docstr: Return:
  
  Examples:
  
  ```python
  >>> from transformers import TFDPRReader, DPRReaderTokenizer
  
  >>> tokenizer = DPRReaderTokenizer.from_pretrained("facebook/dpr-reader-single-nq-base")
  >>> model = TFDPRReader.from_pretrained("facebook/dpr-reader-single-nq-base", from_pt=True)
  >>> encoded_inputs = tokenizer(
  ...     questions=["What is love ?"],
  ...     titles=["Haddaway"],
  ...     texts=["'What Is Love' is a song recorded by the artist Haddaway"],
  ...     return_tensors="tf",
  ... )
  >>> outputs = model(encoded_inputs)
  >>> start_logits = outputs.start_logits
  >>> end_logits = outputs.end_logits
  >>> relevance_logits = outputs.relevance_logits
  ```
code: def call(
      self,
      input_ids=None,
      attention_mask: Optional[tf.Tensor] = None,
      inputs_embeds: Optional[tf.Tensor] = None,
      output_attentions: bool = None,
      output_hidden_states: bool = None,
      return_dict=None,
      training: bool = False,
  ) -> Union[TFDPRReaderOutput, Tuple[tf.Tensor, ...]]:
      r"""
      Return:
  
      Examples:
  
      ```python
      >>> from transformers import TFDPRReader, DPRReaderTokenizer
  
      >>> tokenizer = DPRReaderTokenizer.from_pretrained("facebook/dpr-reader-single-nq-base")
      >>> model = TFDPRReader.from_pretrained("facebook/dpr-reader-single-nq-base", from_pt=True)
      >>> encoded_inputs = tokenizer(
      ...     questions=["What is love ?"],
      ...     titles=["Haddaway"],
      ...     texts=["'What Is Love' is a song recorded by the artist Haddaway"],
      ...     return_tensors="tf",
      ... )
      >>> outputs = model(encoded_inputs)
      >>> start_logits = outputs.start_logits
      >>> end_logits = outputs.end_logits
      >>> relevance_logits = outputs.relevance_logits
      ```
      """
      if input_ids is not None and inputs_embeds is not None:
          raise ValueError(
              "You cannot specify both input_ids and inputs_embeds at the same time"
          )
      elif input_ids is not None:
          input_shape = shape_list(input_ids)
      elif inputs_embeds is not None:
          input_shape = shape_list(inputs_embeds)[:-1]
      else:
          raise ValueError(
              "You have to specify either input_ids or inputs_embeds"
          )
  
      if attention_mask is None:
          attention_mask = tf.ones(input_shape, dtype=tf.dtypes.int32)
  
      return self.span_predictor(
          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,
      )
  
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 3 for calling the TFDPRReader 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: self