezoidal rule.
      x (Tensor): If specified, defines spacing between values as specified above.
  
  Keyword arguments:
      dx (float): constant spacing between values. If neither :attr:`x` or :attr:`dx`
          are specified then this defaults to 1. Effectively multiplies the result by its value.
      dim (int): The dimension along which to compute the trapezoidal rule.
          The last (inner-most) dimension by default.
  
  Examples::
  
      >>> # Computes the trapezoidal rule in 1D, spacing is implicitly 1
      >>> y = torch.tensor([1, 5, 10])
      >>> torch.trapezoid(y)
      tensor(10.5)
  
      >>> # Computes the same trapezoidal rule directly to verify
      >>> (1 + 10 + 10) / 2
      10.5
  
      >>> # Computes the trapezoidal rule in 1D with constant spacing of 2
      >>> # NOTE: the result is the same as before, but multiplied by 2
      >>> torch.trapezoid(y, dx=2)
      21.0
  
      >>> # Computes the trapezoidal rule in 1D with arbitrary spacing
      >>> x = torch.tensor([1, 3, 6])
      >>> torch.trapezoid(y, x)
      28.5
  
      >>> # Computes the same trapezoidal rule directly to verify
      >>> ((3 - 1) * (1 + 5) + (6 - 3) * (5 + 10)) / 2
      28.5
  
      >>> # Computes the trapezoidal rule for each row of a 3x3 matrix
      >>> y = torch.arange(9).reshape(3, 3)
      tensor([[0, 1, 2],
              [3, 4, 5],
              [6, 7, 8]])
      >>> torch.trapezoid(y)
      tensor([ 2., 8., 14.])
  
      >>> # Computes the trapezoidal rule for each column of the matrix
      >>> torch.trapezoid(y, dim=0)
      tensor([ 6., 8., 10.])
  
      >>> # Computes the trapezoidal rule for each row of a 3x3 ones matrix
      >>> #   with the same arbitrary spacing
      >>> y = torch.ones(3, 3)
      >>> x = torch.tensor([1, 3, 6])
      >>> torch.trapezoid(y, x)
      array([5., 5., 5.])
  
      >>> # Computes the trapezoidal rule for each row of a 3x3 ones matrix
      >>> #   with different arbitrary spacing per row
      >>> y = torch.ones(3, 3)
      >>> x = torch.tensor([[1, 2, 3], [1, 3, 5], [1, 4, 7]])
      >>> torch.trapezoid(y, x)
      array([2., 4., 6.])
  """)
  
  add_docstr(torch.trapz,
             r"""
  trapz(y, x, *, dim=-1) -> Tensor
  
  Alias for :func:`torch.trapezoid`.
  """)
  
  add_docstr(torch.cumulative_trapezoid,
             r"""
  cumulative_trapezoid(y, x=None, *, dx=None, dim=-1) -> Tensor
  
  Cumulatively computes the `trapezoidal rule <https://en.wikipedia.org/wiki/Trapezoidal_rule>`_
  along :attr:`dim`. By default the spacing between elements is assumed to be 1, but
  :attr:`dx` can be used to specify a different constant spacing, and :attr:`x` can be
  used to specify arbitrary spacing along :attr:`dim`.
  
  For more details, please read :func:`torch.trapezoid`. The difference between :func:`torch.trapezoid`
  and this function is that, :func:`torch.trapezoid` returns a value for each integration,
  where as this function returns a cumulative value for every spacing within the integration. This
  is analogous to how `.sum` returns a value and `.cumsum` returns a cumulative sum.
  
  Arguments:
      y (Tensor): Values to use when computing the trapezoidal rule.
      x (Tensor): If specified, defines spacing between values as specified above.
  
  Keyword arguments:
      dx (float): constant spacing between values. If neither :attr:`x` or :attr:`dx`
          are specified then this defaults to 1. Effectively multiplies the result by its value.
      dim (int): The dimension along which to compute the trapezoidal rule.
          The last (inner-most) dimension by default.
  
