code: def cartesian_prod(*tensors: Tensor) -> Tensor:
      """Do cartesian product of the given sequence of tensors. The behavior is similar to
      python's `itertools.product`.
  
      Args:
          *tensors: any number of 1 dimensional tensors.
  
      Returns:
          Tensor: A tensor equivalent to converting all the input tensors into lists,
          do `itertools.product` on these lists, and finally convert the resulting list
          into tensor.
  
      Example::
  
          >>> import itertools
          >>> a = [1, 2, 3]
          >>> b = [4, 5]
          >>> list(itertools.product(a, b))
          [(1, 4), (1, 5), (2, 4), (2, 5), (3, 4), (3, 5)]
          >>> tensor_a = torch.tensor(a)
          >>> tensor_b = torch.tensor(b)
          >>> torch.cartesian_prod(tensor_a, tensor_b)
          tensor([[1, 4],
                  [1, 5],
                  [2, 4],
                  [2, 5],
                  [3, 4],
                  [3, 5]])
      """
      # This wrapper exists to support variadic args.
      if has_torch_function(tensors):
          return handle_torch_function(
              cartesian_prod, tensors, *tensors
          )
      return _VF.cartesian_prod(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 16 for calling a global function with example usage: cartesian_prod() and returns: _VF, handle_torch_function