class_docstr: Implementaion of Deep AR model
class_docstr_tok: ["Implementaion","of","Deep","AR","model"]
code: def get_lagged_subsequences(
      sequence,
      sequence_length,
      indices: List[int],
      subsequences_length: int = 1,
  ) -> torch.Tensor:
      assert max(indices) + subsequences_length <= sequence_length, (
          f"lags cannot go further than n_past, found lag {max(indices)} "
          f"while n_past is only {sequence_length}"
      )
      assert all(lag_index >= 0 for lag_index in indices)
  
      lagged_values = []
      for lag_index in indices:
          begin_index = -lag_index - subsequences_length
          end_index = -lag_index if lag_index > 0 else None
          lagged_values.append(sequence[:, begin_index:end_index, ...])
      return torch.stack(lagged_values, dim=-1)
  
imports: import copy
  import logging
  import math
  import numpy
  import pandas
  from typing import List, Optional, Tuple, Union
  from merlion.utils.misc import initializer
  from merlion.models.base import NormalizingConfig
  from merlion.models.deep_base import TorchModel, LossFunction
  from merlion.models.forecast.deep_base import DeepForecasterConfig, DeepForecaster
label_desc: Use image node_id 2 for calling the DeepARModel obj's underlying member method code with example usage: obj.get_lagged_subsequences(sequence, sequence_length, indices, subsequences_length) and returns: torch