code: def atleast_3d(*tensors):
      r"""
      Returns a 3-dimensional view of each input tensor with zero dimensions.
      Input tensors with three or more dimensions are returned as-is.
  
      Args:
          input (Tensor or list of Tensors)
  
      Returns:
          output (Tensor or tuple of Tensors)
  
      Example:
  
          >>> x = torch.tensor(0.5)
          >>> x
          tensor(0.5000)
          >>> torch.atleast_3d(x)
          tensor([[[0.5000]]])
          >>> y = torch.arange(4).view(2, 2)
          >>> y
          tensor([[0, 1],
                  [2, 3]])
          >>> torch.atleast_3d(y)
          tensor([[[0],
                   [1]],
                  <BLANKLINE>
                  [[2],
                   [3]]])
          >>> x = torch.tensor(1).view(1, 1, 1)
          >>> x
          tensor([[[1]]])
          >>> torch.atleast_3d(x)
          tensor([[[1]]])
          >>> x = torch.tensor(0.5)
          >>> y = torch.tensor(1.)
          >>> torch.atleast_3d((x, y))
          (tensor([[[0.5000]]]), tensor([[[1.]]]))
      """
      # This wrapper exists to support variadic args.
      if has_torch_function(tensors):
          return handle_torch_function(atleast_3d, tensors, *tensors)
      if len(tensors) == 1:
          tensors = tensors[0]
      return _VF.atleast_3d(tensors)
  
imports: from typing import List, Tuple, Optional, Union, Any, Sequence, TYPE_CHECKING
  import operator
  import itertools
  import torch
  from torch._C import _add_docstr
  import torch.nn.functional
  from ._lowrank import svd_lowrank, pca_lowrank
  from .overrides import has_torch_function, has_torch_function_unary, has_torch_function_variadic, handle_torch_function
  from ._jit_internal import boolean_dispatch
  from ._jit_internal import _overload
  from torch import _VF
label_desc: Use image node_id 21 for calling a global function with example usage: atleast_3d() and returns: _VF, handle_torch_function