docstr: Number of stacks.
  
  Returns:
      int: number of stacks.
docstr_tok: ["Number","of","stacks",".","Returns",":","int",":","number","of","stacks","."]
code: def n_stacks(self) -> int:
      """Number of stacks.
  
      Returns:
          int: number of stacks.
      """
      return len(self.hparams.n_blocks)
  
imports: from copy import copy
  from typing import Dict, List, Optional, Tuple, Union
  from matplotlib import pyplot
  import numpy
  import torch
  from torch import nn
  from pytorch_forecasting.data import TimeSeriesDataSet
  from pytorch_forecasting.data.encoders import NaNLabelEncoder
  from pytorch_forecasting.metrics import MAE, MAPE, MASE, RMSE, SMAPE, MultiHorizonMetric, MultiLoss
  from pytorch_forecasting.models.base_model import BaseModelWithCovariates
  from pytorch_forecasting.models.nhits.sub_modules import NHiTS
  from pytorch_forecasting.models.nn.embeddings import MultiEmbedding
  from pytorch_forecasting.utils import create_mask, detach, to_list
label_desc: Use image node_id 5 for calling the NHiTS obj's underlying member method code with example usage: obj.n_stacks() and returns: len