      weighted average in the cross-attention heads.
        encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Sequence of hidden-states at the output of the last layer of the encoder of the 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, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
        encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the
            self-attention heads.
        loc (`torch.FloatTensor` of shape `(batch_size,)` or `(batch_size, input_size)`, *optional*):
            Shift values of each time series' context window which is used to give the model inputs of the same
            magnitude and then used to shift back to the original magnitude.
        scale (`torch.FloatTensor` of shape `(batch_size,)` or `(batch_size, input_size)`, *optional*):
            Scaling values of each time series' context window which is used to give the model inputs of the same
            magnitude and then used to rescale back to the original magnitude.
        static_features (`torch.FloatTensor` of shape `(batch_size, feature size)`, *optional*):
            Static features of each time series' in a batch which are copied to the covariates at inference time.
    Nr!