code: def forward(self, x_in: Tuple):
      x, _ = x_in
      # data is of size (batch_size, input_chunk_length, input_size)
      batch_size = x.size(0)
  
      out, hidden = self.rnn(x)
  
      """ Here, we apply the FC network only on the last output point (at the last time step)
          """
      if self.name == "LSTM":
          hidden = hidden[0]
      predictions = hidden[-1, :, :]
      predictions = self.fc(predictions)
      predictions = predictions.view(
          batch_size, self.out_len, self.target_size, self.nr_params
      )
  
      # predictions is of size (batch_size, output_chunk_length, 1)
      return predictions
  
imports: import inspect
  from abc import ABC, abstractmethod
  from typing import List, Optional, Tuple, Type, Union
  import torch
  import torch.nn
  from darts.logging import get_logger, raise_log
  from darts.models.forecasting.pl_forecasting_module import PLPastCovariatesModule, io_processor
  from darts.models.forecasting.torch_forecasting_model import PastCovariatesTorchModel
label_desc: Use image node_id 2 for calling the _BlockRNNModule obj's underlying member method code with example usage: obj.forward(x_in) and returns: predictions