code: def is_valid_inplace_sample_input(sample_input, op, inplace_variant):
      if inplace_variant is None:
          return False
      if sample_input.broadcasts_input:
          return False
      if not isinstance(sample_input.input, torch.Tensor):
          return False
  
      # Check if input's dtype matches the output's dtype
      args = (sample_input.input,) + sample_input.args
      kwargs = sample_input.kwargs
      output_dtype = op(*args, **kwargs).dtype
      return sample_input.input.dtype == output_dtype
  
imports: import itertools
  import torch
  from functorch import vmap
  import torch.utils._pytree
  from functorch_additional_op_db import additional_op_db
  from torch.testing._internal.common_methods_invocations import DecorateInfo
  from torch.testing._internal.common_methods_invocations import op_db
  from torch.testing._internal.common_modules import module_db
  import os
  import unittest
  from torch.testing._internal.common_device_type import toleranceOverride
  from torch.testing._internal.autograd_function_db import autograd_function_db
  from collections import namedtuple
label_desc: Use image node_id 3 for calling a global function with example usage: is_valid_inplace_sample_input(sample_input, op, inplace_variant) and returns: unknown, False, False, False