code: def forward(
      self,
      input_ids: Tensor,
      attention_mask: Optional[Tensor] = None,
      token_type_ids: Optional[Tensor] = None,
      inputs_embeds: Optional[Tensor] = None,
      output_attentions: bool = False,
      output_hidden_states: bool = False,
      return_dict: bool = False,
  ) -> Union[BaseModelOutputWithPooling, Tuple[Tensor, ...]]:
      outputs = self.bert_model(
          input_ids=input_ids,
          attention_mask=attention_mask,
          token_type_ids=token_type_ids,
          inputs_embeds=inputs_embeds,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
      )
      sequence_output = outputs[0]
      pooled_output = sequence_output[:, 0, :]
  
      if self.projection_dim > 0:
          pooled_output = self.encode_proj(pooled_output)
  
      if not return_dict:
          return (sequence_output, pooled_output) + outputs[2:]
  
      return BaseModelOutputWithPooling(
          last_hidden_state=sequence_output,
          pooler_output=pooled_output,
          hidden_states=outputs.hidden_states,
          attentions=outputs.attentions,
      )
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  import torch
  from torch import Tensor, nn
  from ...modeling_outputs import BaseModelOutputWithPooling
  from ...modeling_utils import PreTrainedModel
  from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from ..bert.modeling_bert import BertEncoder, BertModel
  from .configuration_dpr import DPRConfig
label_desc: Use image node_id 2 for calling the DPREncoder obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, token_type_ids, inputs_embeds, output_attentions, output_hidden_states, return_dict) and returns: BaseModelOutputWithPooling, unknown