code: def sample_inputs_mse_loss(
      op_info, device, dtype, requires_grad, **kwargs
  ):
      def make_input(shape, requires_grad=requires_grad):
          return make_tensor(
              shape,
              device=device,
              dtype=dtype,
              requires_grad=requires_grad,
          )
  
      rhs_requires_grad = kwargs.get("rhs_requires_grad", requires_grad)
      S = 5
  
      shapes = ((S, S), (S, S, S), (S, S, S, S))
      reductions = ("none", "mean", "sum")
  
      for shape, reduction in itertools.product(shapes, reductions):
          yield SampleInput(
              make_input(shape),
              args=(
                  make_input(shape, requires_grad=rhs_requires_grad),
              ),
              kwargs={"reduction": reduction},
          )
  
file_path: pytorch/test/functorch/functorch_additional_op_db.py
imports: from functools import partial
  import itertools
  import unittest
  import torch
  from torch.testing._internal.common_dtype import floating_types, floating_types_and, all_types_and_complex_and
  from torch.testing._internal.common_utils import make_tensor
  from torch.testing._internal.common_methods_invocations import OpInfo, SampleInput, DecorateInfo
label_desc: Use image node_id 3 for calling a global function with example usage: sample_inputs_mse_loss(op_info, device, dtype, requires_grad) and returns: make_tensor