  Examples::
  
      >>> # Cumulatively computes the trapezoidal rule in 1D, spacing is implicitly 1.
      >>> y = torch.tensor([1, 5, 10])
      >>> torch.cumulative_trapezoid(y)
      tensor([3., 10.5])
  
      >>> # Computes the same trapezoidal rule directly up to each element to verify
      >>> (1 + 5) / 2
      3.0
      >>> (1 + 10 + 10) / 2
      10.5
  
      >>> # Cumulatively computes the trapezoidal rule in 1D with constant spacing of 2
      >>> # NOTE: the result is the same as before, but multiplied by 2
      >>> torch.cumulative_trapezoid(y, dx=2)
      tensor([6., 21.])
  
      >>> # Cumulatively computes the trapezoidal rule in 1D with arbitrary spacing
      >>> x = torch.tensor([1, 3, 6])
      >>> torch.cumulative_trapezoid(y, x)
      tensor([6., 28.5])
  
      >>> # Computes the same trapezoidal rule directly up to each element to verify
      >>> ((3 - 1) * (1 + 5)) / 2
      6.0
      >>> ((3 - 1) * (1 + 5) + (6 - 3) * (5 + 10)) / 2
      28.5
  
      >>> # Cumulatively computes the trapezoidal rule for each row of a 3x3 matrix
      >>> y = torch.arange(9).reshape(3, 3)
      tensor([[0, 1, 2],
              [3, 4, 5],
              [6, 7, 8]])
      >>> torch.cumulative_trapezoid(y)
      tensor([[ 0.5,  2.],
              [ 3.5,  8.],
              [ 6.5, 14.]])
  
      >>> # Cumulatively computes the trapezoidal rule for each column of the matrix
      >>> torch.cumulative_trapezoid(y, dim=0)
      tensor([[ 1.5,  2.5,  3.5],
              [ 6.0,  8.0, 10.0]])
  
      >>> # Cumulatively computes the trapezoidal rule for each row of a 3x3 ones matrix
      >>> #   with the same arbitrary spacing
      >>> y = torch.ones(3, 3)
      >>> x = torch.tensor([1, 3, 6])
      >>> torch.cumulative_trapezoid(y, x)
      tensor([[2., 5.],
              [2., 5.],
              [2., 5.]])
  
      >>> # Cumulatively computes the trapezoidal rule for each row of a 3x3 ones matrix
      >>> #   with different arbitrary spacing per row
      >>> y = torch.ones(3, 3)
      >>> x = torch.tensor([[1, 2, 3], [1, 3, 5], [1, 4, 7]])
      >>> torch.cumulative_trapezoid(y, x)
      tensor([[1., 2.],
              [2., 4.],
              [3., 6.]])
  """)
  
  add_docstr(torch.repeat_interleave,
             r"""
  repeat_interleave(input, repeats, dim=None, *, output_size=None) -> Tensor
  
  Repeat elements of a tensor.
  
  .. warning::
  
      This is different from :meth:`torch.Tensor.repeat` but similar to ``numpy.repeat``.
  
  Args:
      {input}
      repeats (Tensor or int): The number of repetitions for each element.
          repeats is broadcasted to fit the shape of the given axis.
      dim (int, optional): The dimension along which to repeat values.
          By default, use the flattened input array, and return a flat output
          array.
  
  Keyword args:
      output_size (int, optional): Total output size for the given axis
          ( e.g. sum of repeats). If given, it will avoid stream syncronization
          needed to calculate output shape of the tensor.
  
  Returns:
      Tensor: Repeated tensor which has the same shape as input, except along the given axis.
  
