code: def __init__(
      self,
      units,
      thetas_dim,
      num_block_layers=4,
      backcast_length=10,
      forecast_length=5,
      nb_harmonics=None,
  ):
      super(NBEATSTrendBlock, self).__init__(
          units=units,
          thetas_dim=thetas_dim,
          num_block_layers=num_block_layers,
          backcast_length=backcast_length,
          forecast_length=forecast_length,
          share_thetas=True,
      )
  
      backcast_linspace, forecast_linspace = linspace(
          backcast_length, forecast_length
      )
  
      self.register_buffer(
          "T_backcast",
          torch.tensor(
              [backcast_linspace**i for i in range(thetas_dim)]
          ).float(),
      )
      self.register_buffer(
          "T_forecast",
          torch.tensor(
              [forecast_linspace**i for i in range(thetas_dim)]
          ).float(),
      )
  
imports: from typing import List, Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from gluonts.time_feature import get_seasonality
label_desc: Use image node_id 1 to create a new NBEATSTrendBlock object from inherited base classes: NBEATSBlock with example: obj = NBEATSTrendBlock(units, thetas_dim, num_block_layers, backcast_length, forecast_length, nb_harmonics)