docstr: Computes the per instance absolute difference
  between the true and predicted values.
  
  :param true_y: The true labels.
  :type true_y: list or numpy.ndarray
  :param pred_y: The predicted labels.
  :type pred_y: list or numpy.ndarray
  :return: The regression error data.
  :rtype: list
docstr_tok: ["Computes","the","per","instance","absolute","difference","between","the","true","and","predicted","values",".",":","param","true_y",":","The","true","labels",".",":","type","true_y",":","list","or","numpy.ndarray",":","param","pred_y",":","The","predicted","labels",".",":","type","pred_y",":","list","or","numpy.ndarray",":","return",":","The","regression","error","data",".",":","rtype",":","list"]
code: def _compute_regression_error_data(self, true_y, pred_y):
      """Computes the per instance absolute difference
      between the true and predicted values.
  
      :param true_y: The true labels.
      :type true_y: list or numpy.ndarray
      :param pred_y: The predicted labels.
      :type pred_y: list or numpy.ndarray
      :return: The regression error data.
      :rtype: list
      """
      regression_error = []
      if not isinstance(pred_y, np.ndarray):
          pred_y = np.array(pred_y)
  
      if not isinstance(true_y, np.ndarray):
          true_y = np.array(true_y)
  
      for i in range(len(true_y)):
          regression_error.append(abs(true_y[i] - pred_y[i]))
      return regression_error
  
file_path: responsible-ai-toolbox/erroranalysis/erroranalysis/_internal/cohort_filter.py
imports: from typing import Any, List, Optional
  import numpy
  import pandas
  from erroranalysis._internal.constants import ARG, COLUMN, COMPOSITE_FILTERS, METHOD, OPERATION, PRED_Y, ROW_INDEX, TRUE_Y, CohortFilterMethods, CohortFilterOps, ModelTask
  from erroranalysis._internal.metrics import get_ordered_classes
  from erroranalysis._internal.utils import is_spark
label_desc: Use image node_id 7 for calling the FilterDataWithCohortFilters obj's underlying member method code with example usage: obj._compute_regression_error_data(true_y, pred_y) and returns: regression_error