  Example::
  
      >>> x = torch.tensor([1, 2, 3])
      >>> x.repeat_interleave(2)
      tensor([1, 1, 2, 2, 3, 3])
      >>> y = torch.tensor([[1, 2], [3, 4]])
      >>> torch.repeat_interleave(y, 2)
      tensor([1, 1, 2, 2, 3, 3, 4, 4])
      >>> torch.repeat_interleave(y, 3, dim=1)
      tensor([[1, 1, 1, 2, 2, 2],
              [3, 3, 3, 4, 4, 4]])
      >>> torch.repeat_interleave(y, torch.tensor([1, 2]), dim=0)
      tensor([[1, 2],
              [3, 4],
              [3, 4]])
      >>> torch.repeat_interleave(y, torch.tensor([1, 2]), dim=0, output_size=3)
      tensor([[1, 2],
              [3, 4],
              [3, 4]])
  
  .. function:: repeat_interleave(repeats, *, output_size=None) -> Tensor
     :noindex:
  
  If the `repeats` is `tensor([n1, n2, n3, ...])`, then the output will be
  `tensor([0, 0, ..., 1, 1, ..., 2, 2, ..., ...])` where `0` appears `n1` times,
  `1` appears `n2` times, `2` appears `n3` times, etc.
  """.format(**common_args))
  
  add_docstr(torch.tile, r"""
  tile(input, dims) -> Tensor
  
  Constructs a tensor by repeating the elements of :attr:`input`.
  The :attr:`dims` argument specifies the number of repetitions
  in each dimension.
  
  If :attr:`dims` specifies fewer dimensions than :attr:`input` has, then
  ones are prepended to :attr:`dims` until all dimensions are specified.
  For example, if :attr:`input` has shape (8, 6, 4, 2) and :attr:`dims`
  is (2, 2), then :attr:`dims` is treated as (1, 1, 2, 2).
  
  Analogously, if :attr:`input` has fewer dimensions than :attr:`dims`
  specifies, then :attr:`input` is treated as if it were unsqueezed at
  dimension zero until it has as many dimensions as :attr:`dims` specifies.
  For example, if :attr:`input` has shape (4, 2) and :attr:`dims`
  is (3, 3, 2, 2), then :attr:`input` is treated as if it had the
  shape (1, 1, 4, 2).
  
  .. note::
  
      This function is similar to NumPy's tile function.
  
  Args:
      input (Tensor): the tensor whose elements to repeat.
      dims (tuple): the number of repetitions per dimension.
  
  Example::
  
      >>> x = torch.tensor([1, 2, 3])
      >>> x.tile((2,))
      tensor([1, 2, 3, 1, 2, 3])
      >>> y = torch.tensor([[1, 2], [3, 4]])
      >>> torch.tile(y, (2, 2))
      tensor([[1, 2, 1, 2],
              [3, 4, 3, 4],
              [1, 2, 1, 2],
              [3, 4, 3, 4]])
  """)
  
  add_docstr(torch.quantize_per_tensor,
             r"""
  quantize_per_tensor(input, scale, zero_point, dtype) -> Tensor
  
  Converts a float tensor to a quantized tensor with given scale and zero point.
  
  Arguments:
      input (Tensor): float tensor or list of tensors to quantize
      scale (float or Tensor): scale to apply in quantization formula
      zero_point (int or Tensor): offset in integer value that maps to float zero
      dtype (:class:`torch.dtype`): the desired data type of returned tensor.
          Has to be one of the quantized dtypes: ``torch.quint8``, ``torch.qint8``, ``torch.qint32``
  
  Returns:
      Tensor: A newly quantized tensor or list of quantized tensors.
  
  Example::
  
      >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8)
      tensor([-1.,  0.,  1.,  2.], size=(4,), dtype=torch.quint8,
             quantization_scheme=torch.per_tensor_affine, scale=0.1, zero_point=10)
      >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8).int_repr()
      tensor([ 0, 10, 20, 30], dtype=torch.uint8)
      >>> torch.quantize_per_tensor([torch.tensor([-1.0, 0.0]), torch.tensor([-2.0, 2.0])],
      >>> torch.tensor([0.1, 0.2]), torch.tensor([10, 20]), torch.quint8)
      (tensor([-1.,  0.], size=(2,), dtype=torch.quint8,
          quantization_scheme=torch.per_tensor_affine, scale=0.1, zero_point=10),
          tensor([-2.,  2.], size=(2,), dtype=torch.quint8,
          quantization_scheme=torch.per_tensor_affine, scale=0.2, zero_point=20))
      >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), torch.tensor(0.1), torch.tensor(10), torch.quint8)
      tensor([-1.,  0.,  1.,  2.], size=(4,), dtype=torch.quint8,
         quantization_scheme=torch.per_tensor_affine, scale=0.10, zero_point=10)
  """)
  
  add_docstr(torch.quantize_per_channel,
             r"""
  quantize_per_channel(input, scales, zero_points, axis, dtype) -> Tensor
  
  Converts a float tensor to a per-channel quantized tensor with given scales and zero points.
  
