class_docstr: Transformer decoder consisting of *config.num_layers* layers. Each layer is a [`TFXGLMDecoderLayer`]
  
  Args:
      config: XGLMConfig
      embed_tokens: [TFSharedEmbeddings]: output embedding
class_docstr_tok: ["Transformer","decoder","consisting","of","*","config.num_layers","*","layers",".","Each","layer","is","a","[","`","TFXGLMDecoderLayer","`","]","Args",":","config",":","XGLMConfig","embed_tokens",":","[","TFSharedEmbeddings","]",":","output","embedding"]
code: def build(self, input_shape=None):
      if self.built:
          return
      self.built = True
      if getattr(self, "model", None) is not None:
          with tf.name_scope(self.model.name):
              self.model.build(None)
  
imports: from __future__ import annotations
  import math
  import random
  from typing import Any, Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...activations_tf import get_tf_activation
  from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from ...modeling_tf_outputs import TFBaseModelOutputWithPastAndCrossAttentions, TFCausalLMOutputWithCrossAttentions
  from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, TFSharedEmbeddings, get_initializer, keras_serializable, unpack_inputs
  from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
  from ...utils import logging
  from .configuration_xglm import XGLMConfig
label_desc: Use image node_id 3 for calling the TFXGLMModel obj's underlying member method code with example usage: obj.build(input_shape) without return types