code: def call(
      self,
      input_ids: tf.Tensor = None,
      attention_mask: Optional[tf.Tensor] = None,
      token_type_ids: 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, ...]]:
      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,
      )
      return outputs
  
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 TFDPREncoder obj's underlying member method code with example usage: obj.call(input_ids, attention_mask, token_type_ids, inputs_embeds, output_attentions, output_hidden_states, return_dict, training) and returns: outputs