  Arguments:
      input (Tensor): float tensor to quantize
      scales (Tensor): float 1D tensor of scales to use, size should match ``input.size(axis)``
      zero_points (int): integer 1D tensor of offset to use, size should match ``input.size(axis)``
      axis (int): dimension on which apply per-channel quantization
      dtype (:class:`torch.dtype`): the desired data type of returned tensor.
          Has to be one of the quantized dtypes: ``torch.quint8``, ``torch.qint8``, ``torch.qint32``
  
  Returns:
      Tensor: A newly quantized tensor
  
  Example::
  
      >>> x = torch.tensor([[-1.0, 0.0], [1.0, 2.0]])
      >>> torch.quantize_per_channel(x, torch.tensor([0.1, 0.01]), torch.tensor([10, 0]), 0, torch.quint8)
      tensor([[-1.,  0.],
              [ 1.,  2.]], size=(2, 2), dtype=torch.quint8,
             quantization_scheme=torch.per_channel_affine,
             scale=tensor([0.1000, 0.0100], dtype=torch.float64),
             zero_point=tensor([10,  0]), axis=0)
      >>> torch.quantize_per_channel(x, torch.tensor([0.1, 0.01]), torch.tensor([10, 0]), 0, torch.quint8).int_repr()
      tensor([[  0,  10],
              [100, 200]], dtype=torch.uint8)
  """)
  
  
  add_docstr(torch.quantized_batch_norm,
             r"""
  quantized_batch_norm(input, weight=None, bias=None, mean, var, eps, output_scale, output_zero_point) -> Tensor
  
  Applies batch normalization on a 4D (NCHW) quantized tensor.
  
  .. math::
  
          y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
  
  Arguments:
      input (Tensor): quantized tensor
      weight (Tensor): float tensor that corresponds to the gamma, size C
      bias (Tensor):  float tensor that corresponds to the beta, size C
      mean (Tensor): float mean value in batch normalization, size C
      var (Tensor): float tensor for variance, size C
      eps (float): a value added to the denominator for numerical stability.
      output_scale (float): output quantized tensor scale
      output_zero_point (int): output quantized tensor zero_point
  
  Returns:
      Tensor: A quantized tensor with batch normalization applied.
  
  Example::
  
      >>> qx = torch.quantize_per_tensor(torch.rand(2, 2, 2, 2), 1.5, 3, torch.quint8)
      >>> torch.quantized_batch_norm(qx, torch.ones(2), torch.zeros(2), torch.rand(2), torch.rand(2), 0.00001, 0.2, 2)
      tensor([[[[-0.2000, -0.2000],
            [ 1.6000, -0.2000]],
  
           [[-0.4000, -0.4000],
            [-0.4000,  0.6000]]],
  
  
          [[[-0.2000, -0.2000],
            [-0.2000, -0.2000]],
  
           [[ 0.6000, -0.4000],
            [ 0.6000, -0.4000]]]], size=(2, 2, 2, 2), dtype=torch.quint8,
         quantization_scheme=torch.per_tensor_affine, scale=0.2, zero_point=2)
  """)
  
  
  add_docstr(torch.quantized_max_pool1d,
             r"""
  quantized_max_pool1d(input, kernel_size, stride=[], padding=0, dilation=1, ceil_mode=False) -> Tensor
  
  Applies a 1D max pooling over an input quantized tensor composed of several input planes.
  
