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 build(self, input_shape):
      self.bias = self.add_weight(
          name="bias",
          shape=self.shape,
          initializer=self.initializer,
          trainable=self.trainable,
      )
      super().build(input_shape)
  
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 2 for calling the TFCTRLBiasLayer obj's underlying member method code with example usage: obj.build(input_shape) without return types