code: def forward(
      self, theta: torch.Tensor
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      polynomial_size = self.forecast_basis.shape[
          0
      ]  # [polynomial_size, L+H]
      backcast_theta = theta[:, :polynomial_size]
      forecast_theta = theta[:, polynomial_size:]
      forecast_theta = forecast_theta.reshape(
          len(forecast_theta), polynomial_size, -1
      )
      backcast = torch.einsum(
          "bp,pt->bt", backcast_theta, self.backcast_basis
      )
      forecast = torch.einsum(
          "bpq,pt->btq", forecast_theta, self.forecast_basis
      )
      return backcast, forecast
  
imports: from typing import Tuple, Optional
  import numpy
  import torch
  import torch.nn
  from ..losses.pytorch import MAE
  from ..common._base_windows import BaseWindows
label_desc: Use image node_id 2 for calling the TrendBasis obj's underlying member method code with example usage: obj.forward(theta) and returns: backcast,  forecast