code: def __init__(
      self,
      units,
      thetas_dim,
      num_block_layers=4,
      backcast_length=10,
      forecast_length=5,
      dropout=0.1,
  ):
      super().__init__(
          units=units,
          thetas_dim=thetas_dim,
          num_block_layers=num_block_layers,
          backcast_length=backcast_length,
          forecast_length=forecast_length,
          share_thetas=True,
          dropout=dropout,
      )
  
      backcast_linspace, forecast_linspace = linspace(
          backcast_length, forecast_length, centered=True
      )
      norm = np.sqrt(
          forecast_length / thetas_dim
      )  # ensure range of predictions is comparable to input
  
      coefficients = torch.tensor(
          [backcast_linspace**i for i in range(thetas_dim)],
          dtype=torch.float32,
      )
      self.register_buffer("T_backcast", coefficients * norm)
  
      coefficients = torch.tensor(
          [forecast_linspace**i for i in range(thetas_dim)],
          dtype=torch.float32,
      )
      self.register_buffer("T_forecast", coefficients * norm)
  
imports: from typing import Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
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, dropout)