code: def __init__(
      self,
      config: XGLMConfig,
      embed_tokens: Optional[TFSharedEmbeddings] = None,
      *inputs: Any,
      **kwargs: Any,
  ) -> None:
      super().__init__(config, *inputs, **kwargs)
  
      self.model = TFXGLMMainLayer(
          config, embed_tokens=embed_tokens, name="model"
      )
      self.lm_head = tf.keras.layers.Dense(
          config.vocab_size,
          use_bias=False,
          kernel_initializer=get_initializer(config.init_std),
          name="lm_head",
      )
  
      # TODO (Joao): investigate why XGLM has numerical issues in XLA generate
      self.supports_xla_generation = False
  
imports: 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 DUMMY_INPUTS, 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 shape_list, stable_softmax
  from ...utils import logging
  from .configuration_xglm import XGLMConfig
label_desc: Use image node_id 1 to create a new TFXGLMForCausalLM object from inherited base classes: TFXGLMPreTrainedModel, TFCausalLanguageModelingLoss with example: obj = TFXGLMForCausalLM(config, embed_tokens)