class_docstr: Check for systematic error and abnormal shape in the regression error distribution.
  
  The check shows the distribution of the regression error, and enables to set conditions on two
  of the distribution parameters: Systematic error and Kurtosis value.
  Kurtosis is a measure of the shape of the distribution, helping us understand if the distribution
  is significantly "wider" from a normal distribution.
  Systematic error, otherwise known as the error bias, is the mean prediction error of the model.
  
  Parameters
  ----------
  n_top_samples : int , default: 3
      amount of samples to show which have the largest under / over estimation errors.
  n_bins : int , default: 40
      number of bins to use for the histogram.
  n_samples : int , default: 1_000_000
      number of samples to use for this check.
  random_state : int, default: 42
      random seed for all check internals.
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docstr: Add condition - require kurtosis value to be greater than the provided threshold.
  
  Kurtosis is a measure of the shape of the distribution, helping us understand if the distribution
  is significantly "wider" from a normal distribution. A lower value indicates a "wider" distribution.
  
  Parameters
  ----------
  threshold : float , default: -0.1
      Minimal threshold for kurtosis value.
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code: def add_condition_kurtosis_greater_than(
      self, threshold: float = -0.1
  ):
      """Add condition - require kurtosis value to be greater than the provided threshold.
  
      Kurtosis is a measure of the shape of the distribution, helping us understand if the distribution
      is significantly "wider" from a normal distribution. A lower value indicates a "wider" distribution.
  
      Parameters
      ----------
      threshold : float , default: -0.1
          Minimal threshold for kurtosis value.
      """
  
      def min_kurtosis_condition(
          result: Dict[str, float]
      ) -> ConditionResult:
          details = f'Found kurtosis value of {format_number(result["Kurtosis Value"], 5)}'
          category = (
              ConditionCategory.PASS
              if result["Kurtosis Value"] > threshold
              else ConditionCategory.WARN
          )
          return ConditionResult(category, details)
  
      return self.add_condition(
          f"Kurtosis value higher than {format_number(threshold, 5)}",
          min_kurtosis_condition,
      )
  
file_path: deepchecks/deepchecks/tabular/checks/model_evaluation/regression_error_distribution.py
imports: from typing import Dict
  import numpy
  import pandas
  import plotly.express
  from scipy.stats import kurtosis
  from sklearn.metrics import mean_squared_error
  from deepchecks.core import CheckResult, ConditionCategory, ConditionResult
  from deepchecks.tabular import Context, SingleDatasetCheck
  from deepchecks.utils.strings import format_number
label_desc: Use image node_id 3 for calling the RegressionErrorDistribution obj's underlying member method code with example usage: obj.add_condition_kurtosis_greater_than(threshold) and returns: self, ConditionResult