code: def __init__(self, n_state, config, **kwargs):
      super().__init__(**kwargs)
      nx = config.n_embd
      self.c_fc = TFConv1D(
          n_state,
          nx,
          initializer_range=config.initializer_range,
          name="c_fc",
      )
      self.c_proj = TFConv1D(
          nx,
          n_state,
          initializer_range=config.initializer_range,
          name="c_proj",
      )
      self.act = get_tf_activation(config.activation_function)
      self.dropout = tf.keras.layers.Dropout(config.resid_pdrop)
      self.intermediate_size = n_state
      self.embed_dim = nx
  
imports: from __future__ import annotations
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...activations_tf import get_tf_activation
  from ...modeling_tf_outputs import TFBaseModelOutputWithPastAndCrossAttentions, TFCausalLMOutputWithCrossAttentions, TFSequenceClassifierOutputWithPast
  from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFConv1D, TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, TFSequenceSummary, get_initializer, keras_serializable, unpack_inputs
  from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
  from ...utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_gpt2 import GPT2Config
label_desc: Use image node_id 1 to create a new TFMLP object from inherited base classes: tf.keras.layers with example: obj = TFMLP(n_state, config)