class_docstr: An anchored walk forward cross validation works by performing a forward rolling split.
  
  All training splits start at the beginning of the time series, and finish time varies.
  
  For the validation split it, will start after a certain time delay (if padding is set)
  after their corresponding training split.
  
  Data is expected to be an is a square nd.array of shape [batch_size, total_time_steps, ...].
  It can be N dimensions, such as 3D or more, but the time series axis is currently limited to `axis=1`.
docstr: Get the start time of the training split at the given fold index.
  Here in the anchored splitter, it is always zero. This method is overwritten
  in the non-anchored version of the walk forward ts validation splitter
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code: def _get_beginning_at_fold_i(self, fold_i: int) -> int:
      """
      Get the start time of the training split at the given fold index.
      Here in the anchored splitter, it is always zero. This method is overwritten
      in the non-anchored version of the walk forward ts validation splitter
      """
      return 0
  
imports: import copy
  import math
  from abc import ABC, abstractmethod
  from typing import Any, List, Optional, Set, Tuple
  import numpy
  from neuraxle.base import ExecutionContext
  from neuraxle.data_container import DIT, EOT, IDT, DACTData
  from neuraxle.data_container import DataContainer
  from neuraxle.data_container import TrainDACT, ValidDACT
label_desc: Use image node_id 4 for calling the AnchoredWalkForwardTimeSeriesCrossValidationSplitter obj's underlying member method code with example usage: obj._get_beginning_at_fold_i(fold_i) and returns: int