text: """Base classes for all estimators."""
  
  # Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
  # License: BSD 3 clause
  
  import copy
  import warnings
  from collections import defaultdict
  import platform
  import inspect
  import re
  
  import numpy as np
  
  from . import __version__
  from ._config import get_config
  from .utils import _IS_32BIT
  from .utils._tags import (
      _DEFAULT_TAGS,
      _safe_tags,
  )
  from .utils.validation import check_X_y
  from .utils.validation import check_array
  from .utils.validation import _check_y
  from .utils.validation import _num_features
  from .utils._estimator_html_repr import estimator_html_repr
  
  
  def clone(estimator, *, safe=True):
      """Constructs a new unfitted estimator with the same parameters.
  
      Clone does a deep copy of the model in an estimator
      without actually copying attached data. It yields a new estimator
      with the same parameters that has not been fitted on any data.
  
      If the estimator's `random_state` parameter is an integer (or if the
      estimator doesn't have a `random_state` parameter), an *exact clone* is
      returned: the clone and the original estimator will give the exact same
      results. Otherwise, *statistical clone* is returned: the clone might
      yield different results from the original estimator. More details can be
      found in :ref:`randomness`.
  
      Parameters
      ----------
      estimator : {list, tuple, set} of estimator instance or a single \
              estimator instance
          The estimator or group of estimators to be cloned.
  
      safe : bool, default=True
          If safe is False, clone will fall back to a deep copy on objects
          that are not estimators.
  
      """
      estimator_type = type(estimator)
      # XXX: not handling dictionaries
      if estimator_type in (list, tuple, set, frozenset):
          return estimator_type([clone(e, safe=safe) for e in estimator])
      elif not hasattr(estimator, "get_params") or isinstance(estimator, type):
          if not safe:
              return copy.deepcopy(estimator)
          else:
              if isinstance(estimator, type):
                  raise TypeError(
                      "Cannot clone object. "
                      + "You should provide an instance of "
                      + "scikit-learn estimator instead of a class."
                  )
              else:
                  raise TypeError(
                      "Cannot clone object '%s' (type %s): "
                      "it does not seem to be a scikit-learn "
                      "estimator as it does not implement a "
                      "'get_params' method." % (repr(estimator), type(estimator))
                  )
  
      klass = estimator.__class__
      new_object_params = estimator.get_params(deep=False)
      for name, param in new_object_params.items():
          new_object_params[name] = clone(param, safe=False)
      new_object = klass(**new_object_params)
      params_set = new_object.get_params(deep=False)
  
      # quick sanity check of the parameters of the clone
      for name in new_object_params:
          param1 = new_object_params[name]
          param2 = params_set[name]
          if param1 is not param2:
              raise RuntimeError(
                  "Cannot clone object %s, as the constructor "
                  "either does not set or modifies parameter %s" % (estimator, name)
              )
      return new_object
  
  
  def _pprint(params, offset=0, printer=repr):
      """Pretty print the dictionary 'params'
  
      Parameters
      ----------
      params : dict
          The dictionary to pretty print
  
      offset : int, default=0
          The offset in characters to add at the begin of each line.
  
      printer : callable, default=repr
          The function to convert entries to strings, typically
          the builtin str or repr
  
      """
      # Do a multi-line justified repr:
      options = np.get_printoptions()
      np.set_printoptions(precision=5, threshold=64, edgeitems=2)
      params_list = list()
      this_line_length = offset
      line_sep = ",\n" + (1 + offset // 2) * " "
      for i, (k, v) in enumerate(sorted(params.items())):
          if type(v) is float:
              # use str for representing floating point numbers
              # this way we get consistent representation across
              # architectures and versions.
              this_repr = "%s=%s" % (k, str(v))
          else:
              # use repr of the rest
              this_repr = "%s=%s" % (k, printer(v))
          if len(this_repr) > 500:
              this_repr = this_repr[:300] + "..." + this_repr[-100:]
          if i > 0:
              if this_line_length + len(this_repr) >= 75 or "\n" in this_repr:
                  params_list.append(line_sep)
                  this_line_length = len(line_sep)
              else:
                  params_list.append(", ")
                  this_line_length += 2
          params_list.append(this_repr)
          this_line_length += len(this_repr)
  
      np.set_printoptions(**options)
      lines = "".join(params_list)
      # Strip trailing space to avoid nightmare in doctests
      lines = "\n".join(l.rstrip(" ") for l in lines.split("\n"))
      return lines
  
  
  class BaseEstimator:
      """Base class for all estimators in scikit-learn.
  
