h.FloatTensor` of shape `(batch_size, config.num_labels)` Classification (or regression if
        config.num_labels==1) scores (before SoftMax).
    **hidden_states**: (*optional*, returned when `output_hidden_states=True`) list of `torch.FloatTensor` (one for
    the output of each layer + the output of the embeddings) of shape `(batch_size, sequence_length, hidden_size)`:
    Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**:
    (*optional*, returned when `output_attentions=True`) list of `torch.FloatTensor` (one for each layer) of shape
    `(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.

    Examples:

    ```python
    # For example purposes. Not runnable.
    transformer = BertModel.from_pretrained("bert-base-uncased")
    encoder = ImageEncoder(args)
    model = MMBTForClassification(config, transformer, encoder)
    outputs = model(input_modal, input_ids, labels=labels)
    loss, logits = outputs[:2]
    ```c