  Arguments:
      input (Tensor): quantized tensor
      kernel_size (list of int): the size of the sliding window
      stride (``list of int``, optional): the stride of the sliding window
      padding (``list of int``, opttional): padding to be added on both sides, must be >= 0 and <= kernel_size / 2
      dilation (``list of int``, optional): The stride between elements within a sliding window, must be > 0. Default 1
      ceil_mode (bool, optional):  If True, will use ceil instead of floor to compute the output shape.
          Defaults to False.
  
  
  Returns:
      Tensor: A quantized tensor with max_pool1d applied.
  
  Example::
  
      >>> qx = torch.quantize_per_tensor(torch.rand(2, 2), 1.5, 3, torch.quint8)
      >>> torch.quantized_max_pool1d(qx, [2])
      tensor([[0.0000],
              [1.5000]], size=(2, 1), dtype=torch.quint8,
          quantization_scheme=torch.per_tensor_affine, scale=1.5, zero_point=3)
  """)
  
  
  add_docstr(torch.quantized_max_pool2d,
             r"""
  quantized_max_pool2d(input, kernel_size, stride=[], padding=0, dilation=1, ceil_mode=False) -> Tensor
  
  Applies a 2D max pooling over an input quantized tensor composed of several input planes.
  
  Arguments:
      input (Tensor): quantized tensor
      kernel_size (``list of int``): the size of the sliding window
      stride (``list of int``, optional): the stride of the sliding window
      padding (``list of int``, optional): padding to be added on both sides, must be >= 0 and <= kernel_size / 2
      dilation (``list of int``, optional): The stride between elements within a sliding window, must be > 0. Default 1
      ceil_mode (bool, optional):  If True, will use ceil instead of floor to compute the output shape.
          Defaults to False.
  
  
  Returns:
      Tensor: A quantized tensor with max_pool2d applied.
  
  Example::
  
      >>> qx = torch.quantize_per_tensor(torch.rand(2, 2, 2, 2), 1.5, 3, torch.quint8)
      >>> torch.quantized_max_pool2d(qx, [2,2])
      tensor([[[[1.5000]],
  
              [[1.5000]]],
  
  
              [[[0.0000]],
  
              [[0.0000]]]], size=(2, 2, 1, 1), dtype=torch.quint8,
          quantization_scheme=torch.per_tensor_affine, scale=1.5, zero_point=3)
  """)
  
  
  add_docstr(torch.Generator,
             r"""
  Generator(device='cpu') -> Generator
  
  Creates and returns a generator object that manages the state of the algorithm which
  produces pseudo random numbers. Used as a keyword argument in many :ref:`inplace-random-sampling`
  functions.
  
  Arguments:
      device (:class:`torch.device`, optional): the desired device for the generator.
  
  Returns:
      Generator: An torch.Generator object.
  
  Example::
  
      >>> g_cpu = torch.Generator()
      >>> g_cuda = torch.Generator(device='cuda')
  """)
  
  
  add_docstr(torch.Generator.set_state,
             r"""
  Generator.set_state(new_state) -> void
  
  Sets the Generator state.
  
  Arguments:
      new_state (torch.ByteTensor): The desired state.
  
  Example::
  
      >>> g_cpu = torch.Generator()
      >>> g_cpu_other = torch.Generator()
      >>> g_cpu.set_state(g_cpu_other.get_state())
  """)
  
  
  add_docstr(torch.Generator.get_state,
             r"""
  Generator.get_state() -> Tensor
  
  Returns the Generator state as a ``torch.ByteTensor``.
  
  Returns:
      Tensor: A ``torch.ByteTensor`` which contains all the necessary bits
      to restore a Generator to a specific point in time.
  
  Example::
  
      >>> g_cpu = torch.Generator()
      >>> g_cpu.get_state()
  """)
  
  
  add_docstr(torch.Generator.manual_seed,
             r"""
  Generator.manual_seed(seed) -> Generator
  
  Sets the seed for generating random numbers. Returns a `torch.Generator` object.
  It is recommended to set a large seed, i.e. a number that has a good balance of 0
  and 1 bits. Avoid having many 0 bits in the seed.
  
