code: def broadcast_tensors(*tensors):
      r"""broadcast_tensors(*tensors) -> List of Tensors
  
      Broadcasts the given tensors according to :ref:`broadcasting-semantics`.
  
      Args:
          *tensors: any number of tensors of the same type
  
      .. warning::
  
          More than one element of a broadcasted tensor may refer to a single
          memory location. As a result, in-place operations (especially ones that
          are vectorized) may result in incorrect behavior. If you need to write
          to the tensors, please clone them first.
  
      Example::
  
          >>> x = torch.arange(3).view(1, 3)
          >>> y = torch.arange(2).view(2, 1)
          >>> a, b = torch.broadcast_tensors(x, y)
          >>> a.size()
          torch.Size([2, 3])
          >>> a
          tensor([[0, 1, 2],
                  [0, 1, 2]])
      """
      # This wrapper exists to support variadic args.
      if has_torch_function(tensors):
          return handle_torch_function(
              broadcast_tensors, tensors, *tensors
          )
      return _VF.broadcast_tensors(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 1 for calling a global function with example usage: broadcast_tensors() and returns: _VF, handle_torch_function