      Notes
      -----
      All estimators should specify all the parameters that can be set
      at the class level in their ``__init__`` as explicit keyword
      arguments (no ``*args`` or ``**kwargs``).
      """
  
      @classmethod
      def _get_param_names(cls):
          """Get parameter names for the estimator"""
          # fetch the constructor or the original constructor before
          # deprecation wrapping if any
          init = getattr(cls.__init__, "deprecated_original", cls.__init__)
          if init is object.__init__:
              # No explicit constructor to introspect
              return []
  
          # introspect the constructor arguments to find the model parameters
          # to represent
          init_signature = inspect.signature(init)
          # Consider the constructor parameters excluding 'self'
          parameters = [
              p
              for p in init_signature.parameters.values()
              if p.name != "self" and p.kind != p.VAR_KEYWORD
          ]
          for p in parameters:
              if p.kind == p.VAR_POSITIONAL:
                  raise RuntimeError(
                      "scikit-learn estimators should always "
                      "specify their parameters in the signature"
                      " of their __init__ (no varargs)."
                      " %s with constructor %s doesn't "
                      " follow this convention." % (cls, init_signature)
                  )
          # Extract and sort argument names excluding 'self'
          return sorted([p.name for p in parameters])
  
      def get_params(self, deep=True):
          """
          Get parameters for this estimator.
  
          Parameters
          ----------
          deep : bool, default=True
              If True, will return the parameters for this estimator and
              contained subobjects that are estimators.
  
          Returns
          -------
          params : dict
              Parameter names mapped to their values.
          """
          out = dict()
          for key in self._get_param_names():
              value = getattr(self, key)
              if deep and hasattr(value, "get_params"):
                  deep_items = value.get_params().items()
                  out.update((key + "__" + k, val) for k, val in deep_items)
              out[key] = value
          return out
  
      def set_params(self, **params):
          """
          Set the parameters of this estimator.
  
          The method works on simple estimators as well as on nested objects
          (such as :class:`~sklearn.pipeline.Pipeline`). The latter have
          parameters of the form ``<component>__<parameter>`` so that it's
          possible to update each component of a nested object.
  
          Parameters
          ----------
          **params : dict
              Estimator parameters.
  
          Returns
          -------
          self : estimator instance
              Estimator instance.
          """
          if not params:
              # Simple optimization to gain speed (inspect is slow)
              return self
          valid_params = self.get_params(deep=True)
  
          nested_params = defaultdict(dict)  # grouped by prefix
          for key, value in params.items():
              key, delim, sub_key = key.partition("__")
              if key not in valid_params:
                  raise ValueError(
                      "Invalid parameter %s for estimator %s. "
                      "Check the list of available parameters "
                      "with `estimator.get_params().keys()`." % (key, self)
                  )
  
              if delim:
                  nested_params[key][sub_key] = value
              else:
                  setattr(self, key, value)
                  valid_params[key] = value
  
          for key, sub_params in nested_params.items():
              valid_params[key].set_params(**sub_params)
  
          return self
  
      def __repr__(self, N_CHAR_MAX=700):
          # N_CHAR_MAX is the (approximate) maximum number of non-blank
          # characters to render. We pass it as an optional parameter to ease
          # the tests.
  
