class_docstr: An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
  models.
class_docstr_tok: ["An","abstract","class","to","handle","weights","initialization","and","a","simple","interface","for","downloading","and","loading","pretrained","models","."]
code: def serving(self, inputs):
      output = self.call(inputs)
  
      return self.serving_output(output)
  
imports: from typing import Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...activations_tf import get_tf_activation
  from ...file_utils import DUMMY_INPUTS, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
  from ...modeling_tf_outputs import TFBaseModelOutputWithPast, TFCausalLMOutputWithPast, TFQuestionAnsweringModelOutput, TFSequenceClassifierOutputWithPast
  from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, TFQuestionAnsweringLoss, TFSequenceClassificationLoss, TFSharedEmbeddings, get_initializer, keras_serializable, unpack_inputs
  from ...tf_utils import shape_list, stable_softmax
  from ...utils import logging
  from .configuration_gptj import GPTJConfig
label_desc: Use image node_id 2 for calling the TFGPTJPreTrainedModel obj's underlying member method code with example usage: obj.serving(inputs) and returns: self