code: def __init__(self, expansion_coefficient_dim, target_length):
      super().__init__()
  
      # basis is of size (expansion_coefficient_dim, target_length)
      basis = torch.stack(
          [
              (torch.arange(target_length) / target_length) ** i
              for i in range(expansion_coefficient_dim)
          ],
          dim=1,
      ).T
  
      self.basis = nn.Parameter(basis, requires_grad=False)
  
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 1 to create a new _TrendGenerator object from inherited base classes: nn with example: obj = _TrendGenerator(expansion_coefficient_dim, target_length)