code: import numpy as np
  
  _DEFAULT_TAGS = {
      'non_deterministic': False,
      'requires_positive_X': False,
      'requires_positive_y': False,
      'X_types': ['2darray'],
      'poor_score': False,
      'no_validation': False,
      'multioutput': False,
      "allow_nan": False,
      'stateless': False,
      'multilabel': False,
      '_skip_test': False,
      '_xfail_checks': False,
      'multioutput_only': False,
      'binary_only': False,
      'requires_fit': True,
      'preserves_dtype': [np.float64],
      'requires_y': False,
      'pairwise': False,
  }
  
  
  def _safe_tags(estimator, key=None):
      """Safely get estimator tags.
  
      :class:`~sklearn.BaseEstimator` provides the estimator tags machinery.
      However, if an estimator does not inherit from this base class, we should
      fall-back to the default tags.
  
      For scikit-learn built-in estimators, we should still rely on
      `self._get_tags()`. `_safe_tags(est)` should be used when we are not sure
      where `est` comes from: typically `_safe_tags(self.base_estimator)` where
      `self` is a meta-estimator, or in the common checks.
  
      Parameters
      ----------
      estimator : estimator object
          The estimator from which to get the tag.
  
      key : str, default=None
          Tag name to get. By default (`None`), all tags are returned.
  
      Returns
      -------
      tags : dict or tag value
          The estimator tags. A single value is returned if `key` is not None.
      """
      if hasattr(estimator, "_get_tags"):
          tags_provider = "_get_tags()"
          tags = estimator._get_tags()
      elif hasattr(estimator, "_more_tags"):
          tags_provider = "_more_tags()"
          tags = {**_DEFAULT_TAGS, **estimator._more_tags()}
      else:
          tags_provider = "_DEFAULT_TAGS"
          tags = _DEFAULT_TAGS
  
      if key is not None:
          if key not in tags:
              raise ValueError(
                  f"The key {key} is not defined in {tags_provider} for the "
                  f"class {estimator.__class__.__name__}."
              )
          return tags[key]
      return tags
  