class_docstr: Bias as a layer. It is used for serialization purposes: `tf.keras.Model.save_weights` stores on a per-layer basis,
  so all weights have to be registered in a layer.
class_docstr_tok: ["Bias","as","a","layer",".","It","is","used","for","serialization","purposes",":","`","tf.keras.Model.save_weights","`","stores","on","a","per-layer","basis",",","so","all","weights","have","to","be","registered","in","a","layer","."]
code: def __init__(self, shape, initializer, trainable, name, **kwargs):
      super().__init__(name=name, **kwargs)
      self.shape = shape
      self.initializer = initializer
      self.trainable = trainable
  
imports: from __future__ import annotations
  from typing import Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...modeling_tf_outputs import TFBaseModelOutputWithPast, TFCausalLMOutputWithPast, TFSequenceClassifierOutput
  from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, get_initializer, keras_serializable, unpack_inputs
  from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_ctrl import CTRLConfig
label_desc: Use image node_id 1 to create a new TFCTRLBiasLayer object from inherited base classes: tf.keras.layers with example: obj = TFCTRLBiasLayer(shape, initializer, trainable, name)