docstr:     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).
  
  Returns:
      :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
      loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
          Classification (or regression if config.num_labels==1) loss.
      logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
          Classification (or regression if config.num_labels==1) scores (before SoftMax).
      hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
          Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
          of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
          Hidden-states of the model at the output of each layer plus the initial embedding outputs.
      attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
          Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
          :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
          Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
          heads.
      highway_exits (:obj:`tuple(tuple(torch.Tensor))`:
          Tuple of each early exit's results (total length: number of layers)
          Each tuple is again, a tuple of length 2 - the first entry is logits and the second entry is hidden states.
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code: 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_layer=-1,
      train_highway=False,
  ):
      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).
  
      Returns:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
              Classification (or regression if config.num_labels==1) loss.
          logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
              Classification (or regression if config.num_labels==1) scores (before SoftMax).
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
          highway_exits (:obj:`tuple(tuple(torch.Tensor))`:
              Tuple of each early exit's results (total length: number of layers)
              Each tuple is again, a tuple of length 2 - the first entry is logits and the second entry is hidden states.
      """
  
      exit_layer = self.num_layers
      try:
          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,
          )
          # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
  
          pooled_output = outputs[1]
  
          pooled_output = self.dropout(pooled_output)
          logits = self.classifier(pooled_output)
          outputs = (logits,) + outputs[
              2:
          ]  # add hidden states and attention if they are here
      except HighwayException as e:
          outputs = e.message
          exit_layer = e.exit_layer
          logits = outputs[0]
  
      if not self.training:
          original_entropy = entropy(logits)
          highway_entropy = []
          highway_logits_all = []
      if labels is not None:
          if self.num_labels == 1:
              #  We are doing regression
              loss_fct = MSELoss()
              loss = loss_fct(logits.view(-1), labels.view(-1))
          else:
              loss_fct = CrossEntropyLoss()
              loss = loss_fct(
                  logits.view(-1, self.num_labels), labels.view(-1)
              )
  
          # work with highway exits
          highway_losses = []
          for highway_exit in outputs[-1]:
              highway_logits = highway_exit[0]
              if not self.training:
                  highway_logits_all.append(highway_logits)
                  highway_entropy.append(highway_exit[2])
              if self.num_labels == 1:
                  #  We are doing regression
                  loss_fct = MSELoss()
                  highway_loss = loss_fct(
                      highway_logits.view(-1), labels.view(-1)
                  )
              else:
                  loss_fct = CrossEntropyLoss()
                  highway_loss = loss_fct(
                      highway_logits.view(-1, self.num_labels),
                      labels.view(-1),
                  )
              highway_losses.append(highway_loss)
  
          if train_highway:
              outputs = (sum(highway_losses[:-1]),) + outputs
              # exclude the final highway, of course
          else:
              outputs = (loss,) + outputs
      if not self.training:
          outputs = outputs + (
              (original_entropy, highway_entropy),
              exit_layer,
          )
          if output_layer >= 0:
              outputs = (
                  (outputs[0],)
                  + (highway_logits_all[output_layer],)
                  + outputs[2:]
              )  # use the highway of the last layer
  
      return outputs
  
imports: import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
  from transformers.models.bert.modeling_bert import BERT_INPUTS_DOCSTRING, BERT_START_DOCSTRING, BertEmbeddings, BertLayer, BertPooler, BertPreTrainedModel
label_desc: Use image node_id 2 for calling the DeeBertForSequenceClassification obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, labels, output_layer, train_highway) and returns: outputs