docstr: labels (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
      Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
      `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
      are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
code: def call(
      self,
      input_ids: TFModelInputType | None = None,
      attention_mask: np.ndarray | tf.Tensor | None = None,
      position_ids: np.ndarray | tf.Tensor | None = None,
      encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
      encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
      head_mask: np.ndarray | tf.Tensor | None = None,
      cross_attn_head_mask: np.ndarray | tf.Tensor | None = None,
      past_key_values: Optional[
          Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]
      ] = None,
      inputs_embeds: np.ndarray | tf.Tensor | None = None,
      labels: np.ndarray | tf.Tensor | None = None,
      use_cache: Optional[bool] = None,
      output_attentions: Optional[bool] = None,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
      training: Optional[bool] = False,
      **kwargs: Any,
  ) -> Union[TFCausalLMOutputWithCrossAttentions, Tuple[tf.Tensor]]:
      r"""
      labels (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
          Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
          `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
          are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
      """
  
      outputs = self.model(
          input_ids=input_ids,
          attention_mask=attention_mask,
          position_ids=position_ids,
          encoder_hidden_states=encoder_hidden_states,
          encoder_attention_mask=encoder_attention_mask,
          head_mask=head_mask,
          cross_attn_head_mask=cross_attn_head_mask,
          past_key_values=past_key_values,
          inputs_embeds=inputs_embeds,
          use_cache=use_cache,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
          training=training,
      )
      hidden_states = outputs[0]
      lm_logits = self.lm_head(hidden_states)
  
      loss = None
      if labels is not None:
          # shift labels to the left and cut last logit token
          labels = tf.concat(
              [
                  labels[:, 1:],
                  tf.fill(
                      (labels.shape[0], 1),
                      tf.cast(self.config.pad_token_id, labels.dtype),
                  ),
              ],
              axis=-1,
          )
          loss = self.hf_compute_loss(labels, lm_logits)
  
      if not return_dict:
          output = (lm_logits,) + outputs[1:]
          return ((loss,) + output) if loss is not None else output
  
      return TFCausalLMOutputWithCrossAttentions(
          loss=loss,
          logits=lm_logits,
          past_key_values=outputs.past_key_values,
          hidden_states=outputs.hidden_states,
          attentions=outputs.attentions,
          cross_attentions=outputs.cross_attentions,
      )
  
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 5 for calling the TFXGLMForCausalLM obj's underlying member method code with example usage: obj.call(input_ids, attention_mask, position_ids, encoder_hidden_states, encoder_attention_mask, head_mask, cross_attn_head_mask, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict, training) and returns: TFCausalLMOutputWithCrossAttentions, unknown