code: def __init__(self, config: GPTJConfig, **kwargs):
      super().__init__(**kwargs)
      inner_dim = (
          config.n_inner
          if config.n_inner is not None
          else 4 * config.n_embd
      )
      self.ln_1 = tf.keras.layers.LayerNormalization(
          epsilon=config.layer_norm_epsilon, name="ln_1"
      )
      self.attn = TFGPTJAttention(config, name="attn")
      self.mlp = TFGPTJMLP(inner_dim, config, name="mlp")
  
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 TFGPTJBlock object from inherited base classes: tf.keras.layers with example: obj = TFGPTJBlock(config)