  Arguments:
      seed (int): The desired seed. Value must be within the inclusive range
          `[-0x8000_0000_0000_0000, 0xffff_ffff_ffff_ffff]`. Otherwise, a RuntimeError
          is raised. Negative inputs are remapped to positive values with the formula
          `0xffff_ffff_ffff_ffff + seed`.
  
  Returns:
      Generator: An torch.Generator object.
  
  Example::
  
      >>> g_cpu = torch.Generator()
      >>> g_cpu.manual_seed(2147483647)
  """)
  
  
  add_docstr(torch.Generator.initial_seed,
             r"""
  Generator.initial_seed() -> int
  
  Returns the initial seed for generating random numbers.
  
  Example::
  
      >>> g_cpu = torch.Generator()
      >>> g_cpu.initial_seed()
      2147483647
  """)
  
  
  add_docstr(torch.Generator.seed,
             r"""
  Generator.seed() -> int
  
  Gets a non-deterministic random number from std::random_device or the current
  time and uses it to seed a Generator.
  
  Example::
  
      >>> g_cpu = torch.Generator()
      >>> g_cpu.seed()
      1516516984916
  """)
  
  
  add_docstr(torch.Generator.device,
             r"""
  Generator.device -> device
  
  Gets the current device of the generator.
  
  Example::
  
      >>> g_cpu = torch.Generator()
      >>> g_cpu.device
      device(type='cpu')
  """)
  
  add_docstr(torch._assert_async,
             r"""
  _assert_async(tensor) -> void
  
  Asynchronously assert that the contents of tensor are nonzero.  For CPU tensors,
  this is equivalent to ``assert tensor`` or ``assert tensor.is_nonzero()``; for
  CUDA tensors, we DO NOT synchronize and you may only find out the assertion
  failed at a later CUDA kernel launch.  Asynchronous assertion can be helpful for
  testing invariants in CUDA tensors without giving up performance.  This function
  is NOT intended to be used for regular error checking, as it will trash your CUDA
  context if the assert fails (forcing you to restart your PyTorch process.)
  
  Args:
      tensor (Tensor): a one element tensor to test to see if it is nonzero.  Zero
          elements (including False for boolean tensors) cause an assertion failure
          to be raised.
  """)
  
  add_docstr(torch.searchsorted,
             r"""
  searchsorted(sorted_sequence, values, *, out_int32=False, right=False, out=None) -> Tensor
  
  Find the indices from the *innermost* dimension of :attr:`sorted_sequence` such that, if the
  corresponding values in :attr:`values` were inserted before the indices, the order of the
  corresponding *innermost* dimension within :attr:`sorted_sequence` would be preserved.
  Return a new tensor with the same size as :attr:`values`. If :attr:`right` is False (default),
  then the left boundary of :attr:`sorted_sequence` is closed. More formally, the returned index
  satisfies the following rules:
  
  .. list-table::
     :widths: 12 10 78
     :header-rows: 1
  
     * - :attr:`sorted_sequence`
       - :attr:`right`
       - *returned index satisfies*
     * - 1-D
       - False
       - ``sorted_sequence[i-1] < values[m][n]...[l][x] <= sorted_sequence[i]``
     * - 1-D
       - True
       - ``sorted_sequence[i-1] <= values[m][n]...[l][x] < sorted_sequence[i]``
     * - N-D
       - False
       - ``sorted_sequence[m][n]...[l][i-1] < values[m][n]...[l][x] <= sorted_sequence[m][n]...[l][i]``
     * - N-D
       - True
       - ``sorted_sequence[m][n]...[l][i-1] <= values[m][n]...[l][x] < sorted_sequence[m][n]...[l][i]``
  
  Args:
      sorted_sequence (Tensor): N-D or 1-D tensor, containing monotonically increasing sequence on the *innermost*
                                dimension.
      values (Tensor or Scalar): N-D tensor or a Scalar containing the search value(s).
  
