def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        start_positions=None,
        end_positions=None,
        threshold=None,
    ):
        r"""
            start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
                Labels for position (index) of the start of the labelled span for computing the token classification loss.
                Positions are clamped to the length of the sequence (`sequence_length`).
                Position outside of the sequence are not taken into account for computing the loss.
            end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
                Labels for position (index) of the end of the labelled span for computing the token classification loss.
                Positions are clamped to the length of the sequence (`sequence_length`).
                Position outside of the sequence are not taken into account for computing the loss.
            threshold (:obj:`float`):
                Threshold value (see :class:`~emmental.MaskedLinear`).

        Returns:
            :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~emmental.MaskedBertConfig`) and inputs:
            loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
                Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
            start_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
                Span-start scores (before SoftMax).
            end_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
                Span-end scores (before SoftMax).
            hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned 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 ``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.
        """

        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,
            threshold=threshold,
        )

        sequence_output = outputs[0]

        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1)
        end_logits = end_logits.squeeze(-1)

        outputs = (
            start_logits,
            end_logits,
        ) + outputs[2:]
        if start_positions is not None and end_positions is not None:
            # If we are on multi-GPU, split add a dimension
            if len(start_positions.size()) > 1:
                start_positions = start_positions.squeeze(-1)
            if len(end_positions.size()) > 1:
                end_positions = end_positions.squeeze(-1)
            # sometimes the start/end positions are outside our model inputs, we ignore these terms
            ignored_index = start_logits.size(1)
            start_positions.clamp_(0, ignored_index)
            end_positions.clamp_(0, ignored_index)

            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2
            outputs = (total_loss,) + outputs

        return outputs  # (loss), start_logits, end_logits, (hidden_states), (attentions)

            start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
                Labels for position (index) of the start of the labelled span for computing the token classification loss.
                Positions are clamped to the length of the sequence (`sequence_length`).
                Position outside of the sequence are not taken into account for computing the loss.
            end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
                Labels for position (index) of the end of the labelled span for computing the token classification loss.
                Positions are clamped to the length of the sequence (`sequence_length`).
                Position outside of the sequence are not taken into account for computing the loss.
            threshold (:obj:`float`):
                Threshold value (see :class:`~emmental.MaskedLinear`).

        Returns:
            :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~emmental.MaskedBertConfig`) and inputs:
            loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
                Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
            start_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
                Span-start scores (before SoftMax).
            end_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
                Span-end scores (before SoftMax).
            hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned 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 ``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.
        
dpkd/transformers/examples/research_projects/movement-pruning/emmental/modeling_bert_masked.py