code: def forward(
      self,
      backcast_theta: torch.Tensor,
      forecast_theta: torch.Tensor,
      encoder_x_t: torch.Tensor,
      decoder_x_t: torch.Tensor,
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      backcast = backcast_theta
      knots = forecast_theta
  
      if self.interpolation_mode == "nearest":
          knots = knots[:, None, :]
          forecast = F.interpolate(
              knots,
              size=self.forecast_size,
              mode=self.interpolation_mode,
          )
          forecast = forecast[:, 0, :]
      elif self.interpolation_mode == "linear":
          knots = knots[:, None, :]
          forecast = F.interpolate(
              knots,
              size=self.forecast_size,
              mode=self.interpolation_mode,
          )  # , align_corners=True)
          forecast = forecast[:, 0, :]
      elif "cubic" in self.interpolation_mode:
          batch_size = int(self.interpolation_mode.split("-")[-1])
          knots = knots[:, None, None, :]
          forecast = torch.zeros((len(knots), self.forecast_size)).to(
              knots.device
          )
          n_batches = int(np.ceil(len(knots) / batch_size))
          for i in range(n_batches):
              forecast_i = F.interpolate(
                  knots[i * batch_size : (i + 1) * batch_size],
                  size=self.forecast_size,
                  mode="bicubic",
              )  # , align_corners=True)
              forecast[
                  i * batch_size : (i + 1) * batch_size
              ] += forecast_i[:, 0, 0, :]
  
      return backcast, forecast
  
imports: from functools import partial
  from typing import List, Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the IdentityBasis obj's underlying member method code with example usage: obj.forward(backcast_theta, forecast_theta, encoder_x_t, decoder_x_t) and returns: backcast,  forecast