def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        mc_token_ids: Optional[torch.LongTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        mc_labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], OpenAIGPTDoubleHeadsModelOutput]:
        r"""
        mc_token_ids (`torch.LongTensor` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input):
            Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) -
            1]`.
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
            `labels = input_ids` Indices are selected in `[-1, 0, ..., config.vocab_size]` All labels set to `-100` are
            ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
        mc_labels (`torch.LongTensor` of shape `(batch_size)`, *optional*):
            Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
            where *num_choices* is the size of the second dimension of the input tensors. (see *input_ids* above)

        Return:

        Examples:

        ```python
        >>> from transformers import AutoTokenizer, OpenAIGPTDoubleHeadsModel
        >>> import torch

        >>> tokenizer = AutoTokenizer.from_pretrained("openai-gpt")
        >>> model = OpenAIGPTDoubleHeadsModel.from_pretrained("openai-gpt")
        >>> tokenizer.add_special_tokens(
        ...     {"cls_token": "[CLS]"}
        ... )  # Add a [CLS] to the vocabulary (we should train it also!)
        >>> model.resize_token_embeddings(len(tokenizer))

        >>> choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
        >>> input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0)  # Batch size 1, 2 choices
        >>> mc_token_ids = torch.tensor([input_ids.size(-1) - 1, input_ids.size(-1) - 1]).unsqueeze(0)  # Batch size 1

        >>> outputs = model(input_ids, mc_token_ids=mc_token_ids)
        >>> lm_logits = outputs.logits
        >>> mc_logits = outputs.mc_logits
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        transformer_outputs = self.transformer(
            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,
        )
        hidden_states = transformer_outputs[0]

        lm_logits = self.lm_head(hidden_states)
        mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids).squeeze(-1)

        lm_loss, mc_loss = None, None
        if mc_labels is not None:
            loss_fct = CrossEntropyLoss()
            mc_loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1))
        if labels is not None:
            shift_logits = lm_logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            loss_fct = CrossEntropyLoss()
            lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))

        if not return_dict:
            output = (lm_logits, mc_logits) + transformer_outputs[1:]
            if mc_loss is not None:
                output = (mc_loss,) + output
            return ((lm_loss,) + output) if lm_loss is not None else output

        return OpenAIGPTDoubleHeadsModelOutput(
            loss=lm_loss,
            mc_loss=mc_loss,
            logits=lm_logits,
            mc_logits=mc_logits,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )

        mc_token_ids (`torch.LongTensor` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input):
            Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) -
            1]`.
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
            `labels = input_ids` Indices are selected in `[-1, 0, ..., config.vocab_size]` All labels set to `-100` are
            ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
        mc_labels (`torch.LongTensor` of shape `(batch_size)`, *optional*):
            Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
            where *num_choices* is the size of the second dimension of the input tensors. (see *input_ids* above)

        Return:

        Examples:

        ```python
        >>> from transformers import AutoTokenizer, OpenAIGPTDoubleHeadsModel
        >>> import torch

        >>> tokenizer = AutoTokenizer.from_pretrained("openai-gpt")
        >>> model = OpenAIGPTDoubleHeadsModel.from_pretrained("openai-gpt")
        >>> tokenizer.add_special_tokens(
        ...     {"cls_token": "[CLS]"}
        ... )  # Add a [CLS] to the vocabulary (we should train it also!)
        >>> model.resize_token_embeddings(len(tokenizer))

        >>> choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
        >>> input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0)  # Batch size 1, 2 choices
        >>> mc_token_ids = torch.tensor([input_ids.size(-1) - 1, input_ids.size(-1) - 1]).unsqueeze(0)  # Batch size 1

        >>> outputs = model(input_ids, mc_token_ids=mc_token_ids)
        >>> lm_logits = outputs.logits
        >>> mc_logits = outputs.mc_logits
        ```
dpkd/transformers/src/transformers/models/openai/modeling_openai.py