code: def __init__(
      self,
      past_seq_len,
      future_seq_len,
      stack_types=(GENERIC_BLOCK, GENERIC_BLOCK),
      nb_blocks_per_stack=3,
      thetas_dim=(4, 8),
      share_weights_in_stack=False,
      hidden_layer_units=256,
      nb_harmonics=None,
      seed=None,
  ):
      super(NBeatsNet, self).__init__()
      seed_everything(seed, workers=True)
      self.future_seq_len = future_seq_len
      self.past_seq_len = past_seq_len
      self.hidden_layer_units = hidden_layer_units
      self.nb_blocks_per_stack = nb_blocks_per_stack
      self.share_weights_in_stack = share_weights_in_stack
      self.nb_harmonics = nb_harmonics
      self.stack_types = stack_types
      self.stacks = torch.nn.ModuleList()
      self.thetas_dim = thetas_dim
      for stack_id in range(len(self.stack_types)):
          self.stacks.append(self.create_stack(stack_id))
  
imports: import pickle
  import random
  from time import time
  from typing import Union
  import numpy
  import torch
  from torch import nn, optim
  from torch.nn import functional
  from torch.nn.functional import mse_loss, l1_loss, binary_cross_entropy, cross_entropy
  from torch.optim import Optimizer
  from .utils import PYTORCH_REGRESSION_LOSS_MAP
  from pytorch_lightning import seed_everything
label_desc: Use image node_id 1 to create a new NBeatsNet object from inherited base classes: nn with example: obj = NBeatsNet(past_seq_len, future_seq_len, stack_types, nb_blocks_per_stack, thetas_dim, share_weights_in_stack, hidden_layer_units, nb_harmonics, seed)