class_docstr: Groups wrapped layer call functions.
  
  This is used to ensure that all layer call functions are traced with the same
  inputs-
    - call
    - call_and_return_conditional_losses
    - call_and_return_all_conditional_losses
class_docstr_tok: ["Groups","wrapped","layer","call","functions",".","This","is","used","to","ensure","that","all","layer","call","functions","are","traced","with","the","same","inputs-","-","call","-","call_and_return_conditional_losses","-","call_and_return_all_conditional_losses"]
code: def get_training_arg_value(self, args, kwargs):
      if (
          not self.layer._expects_training_arg
          and self._expects_training_arg
      ):  # pylint: disable=protected-access
          return utils.get_training_arg(
              self._training_arg_index, args, kwargs
          )
      else:
          return self.layer._get_call_arg_value(  # pylint: disable=protected-access
              "training", args, kwargs, inputs_in_args=True
          )
  
imports: import functools
  import threading
  import weakref
  from tensorflow.python.eager import def_function
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.keras import backend
  from tensorflow.python.keras.engine import base_layer_utils
  from tensorflow.python.keras.engine import input_spec
  from tensorflow.python.keras.mixed_precision import autocast_variable
  from tensorflow.python.keras.saving import saving_utils
  from tensorflow.python.keras.saving.saved_model import constants
  from tensorflow.python.keras.saving.saved_model import load
  from tensorflow.python.keras.saving.saved_model import serialized_attributes
  from tensorflow.python.keras.saving.saved_model import utils
  from tensorflow.python.keras.utils import tf_contextlib
  from tensorflow.python.keras.utils import tf_inspect
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.python.keras.utils import version_utils
  from tensorflow.python.keras.utils.generic_utils import LazyLoader
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.trackable import data_structures
  from tensorflow.python.util import nest
  from tensorflow.python.util import tf_decorator
label_desc: Use image node_id 6 for calling the LayerCallCollection obj's underlying member method code with example usage: obj.get_training_arg_value(args, kwargs) and returns: utils, self