code: def __init__(
      self,
      prediction_length: int,
      context_length: int,
      num_stacks: int,
      widths: List[int],
      num_blocks: List[int],
      num_block_layers: List[int],
      expansion_coefficient_lengths: List[int],
      sharing: List[bool],
      stack_types: List[str],
      **kwargs,
  ) -> None:
      super(NBEATSNetwork, self).__init__()
  
      self.num_stacks = num_stacks
      self.widths = widths
      self.num_blocks = num_blocks
      self.num_block_layers = num_block_layers
      self.sharing = sharing
      self.expansion_coefficient_lengths = expansion_coefficient_lengths
      self.stack_types = stack_types
      self.prediction_length = prediction_length
      self.context_length = context_length
  
      self.net_blocks = nn.ModuleList()
      for stack_id in range(num_stacks):
          for block_id in range(num_blocks[stack_id]):
              if self.stack_types[stack_id] == "G":
                  net_block = NBEATSGenericBlock(
                      units=self.widths[stack_id],
                      thetas_dim=self.expansion_coefficient_lengths[
                          stack_id
                      ],
                      num_block_layers=self.num_block_layers[stack_id],
                      backcast_length=context_length,
                      forecast_length=prediction_length,
                  )
              elif self.stack_types[stack_id] == "S":
                  net_block = NBEATSSeasonalBlock(
                      units=self.widths[stack_id],
                      num_block_layers=self.num_block_layers[stack_id],
                      backcast_length=context_length,
                      forecast_length=prediction_length,
                  )
              else:
                  net_block = NBEATSTrendBlock(
                      units=self.widths[stack_id],
                      thetas_dim=self.expansion_coefficient_lengths[
                          stack_id
                      ],
                      num_block_layers=self.num_block_layers[stack_id],
                      backcast_length=context_length,
                      forecast_length=prediction_length,
                  )
              self.net_blocks.append(net_block)
  
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 NBEATSNetwork object from inherited base classes: nn with example: obj = NBEATSNetwork(prediction_length, context_length, num_stacks, widths, num_blocks, num_block_layers, expansion_coefficient_lengths, sharing, stack_types)