      pixel_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Last hidden states (final feature map) of the last stage of the pixel decoder model (FPN).
        transformer_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Last hidden states (final feature map) of the last stage of the transformer decoder model.
        encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
            shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the encoder
            model at the output of each stage.
        pixel_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
            shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the pixel
            decoder model at the output of each stage.
        transformer_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the
            transformer decoder at the output of each stage.
        hidden_states `tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` containing `encoder_hidden_states`, `pixel_decoder_hidden_states` and
            `decoder_hidden_states`
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`. Attentions weights from Detr's decoder after the attention softmax, used to compute the
            weighted average in the self-attention heads.
    Nr(