          from .utils._pprint import _EstimatorPrettyPrinter
  
          N_MAX_ELEMENTS_TO_SHOW = 30  # number of elements to show in sequences
  
          # use ellipsis for sequences with a lot of elements
          pp = _EstimatorPrettyPrinter(
              compact=True,
              indent=1,
              indent_at_name=True,
              n_max_elements_to_show=N_MAX_ELEMENTS_TO_SHOW,
          )
  
          repr_ = pp.pformat(self)
  
          # Use bruteforce ellipsis when there are a lot of non-blank characters
          n_nonblank = len("".join(repr_.split()))
          if n_nonblank > N_CHAR_MAX:
              lim = N_CHAR_MAX // 2  # apprx number of chars to keep on both ends
              regex = r"^(\s*\S){%d}" % lim
              # The regex '^(\s*\S){%d}' % n
              # matches from the start of the string until the nth non-blank
              # character:
              # - ^ matches the start of string
              # - (pattern){n} matches n repetitions of pattern
              # - \s*\S matches a non-blank char following zero or more blanks
              left_lim = re.match(regex, repr_).end()
              right_lim = re.match(regex, repr_[::-1]).end()
  
              if "\n" in repr_[left_lim:-right_lim]:
                  # The left side and right side aren't on the same line.
                  # To avoid weird cuts, e.g.:
                  # categoric...ore',
                  # we need to start the right side with an appropriate newline
                  # character so that it renders properly as:
                  # categoric...
                  # handle_unknown='ignore',
                  # so we add [^\n]*\n which matches until the next \n
                  regex += r"[^\n]*\n"
                  right_lim = re.match(regex, repr_[::-1]).end()
  
              ellipsis = "..."
              if left_lim + len(ellipsis) < len(repr_) - right_lim:
                  # Only add ellipsis if it results in a shorter repr
                  repr_ = repr_[:left_lim] + "..." + repr_[-right_lim:]
  
          return repr_
  
      def __getstate__(self):
          try:
              state = super().__getstate__()
          except AttributeError:
              state = self.__dict__.copy()
  
          if type(self).__module__.startswith("sklearn."):
              return dict(state.items(), _sklearn_version=__version__)
          else:
              return state
  
      def __setstate__(self, state):
          if type(self).__module__.startswith("sklearn."):
              pickle_version = state.pop("_sklearn_version", "pre-0.18")
              if pickle_version != __version__:
                  warnings.warn(
                      "Trying to unpickle estimator {0} from version {1} when "
                      "using version {2}. This might lead to breaking code or "
                      "invalid results. Use at your own risk.".format(
                          self.__class__.__name__, pickle_version, __version__
                      ),
                      UserWarning,
                  )
          try:
              super().__setstate__(state)
          except AttributeError:
              self.__dict__.update(state)
  
      def _more_tags(self):
          return _DEFAULT_TAGS
  
      def _get_tags(self):
          collected_tags = {}
          for base_class in reversed(inspect.getmro(self.__class__)):
              if hasattr(base_class, "_more_tags"):
                  # need the if because mixins might not have _more_tags
                  # but might do redundant work in estimators
                  # (i.e. calling more tags on BaseEstimator multiple times)
                  more_tags = base_class._more_tags(self)
                  collected_tags.update(more_tags)
          return collected_tags
  
      def _check_n_features(self, X, reset):
          """Set the `n_features_in_` attribute, or check against it.
  
          Parameters
          ----------
          X : {ndarray, sparse matrix} of shape (n_samples, n_features)
              The input samples.
          reset : bool
              If True, the `n_features_in_` attribute is set to `X.shape[1]`.
              If False and the attribute exists, then check that it is equal to
              `X.shape[1]`. If False and the attribute does *not* exist, then
              the check is skipped.
              .. note::
                 It is recommended to call reset=True in `fit` and in the first
                 call to `partial_fit`. All other methods that validate `X`
                 should set `reset=False`.
          """
          try:
              n_features = _num_features(X)
          except TypeError as e:
              if not reset and hasattr(self, "n_features_in_"):
                  raise ValueError(
                      "X does not contain any features, but "
                      f"{self.__class__.__name__} is expecting "
                      f"{self.n_features_in_} features"
                  ) from e
              # If the number of features is not defined and reset=True,
              # then we skip this check
              return
  
