code: def __init__(self, config, *inputs, **kwargs):
      super().__init__(config, *inputs, **kwargs)
      self.transformer = TFGPTJMainLayer(config, name="transformer")
      self.lm_head = tf.keras.layers.Dense(
          config.vocab_size,
          kernel_initializer=get_initializer(config.initializer_range),
          name="lm_head",
      )
  
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 1 to create a new TFGPTJForCausalLM object from inherited base classes: TFGPTJPreTrainedModel, TFCausalLanguageModelingLoss with example: obj = TFGPTJForCausalLM(config)