class_docstr: The ArbitraryOutlierCapper() caps the maximum or minimum values of a variable
  at an arbitrary value indicated by the user.
  
  You must provide the maximum or minimum values that will be used to cap each
  variable in a dictionary containing the features as keys and the capping values as
  values.
  
  More details in the :ref:`User Guide <arbitrary_capper>`.
  
  Parameters
  ----------
  max_capping_dict: dictionary, default=None
      Dictionary containing the user specified capping values for the right tail of
      the distribution of each variable to cap (maximum values).
  
  min_capping_dict: dictionary, default=None
      Dictionary containing user specified capping values for the eft tail of the
      distribution of each variable to cap (minimum values).
  
  {missing_values}
  
  Attributes
  ----------
  {right_tail_caps_}
  
  {left_tail_caps_}
  
  {variables_}
  
  {feature_names_in_}
  
  {n_features_in_}
  
  Methods
  -------
  
  {fit}
  
  {fit_transform}
  
  transform:
      Cap the variables.
code: def _more_tags(self):
      tags_dict = _return_tags()
      # add additional test that fails
      tags_dict["_xfail_checks"][
          "check_parameters_default_constructible"
      ] = "transformer has 1 mandatory parameter"
      return tags_dict
  
file_path: feature_engine/feature_engine/outliers/artbitrary.py
imports: from typing import Optional
  import pandas
  from feature_engine._check_input_parameters.check_input_dictionary import _check_numerical_dict
  from feature_engine._docstrings.fit_attributes import _feature_names_in_docstring, _n_features_in_docstring, _variables_attribute_docstring
  from feature_engine._docstrings.init_parameters import _missing_values_docstring
  from feature_engine._docstrings.methods import _fit_not_learn_docstring, _fit_transform_docstring
  from feature_engine._docstrings.substitute import Substitution
  from feature_engine._variable_handling.variable_type_selection import _find_or_check_numerical_variables
  from feature_engine.dataframe_checks import _check_contains_inf, _check_contains_na, check_X
  from feature_engine.outliers.base_outlier import BaseOutlier
  from feature_engine.tags import _return_tags
label_desc: Use image node_id 3 for calling the ArbitraryOutlierCapper obj's underlying member method code with example usage: obj._more_tags() and returns: tags_dict