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`.
code: def _get_k_fold(self, dact_data: DACTData = None) -> int:
      if self.drop_remainder:
          _round_func = math.floor
      else:
          _round_func = math.ceil
      k_folds = _round_func(
          (dact_data.shape[1] - self._validation_initial_start)
          / float(self.validation_window_size)
      )
      return k_folds
  
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 2 for calling the AnchoredWalkForwardTimeSeriesCrossValidationSplitter obj's underlying member method code with example usage: obj._get_k_fold(dact_data) and returns: k_folds