code: def __init__(
      self,
      units,
      thetas_dim,
      num_block_layers=4,
      backcast_length=10,
      forecast_length=5,
  ):
      super(NBEATSGenericBlock, self).__init__(
          units=units,
          thetas_dim=thetas_dim,
          num_block_layers=num_block_layers,
          backcast_length=backcast_length,
          forecast_length=forecast_length,
      )
  
      self.backcast_fc = nn.Linear(thetas_dim, backcast_length)
      self.forecast_fc = nn.Linear(thetas_dim, forecast_length)
  
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 NBEATSGenericBlock object from inherited base classes: NBEATSBlock with example: obj = NBEATSGenericBlock(units, thetas_dim, num_block_layers, backcast_length, forecast_length)