code: def forward(self, x):
      batch_size = x.shape[0]
  
      # fully connected layer stack
      for layer in self.linear_layer_stack_list:
          x = self.activation(layer(x))
  
      # forked linear layers producing waveform generator parameters
      theta_backcast = self.backcast_linear_layer(x)
      theta_forecast = self.forecast_linear_layer(x)
  
      # set the expansion coefs in last dimension for the forecasts
      theta_forecast = theta_forecast.view(
          batch_size, self.nr_params, -1
      )
  
      # waveform generator applications (project the expansion coefs onto basis vectors)
      x_hat = self.backcast_g(theta_backcast)
      y_hat = self.forecast_g(theta_forecast)
  
      # Set the distribution parameters as the last dimension
      y_hat = y_hat.reshape(
          x.shape[0], self.target_length, self.nr_params
      )
  
      return x_hat, y_hat
  
imports: from enum import Enum
  from typing import List, NewType, Tuple, Union
  import numpy
  import torch
  import torch.nn
  from darts.logging import get_logger, raise_if_not, raise_log
  from darts.models.forecasting.pl_forecasting_module import PLPastCovariatesModule, io_processor
  from darts.models.forecasting.torch_forecasting_model import PastCovariatesTorchModel
  from darts.utils.torch import MonteCarloDropout
label_desc: Use image node_id 2 for calling the _Block obj's underlying member method code with example usage: obj.forward(x) and returns: x_hat,  y_hat