          if reset:
              self.n_features_in_ = n_features
              return
  
          if not hasattr(self, "n_features_in_"):
              # Skip this check if the expected number of expected input features
              # was not recorded by calling fit first. This is typically the case
              # for stateless transformers.
              return
  
          if n_features != self.n_features_in_:
              raise ValueError(
                  f"X has {n_features} features, but {self.__class__.__name__} "
                  f"is expecting {self.n_features_in_} features as input."
              )
  
      def _validate_data(
          self,
          X="no_validation",
          y="no_validation",
          reset=True,
          validate_separately=False,
          **check_params,
      ):
          """Validate input data and set or check the `n_features_in_` attribute.
  
          Parameters
          ----------
          X : {array-like, sparse matrix, dataframe} of shape \
                  (n_samples, n_features), default='no validation'
              The input samples.
              If `'no_validation'`, no validation is performed on `X`. This is
              useful for meta-estimator which can delegate input validation to
              their underlying estimator(s). In that case `y` must be passed and
              the only accepted `check_params` are `multi_output` and
              `y_numeric`.
  
          y : array-like of shape (n_samples,), default='no_validation'
              The targets.
  
              - If `None`, `check_array` is called on `X`. If the estimator's
                requires_y tag is True, then an error will be raised.
              - If `'no_validation'`, `check_array` is called on `X` and the
                estimator's requires_y tag is ignored. This is a default
                placeholder and is never meant to be explicitly set. In that case
                `X` must be passed.
              - Otherwise, only `y` with `_check_y` or both `X` and `y` are
                checked with either `check_array` or `check_X_y` depending on
                `validate_separately`.
  
          reset : bool, default=True
              Whether to reset the `n_features_in_` attribute.
              If False, the input will be checked for consistency with data
              provided when reset was last True.
              .. note::
                 It is recommended to call reset=True in `fit` and in the first
                 call to `partial_fit`. All other methods that validate `X`
                 should set `reset=False`.
          validate_separately : False or tuple of dicts, default=False
              Only used if y is not None.
              If False, call validate_X_y(). Else, it must be a tuple of kwargs
              to be used for calling check_array() on X and y respectively.
          **check_params : kwargs
              Parameters passed to :func:`sklearn.utils.check_array` or
              :func:`sklearn.utils.check_X_y`. Ignored if validate_separately
              is not False.
  
          Returns
          -------
          out : {ndarray, sparse matrix} or tuple of these
              The validated input. A tuple is returned if both `X` and `y` are
              validated.
          """
          if y is None and self._get_tags()["requires_y"]:
              raise ValueError(
                  f"This {self.__class__.__name__} estimator "
                  f"requires y to be passed, but the target y is None."
              )
  
          no_val_X = isinstance(X, str) and X == "no_validation"
          no_val_y = y is None or isinstance(y, str) and y == "no_validation"
  
          if no_val_X and no_val_y:
              raise ValueError("Validation should be done on X, y or both.")
          elif not no_val_X and no_val_y:
              X = check_array(X, **check_params)
              out = X
          elif no_val_X and not no_val_y:
              y = _check_y(y, **check_params)
              out = y
          else:
              if validate_separately:
                  # We need this because some estimators validate X and y
                  # separately, and in general, separately calling check_array()
                  # on X and y isn't equivalent to just calling check_X_y()
                  # :(
                  check_X_params, check_y_params = validate_separately
                  X = check_array(X, **check_X_params)
                  y = check_array(y, **check_y_params)
              else:
                  X, y = check_X_y(X, y, **check_params)
              out = X, y
  
          if not no_val_X and check_params.get("ensure_2d", True):
              self._check_n_features(X, reset=reset)
  
          return out
  
      @property
      def _repr_html_(self):
          """HTML representation of estimator.
  
