code: def call(
      self,
      input_ids: tf.Tensor = None,
      attention_mask: Optional[tf.Tensor] = None,
      inputs_embeds: Optional[tf.Tensor] = None,
      output_attentions: bool = False,
      output_hidden_states: bool = False,
      return_dict: bool = False,
      training: bool = False,
  ) -> Union[TFDPRReaderOutput, Tuple[tf.Tensor, ...]]:
      # notations: N - number of questions in a batch, M - number of passages per questions, L - sequence length
      n_passages, sequence_length = (
          shape_list(input_ids)
          if input_ids is not None
          else shape_list(inputs_embeds)[:2]
      )
      # feed encoder
      outputs = self.encoder(
          input_ids=input_ids,
          attention_mask=attention_mask,
          inputs_embeds=inputs_embeds,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
          training=training,
      )
      sequence_output = outputs[0]
  
      # compute logits
      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)
      relevance_logits = self.qa_classifier(sequence_output[:, 0, :])
  
      # resize
      start_logits = tf.reshape(
          start_logits, [n_passages, sequence_length]
      )
      end_logits = tf.reshape(end_logits, [n_passages, sequence_length])
      relevance_logits = tf.reshape(relevance_logits, [n_passages])
  
      if not return_dict:
          return (start_logits, end_logits, relevance_logits) + outputs[
              2:
          ]
  
      return TFDPRReaderOutput(
          start_logits=start_logits,
          end_logits=end_logits,
          relevance_logits=relevance_logits,
          hidden_states=outputs.hidden_states,
          attentions=outputs.attentions,
      )
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  import tensorflow
  from ...modeling_tf_outputs import TFBaseModelOutputWithPooling
  from ...modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list, unpack_inputs
  from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from ..bert.modeling_tf_bert import TFBertMainLayer
  from .configuration_dpr import DPRConfig
label_desc: Use image node_id 2 for calling the TFDPRSpanPredictorLayer obj's underlying member method code with example usage: obj.call(input_ids, attention_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict, training) and returns: TFDPRReaderOutput, unknown