text: """
  Implementation borrowed from transformers package and extended to support multiple prediction heads:
  
  https://github.com/huggingface/transformers/blob/master/src/transformers/models/bert/modeling_bert.py
  """
  
  import torch
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  
  import transformers
  from transformers import BertTokenizer
  from transformers import models
  from transformers.modeling_outputs import SequenceClassifierOutput
  
  from transformers.models.bert.configuration_bert import BertConfig
  from transformers.models.bert.modeling_bert import (
      BertPreTrainedModel,
      BERT_INPUTS_DOCSTRING,
      _TOKENIZER_FOR_DOC,
      _CHECKPOINT_FOR_DOC,
      _CONFIG_FOR_DOC,
      BertModel,
  )
  
  from transformers.file_utils import (
      add_code_sample_docstrings,
      add_start_docstrings_to_model_forward,
  )
  
  
  class BertForSequenceClassification(BertPreTrainedModel):
      def __init__(self, config, **kwargs):
          super().__init__(transformers.PretrainedConfig())
          self.num_labels = kwargs.get("task_labels_map", {})
          self.config = config
  
          self.bert = BertModel(config)
          classifier_dropout = (
              config.classifier_dropout
              if config.classifier_dropout is not None
              else config.hidden_dropout_prob
          )
          self.dropout = nn.Dropout(classifier_dropout)
          ## add task specific output heads
          self.classifier1 = nn.Linear(
              config.hidden_size, list(self.num_labels.values())[0]
          )
          self.classifier2 = nn.Linear(
              config.hidden_size, list(self.num_labels.values())[1]
          )
  
          self.init_weights()
  
      @add_start_docstrings_to_model_forward(
          BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")
      )
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=SequenceClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          labels=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          task_name=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
              config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
              If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
          """
          return_dict = (
              return_dict if return_dict is not None else self.config.use_return_dict
          )
  
          outputs = self.bert(
              input_ids,
              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,
          )
  
          pooled_output = outputs[1]
  
          pooled_output = self.dropout(pooled_output)
          logits = None
          if task_name == list(self.num_labels.keys())[0]:
              logits = self.classifier1(pooled_output)
          elif task_name == list(self.num_labels.keys())[1]:
              logits = self.classifier2(pooled_output)
  
          loss = None
          if labels is not None:
              if self.config.problem_type is None:
                  if self.num_labels[task_name] == 1:
                      self.config.problem_type = "regression"
                  elif self.num_labels[task_name] > 1 and (
                      labels.dtype == torch.long or labels.dtype == torch.int
                  ):
                      self.config.problem_type = "single_label_classification"
                  else:
                      self.config.problem_type = "multi_label_classification"
  
              if self.config.problem_type == "regression":
                  loss_fct = MSELoss()
                  if self.num_labels[task_name] == 1:
                      loss = loss_fct(logits.squeeze(), labels.squeeze())
                  else:
                      loss = loss_fct(logits, labels)
              elif self.config.problem_type == "single_label_classification":
                  loss_fct = CrossEntropyLoss()
                  loss = loss_fct(
                      logits.view(-1, self.num_labels[task_name]), labels.view(-1)
                  )
              elif self.config.problem_type == "multi_label_classification":
                  loss_fct = BCEWithLogitsLoss()
                  loss = loss_fct(logits, labels)
          if not return_dict:
              output = (logits,) + outputs[2:]
              return ((loss,) + output) if loss is not None else output
  
          return SequenceClassifierOutput(
              loss=loss,
              logits=logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  