class_docstr: The LogTransformer() applies the natural logarithm or the base 10 logarithm to
  numerical variables. The natural logarithm is the logarithm in base e.
  
  The LogTransformer() only works with positive values. If the variable
  contains a zero or a negative value the transformer will return an error.
  
  A list of variables can be passed as an argument. Alternatively, the transformer
  will automatically select and transform all variables of type numeric.
  
  More details in the :ref:`User Guide <log_transformer>`.
  
  Parameters
  ----------
  {variables}
  
  base: string, default='e'
      Indicates if the natural or base 10 logarithm should be applied. Can take
      values 'e' or '10'.
  
  Attributes
  ----------
  {variables_}
  
  {feature_names_in_}
  
  {n_features_in_}
  
  Methods
  -------
  {fit}
  
  {fit_transform}
  
  {inverse_transform}
  
  transform:
      Transform the variables using the logarithm.
code: def _more_tags(self):
      tags_dict = _return_tags()
      # =======  this tests fail because the transformers throw an error
      # when the values are 0. Nothing to do with the test itself but
      # mostly with the data created and used in the test
      msg = "transformers raise errors when data contains zeroes, thus this check fails"
      tags_dict["_xfail_checks"]["check_estimators_dtypes"] = msg
      tags_dict["_xfail_checks"][
          "check_estimators_fit_returns_self"
      ] = msg
      tags_dict["_xfail_checks"]["check_pipeline_consistency"] = msg
      tags_dict["_xfail_checks"][
          "check_estimators_overwrite_params"
      ] = msg
      tags_dict["_xfail_checks"]["check_estimators_pickle"] = msg
      tags_dict["_xfail_checks"]["check_transformer_general"] = msg
  
      return tags_dict
  
imports: from typing import Dict, List, Optional, Union
  import numpy
  import pandas
  from feature_engine._base_transformers.base_numerical import BaseNumericalTransformer
  from feature_engine._base_transformers.mixins import FitFromDictMixin
  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 _variables_numerical_docstring
  from feature_engine._docstrings.methods import _fit_not_learn_docstring, _fit_transform_docstring, _inverse_transform_docstring
  from feature_engine._docstrings.substitute import Substitution
  from feature_engine._variable_handling.init_parameter_checks import _check_init_parameter_variables
  from feature_engine.tags import _return_tags
label_desc: Use image node_id 5 for calling the LogTransformer obj's underlying member method code with example usage: obj._more_tags() and returns: tags_dict