code: def can_return_loss(model_class):
      """
      Check if a given model can return loss.
  
      Args:
          model_class (`type`): The class of the model.
      """
      framework = infer_framework(model_class)
      if framework == "tf":
          signature = inspect.signature(
              model_class.call
          )  # TensorFlow models
      elif framework == "pt":
          signature = inspect.signature(
              model_class.forward
          )  # PyTorch models
      else:
          signature = inspect.signature(
              model_class.__call__
          )  # Flax models
  
      for p in signature.parameters:
          if (
              p == "return_loss"
              and signature.parameters[p].default is True
          ):
              return True
  
      return False
  
imports: import inspect
  import tempfile
  from collections import OrderedDict, UserDict
  from collections.abc import MutableMapping
  from contextlib import ExitStack, contextmanager
  from dataclasses import fields, is_dataclass
  from enum import Enum
  from typing import Any, ContextManager, Iterable, List, Tuple
  import numpy
  from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
label_desc: Use image node_id 21 for calling a global function with example usage: can_return_loss(model_class) and returns: False, True