class_docstr: MSES training configuration.
class_docstr_tok: ["MSES","training","configuration","."]
docstr: :param incremental: If True, train the MSES model incrementally with the initial
      training data at the given ``train_cadence``. This allows MSES to return a
      forecast for the training data.
  :param: If True, track the losses encountered during incremental initial training.
  :tune_recency_weights: If True, tune recency weights during incremental initial
      training.
  :param init_batch_sz: The size of the inital training batch for MSES. This is
      necessary because MSES cannot predict the past, but needs to start with some
      data. This should be very small. 2 is the minimum, and is recommended because
      2 will result in the most representative train forecast.
  :param train_cadence: The frequency at which the training forecasts will be generated
      during incremental training.
code: def __init__(
      self,
      incremental: bool = True,
      process_losses: bool = True,
      tune_recency_weights: bool = False,
      init_batch_sz: int = 2,
      train_cadence: int = None,
  ):
      """
      :param incremental: If True, train the MSES model incrementally with the initial
          training data at the given ``train_cadence``. This allows MSES to return a
          forecast for the training data.
      :param: If True, track the losses encountered during incremental initial training.
      :tune_recency_weights: If True, tune recency weights during incremental initial
          training.
      :param init_batch_sz: The size of the inital training batch for MSES. This is
          necessary because MSES cannot predict the past, but needs to start with some
          data. This should be very small. 2 is the minimum, and is recommended because
          2 will result in the most representative train forecast.
      :param train_cadence: The frequency at which the training forecasts will be generated
          during incremental training.
      """
      assert init_batch_sz >= 2
      self.incremental = incremental
      self.process_losses = process_losses
      self.tune_recency_weights = tune_recency_weights
      self.init_batch_sz = init_batch_sz
      self.train_cadence = train_cadence
  
imports: import logging
  from math import floor
  from typing import Dict, List, Optional, Tuple, Union
  import numpy
  import pandas
  from merlion.utils.time_series import TimeSeries, UnivariateTimeSeries, assert_equal_timedeltas
  from merlion.utils.istat import ExponentialMovingAverage, RecencyWeightedVariance
  from merlion.utils.resample import to_pd_datetime, to_timestamp
  from merlion.transform.moving_average import LagTransform
  from merlion.transform.resample import TemporalResample
  from merlion.models.forecast.base import ForecasterBase, ForecasterConfig
label_desc: Use image node_id 1 to create a new MSESTrainConfig object from inherited base classes: object with example: obj = MSESTrainConfig(incremental, process_losses, tune_recency_weights, init_batch_sz, train_cadence)