class_docstr: Helper class to transform torch function into torch nn module.
  This helps to keep the testing interface same for torch functional api.
class_docstr_tok: ["Helper","class","to","transform","torch","function","into","torch","nn","module",".","This","helps","to","keep","the","testing","interface","same","for","torch","functional","api","."]
code: def forward(self, *args):
      return self.function(*args, **self.kwargs)
  
imports: import numpy
  import pytest
  import torch
  import torch.nn
  import coremltools
  import coremltools.models.utils
  from coremltools import RangeDim, TensorType
  from coremltools._deps import _HAS_EXECUTORCH, _HAS_TORCH_EXPORT_API, _IS_MACOS
  from coremltools.converters.mil.mil.types.type_mapping import nptype_from_builtin
  from coremltools.converters.mil.testing_utils import ct_convert, validate_minimum_deployment_target
  from ..ops import TorchFrontend
  from ..torchscript_utils import torch_to_mil_types
label_desc: Use image node_id 2 for calling the ModuleWrapper obj's underlying member method code with example usage: obj.forward() and returns: self