  Keyword args:
      out_int32 (bool, optional): indicate the output data type. torch.int32 if True, torch.int64 otherwise.
                                  Default value is False, i.e. default output data type is torch.int64.
      right (bool, optional): if False, return the first suitable location that is found. If True, return the
                              last such index. If no suitable index found, return 0 for non-numerical value
                              (eg. nan, inf) or the size of *innermost* dimension within :attr:`sorted_sequence`
                              (one pass the last index of the *innermost* dimension). In other words, if False,
                              gets the lower bound index for each value in :attr:`values` on the corresponding
                              *innermost* dimension of the :attr:`sorted_sequence`. If True, gets the upper
                              bound index instead. Default value is False.
      out (Tensor, optional): the output tensor, must be the same size as :attr:`values` if provided.
  
  .. note:: If your use case is always 1-D sorted sequence, :func:`torch.bucketize` is preferred,
            because it has fewer dimension checks resulting in slightly better performance.
  
  
  Example::
  
      >>> sorted_sequence = torch.tensor([[1, 3, 5, 7, 9], [2, 4, 6, 8, 10]])
      >>> sorted_sequence
      tensor([[ 1,  3,  5,  7,  9],
              [ 2,  4,  6,  8, 10]])
      >>> values = torch.tensor([[3, 6, 9], [3, 6, 9]])
      >>> values
      tensor([[3, 6, 9],
              [3, 6, 9]])
      >>> torch.searchsorted(sorted_sequence, values)
      tensor([[1, 3, 4],
              [1, 2, 4]])
      >>> torch.searchsorted(sorted_sequence, values, right=True)
      tensor([[2, 3, 5],
              [1, 3, 4]])
  
      >>> sorted_sequence_1d = torch.tensor([1, 3, 5, 7, 9])
      >>> sorted_sequence_1d
      tensor([1, 3, 5, 7, 9])
      >>> torch.searchsorted(sorted_sequence_1d, values)
      tensor([[1, 3, 4],
              [1, 3, 4]])
  """)
  
  add_docstr(torch.bucketize,
             r"""
  bucketize(input, boundaries, *, out_int32=False, right=False, out=None) -> Tensor
  
  Returns the indices of the buckets to which each value in the :attr:`input` belongs, where the
  boundaries of the buckets are set by :attr:`boundaries`. Return a new tensor with the same size
  as :attr:`input`. If :attr:`right` is False (default), then the left boundary is closed. More
  formally, the returned index satisfies the following rules:
  
  .. list-table::
     :widths: 15 85
     :header-rows: 1
  
     * - :attr:`right`
       - *returned index satisfies*
     * - False
       - ``boundaries[i-1] < input[m][n]...[l][x] <= boundaries[i]``
     * - True
       - ``boundaries[i-1] <= input[m][n]...[l][x] < boundaries[i]``
  
  Args:
      input (Tensor or Scalar): N-D tensor or a Scalar containing the search value(s).
      boundaries (Tensor): 1-D tensor, must contain a monotonically increasing sequence.
  
  Keyword args:
      out_int32 (bool, optional): indicate the output data type. torch.int32 if True, torch.int64 otherwise.
                                  Default value is False, i.e. default output data type is torch.int64.
      right (bool, optional): if False, return the first suitable location that is found. If True, return the
                              last such index. If no suitable index found, return 0 for non-numerical value
                              (eg. nan, inf) or the size of :attr:`boundaries` (one pass the last index).
                              In other words, if False, gets the lower bound index for each value in :attr:`input`
                              from :attr:`boundaries`. If True, gets the upper bound index instead.
                              Default value is False.
      out (Tensor, optional): the output tensor, must be the same size as :attr:`input` if provided.
  
  
  Example::
  
      >>> boundaries = torch.tensor([1, 3, 5, 7, 9])
      >>> boundaries
      tensor([1, 3, 5, 7, 9])
      >>> v = torch.tensor([[3, 6, 9], [3, 6, 9]])
      >>> v
      tensor([[3, 6, 9],
              [3, 6, 9]])
      >>> torch.bucketize(v, boundaries)
      tensor([[1, 3, 4],
              [1, 3, 4]])
      >>> torch.bucketize(v, boundaries, right=True)
      tensor([[2, 3, 5],
              [2, 3, 5]])
  """)
  