code: def forward(self, x):
      batch_size = x.shape[0]
  
      # pooling
      x = x.unsqueeze(1)
      x = self.pooling_layer(x)
      x = x.squeeze(1)
  
      # fully connected layer stack
      x = self.layers(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
      )
  
      # interpolate function expects (batch, "channels", time)
      theta_backcast = theta_backcast.unsqueeze(1)
  
      # interpolate both backcast and forecast from the thetas
      x_hat = F.interpolate(
          theta_backcast, size=self.input_chunk_length, mode="linear"
      )
      y_hat = F.interpolate(
          theta_forecast, size=self.output_chunk_length, mode="linear"
      )
  
      x_hat = x_hat.squeeze(
          1
      )  # remove 2nd dim we added before interpolation
  
      # Set the distribution parameters as the last dimension
      y_hat = y_hat.reshape(
          x.shape[0], self.output_chunk_length, self.nr_params
      )
  
      return x_hat, y_hat
  
imports: from typing import List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from darts.logging import get_logger, raise_if_not
  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