code: def load_graph_and_inputs(ir: str) -> Tuple[Any, List[Any]]:
      graph = torch._C.parse_ir(ir, parse_tensor_constants=True)
      graph.makeMultiOutputIntoTuple()
      inputs = []
      for inp in graph.inputs():
          if isinstance(inp.type(), torch._C.FloatType):
              inputs.append(random.uniform(0.1, 100))
          elif isinstance(inp.type(), torch._C.IntType):
              inputs.append(random.randint(1, 100))
          elif isinstance(inp.type(), torch._C.TensorType):
              tensorType = cast(torch._C.TensorType, inp.type())
              inputs.append(make_tensor_from_type(tensorType))
          elif isinstance(inp.type(), torch._C.BoolType):
              inputs.append(random.randint(0, 1) == 1)
          else:
              raise NotImplementedError(
                  f"A default value is not implemented for type {inp.type()}"
              )
  
      func = torch._C._create_function_from_graph("forward", graph)
      torch._C._jit_pass_erase_shape_information(func.graph)
      return (func, inputs)
  
file_path: pytorch/torch/utils/jit/log_extract.py
imports: from contextlib import contextmanager
  from typing import Any, List, Tuple, cast
  import random
  import torch
  import time
  from torch.utils.benchmark import Timer
label_desc: Use image node_id 3 for calling a global function with example usage: load_graph_and_inputs(ir) and returns: func,  inputs