code: def ones_like(tensor, dtype=None):
      """Returns a tensor of all ones with the same shape and dtype
      as the input tensor.
  
      Args:
          tensor (tensor_like): input tensor
          dtype (str, np.dtype, None): The desired output datatype of the array. If not provided, the dtype of
              ``tensor`` is used. This argument can be any supported NumPy dtype representation, including
              a string (``"float64"``), a ``np.dtype`` object (``np.dtype("float64")``), or
              a dtype class (``np.float64``). If ``tensor`` is not a NumPy array, the
              **equivalent** dtype in the dispatched framework is used.
  
      Returns:
          tensor_like: an all-ones tensor with the same shape and
          size as ``tensor``
  
      **Example**
  
      >>> x = torch.tensor([1., 2.])
      >>> ones_like(x)
      tensor([1, 1])
      >>> y = tf.Variable([[0], [5]])
      >>> ones_like(y, dtype=np.complex128)
      <tf.Tensor: shape=(2, 1), dtype=complex128, numpy=
      array([[1.+0.j],
             [1.+0.j]])>
      """
      if dtype is not None:
          return cast(np.ones_like(tensor), dtype)
  
      return np.ones_like(tensor)
  
imports: import functools
  from collections.abc import Sequence
  import autoray
  import numpy
  from autograd.numpy.numpy_boxes import ArrayBox
  from autoray import numpy
  from numpy import ndarray
  from .None import single_dispatch
  from .utils import cast, cast_like, get_interface, requires_grad
label_desc: Use image node_id 12 for calling a global function with example usage: ones_like(tensor, dtype) and returns: np, cast