code: def forward(
      self, theta: torch.Tensor
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      backcast = theta[:, : self.backcast_size]
      knots = theta[:, self.backcast_size :]
  
      # Interpolation is performed on default dim=-1 := H
      knots = knots.reshape(len(knots), self.out_features, -1)
      if self.interpolation_mode in ["nearest", "linear"]:
          # knots = knots[:,None,:]
          forecast = F.interpolate(
              knots,
              size=self.forecast_size,
              mode=self.interpolation_mode,
          )
          # forecast = forecast[:,0,:]
      elif "cubic" in self.interpolation_mode:
          if self.out_features > 1:
              raise Exception(
                  "Cubic interpolation not available with multiple outputs."
              )
          batch_size = len(backcast)
          knots = knots[:, 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",
              )
              forecast[
                  i * batch_size : (i + 1) * batch_size
              ] += forecast_i[
                  :, 0, 0, :
              ]  # [B,None,H,H] -> [B,H]
          forecast = forecast[:, None, :]  # [B,H] -> [B,None,H]
  
      # [B,Q,H] -> [B,H,Q]
      forecast = forecast.permute(0, 2, 1)
      return backcast, forecast
  
imports: from typing import Tuple, Optional
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from ..losses.pytorch import MAE
  from ..common._base_windows import BaseWindows
label_desc: Use image node_id 2 for calling the _IdentityBasis obj's underlying member method code with example usage: obj.forward(theta) and returns: backcast,  forecast