code: def __init__(
      self,
      temporal,
      temporal_cols,
      indptr,
      max_size: int,
      min_size: int,
      y_idx: int,
      static=None,
      static_cols=None,
      sorted=False,
  ):
      super().__init__()
      self.temporal = torch.tensor(temporal, dtype=torch.float)
      self.temporal_cols = pd.Index(list(temporal_cols))
  
      if static is not None:
          self.static = torch.tensor(static, dtype=torch.float)
          self.static_cols = static_cols
      else:
          self.static = static
          self.static_cols = static_cols
  
      self.indptr = indptr
      self.n_groups = self.indptr.size - 1
      self.max_size = max_size
      self.min_size = min_size
      self.y_idx = y_idx
  
      # Upadated flag. To protect consistency, dataset can only be updated once
      self.updated = False
      self.sorted = sorted
  
imports: import warnings
  from collections.abc import Mapping
  import numpy
  import pandas
  import pytorch_lightning
  import torch
  import utilsforecast.processing
  from torch.utils.data import Dataset, DataLoader
  from utilsforecast.compat import DataFrame, pl_Series
label_desc: Use image node_id 1 to create a new TimeSeriesDataset object from inherited base classes: Dataset with example: obj = TimeSeriesDataset(temporal, temporal_cols, indptr, max_size, min_size, y_idx, static, static_cols, sorted)