code: def get_call(method_name, func_type, args, kwargs):
      kwargs_str = ", ".join(
          [k + "=" + value_to_literal(v) for k, v in kwargs.items()]
      )
      self_arg = args[0]
      if func_type == "method":
          args = args[1:]
  
      argument_str = ", ".join(args)
      argument_str += ", " if len(args) and len(kwargs) else ""
      argument_str += kwargs_str
  
      if func_type == "functional" or func_type == "function":
          call = f"torch.{method_name}({argument_str})"
      elif func_type == "method":
          call = f"{self_arg}.{method_name}({argument_str})"
      elif func_type == "nn_functional":
          call = f"torch.nn.functional.{method_name}({argument_str})"
      else:
          raise TypeError("Unsupported function type")
  
      return call
  
imports: from torch.jit.annotations import BroadcastingList2, BroadcastingList3
  import torch.nn.functional
  import torch
  import torch.cuda
  import torch.jit
  import torch.jit._logging
  import torch.jit.frontend
  from torch.testing._internal.common_nn import module_tests, new_module_tests
  from torch.testing._internal.common_utils import is_iterable_of_tensors, noncontiguous_like
  import collections
  from copy import deepcopy
  from typing import Any, Dict, List, Union
  import math
  from torch import inf
label_desc: Use image node_id 4 for calling a global function with example usage: get_call(method_name, func_type, args, kwargs) and returns: call