docstr: start_positions (`np.ndarray` or `tf.Tensor` of shape `(batch_size,)`, *optional*):
      Labels for position (index) of the start of the labelled span for computing the token classification loss.
      Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
      are not taken into account for computing the loss.
  end_positions (`np.ndarray` or `tf.Tensor` of shape `(batch_size,)`, *optional*):
      Labels for position (index) of the end of the labelled span for computing the token classification loss.
      Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
      are not taken into account for computing the loss.
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code: def call(
      self,
      input_ids: TFModelInputType | None = None,
      past_key_values: Optional[
          Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]
      ] = None,
      attention_mask: np.ndarray | tf.Tensor | None = None,
      token_type_ids: np.ndarray | tf.Tensor | None = None,
      position_ids: np.ndarray | tf.Tensor | None = None,
      head_mask: np.ndarray | tf.Tensor | None = None,
      inputs_embeds: np.ndarray | tf.Tensor | None = None,
      start_positions: np.ndarray | tf.Tensor | None = None,
      end_positions: np.ndarray | tf.Tensor | None = None,
      output_attentions: Optional[bool] = None,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
      training: Optional[bool] = False,
  ) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
      r"""
      start_positions (`np.ndarray` or `tf.Tensor` of shape `(batch_size,)`, *optional*):
          Labels for position (index) of the start of the labelled span for computing the token classification loss.
          Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
          are not taken into account for computing the loss.
      end_positions (`np.ndarray` or `tf.Tensor` of shape `(batch_size,)`, *optional*):
          Labels for position (index) of the end of the labelled span for computing the token classification loss.
          Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
          are not taken into account for computing the loss.
      """
  
      transformer_outputs = self.transformer(
          input_ids=input_ids,
          past_key_values=past_key_values,
          attention_mask=attention_mask,
          token_type_ids=token_type_ids,
          position_ids=position_ids,
          head_mask=head_mask,
          inputs_embeds=inputs_embeds,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
          training=training,
      )
      sequence_output = transformer_outputs[0]
  
      logits = self.qa_outputs(sequence_output)
      start_logits, end_logits = tf.split(logits, 2, axis=-1)
      start_logits = tf.squeeze(start_logits, axis=-1)
      end_logits = tf.squeeze(end_logits, axis=-1)
  
      loss = None
      if start_positions is not None and end_positions is not None:
          labels = {"start_position": start_positions}
          labels["end_position"] = end_positions
          loss = self.hf_compute_loss(
              labels, (start_logits, end_logits)
          )
  
      if not return_dict:
          output = (start_logits, end_logits) + transformer_outputs[2:]
          return ((loss,) + output) if loss is not None else output
  
      return TFQuestionAnsweringModelOutput(
          loss=loss,
          start_logits=start_logits,
          end_logits=end_logits,
          hidden_states=transformer_outputs.hidden_states,
          attentions=transformer_outputs.attentions,
      )
  
imports: from __future__ import annotations
  from typing import 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
  from ...modeling_tf_outputs import TFBaseModelOutputWithPast, TFCausalLMOutputWithPast, TFQuestionAnsweringModelOutput, TFSequenceClassifierOutputWithPast
  from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, TFQuestionAnsweringLoss, TFSequenceClassificationLoss, 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_gptj import GPTJConfig
label_desc: Use image node_id 2 for calling the TFGPTJForQuestionAnswering obj's underlying member method code with example usage: obj.call(input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, start_positions, end_positions, output_attentions, output_hidden_states, return_dict, training) and returns: TFQuestionAnsweringModelOutput, unknown