          This is redundant with the logic of `_repr_mimebundle_`. The latter
          should be favorted in the long term, `_repr_html_` is only
          implemented for consumers who do not interpret `_repr_mimbundle_`.
          """
          if get_config()["display"] != "diagram":
              raise AttributeError(
                  "_repr_html_ is only defined when the "
                  "'display' configuration option is set to "
                  "'diagram'"
              )
          return self._repr_html_inner
  
      def _repr_html_inner(self):
          """This function is returned by the @property `_repr_html_` to make
          `hasattr(estimator, "_repr_html_") return `True` or `False` depending
          on `get_config()["display"]`.
          """
          return estimator_html_repr(self)
  
      def _repr_mimebundle_(self, **kwargs):
          """Mime bundle used by jupyter kernels to display estimator"""
          output = {"text/plain": repr(self)}
          if get_config()["display"] == "diagram":
              output["text/html"] = estimator_html_repr(self)
          return output
  
  
  class ClassifierMixin:
      """Mixin class for all classifiers in scikit-learn."""
  
      _estimator_type = "classifier"
  
      def score(self, X, y, sample_weight=None):
          """
          Return the mean accuracy on the given test data and labels.
  
          In multi-label classification, this is the subset accuracy
          which is a harsh metric since you require for each sample that
          each label set be correctly predicted.
  
          Parameters
          ----------
          X : array-like of shape (n_samples, n_features)
              Test samples.
  
          y : array-like of shape (n_samples,) or (n_samples, n_outputs)
              True labels for `X`.
  
          sample_weight : array-like of shape (n_samples,), default=None
              Sample weights.
  
          Returns
          -------
          score : float
              Mean accuracy of ``self.predict(X)`` wrt. `y`.
          """
          from .metrics import accuracy_score
  
          return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
  
      def _more_tags(self):
          return {"requires_y": True}
  
  
  class RegressorMixin:
      """Mixin class for all regression estimators in scikit-learn."""
  
      _estimator_type = "regressor"
  
      def score(self, X, y, sample_weight=None):
          """Return the coefficient of determination :math:`R^2` of the prediction.
  
          The coefficient :math:`R^2` is defined as :math:`(1 - \\frac{u}{v})`,
          where :math:`u` is the residual sum of squares ``((y_true - y_pred)
          ** 2).sum()`` and :math:`v` is the total sum of squares ``((y_true -
          y_true.mean()) ** 2).sum()``. The best possible score is 1.0 and it
          can be negative (because the model can be arbitrarily worse). A
          constant model that always predicts the expected value of `y`,
          disregarding the input features, would get a :math:`R^2` score of
          0.0.
  
          Parameters
          ----------
          X : array-like of shape (n_samples, n_features)
              Test samples. For some estimators this may be a precomputed
              kernel matrix or a list of generic objects instead with shape
              ``(n_samples, n_samples_fitted)``, where ``n_samples_fitted``
              is the number of samples used in the fitting for the estimator.
  
          y : array-like of shape (n_samples,) or (n_samples, n_outputs)
              True values for `X`.
  
          sample_weight : array-like of shape (n_samples,), default=None
              Sample weights.
  
          Returns
          -------
          score : float
              :math:`R^2` of ``self.predict(X)`` wrt. `y`.
  
          Notes
          -----
          The :math:`R^2` score used when calling ``score`` on a regressor uses
          ``multioutput='uniform_average'`` from version 0.23 to keep consistent
          with default value of :func:`~sklearn.metrics.r2_score`.
          This influences the ``score`` method of all the multioutput
          regressors (except for
          :class:`~sklearn.multioutput.MultiOutputRegressor`).
          """
  
          from .metrics import r2_score
  
          y_pred = self.predict(X)
          return r2_score(y, y_pred, sample_weight=sample_weight)
  
      def _more_tags(self):
          return {"requires_y": True}
  
  
  class ClusterMixin:
      """Mixin class for all cluster estimators in scikit-learn."""
  
      _estimator_type = "clusterer"
  
      def fit_predict(self, X, y=None):
          """
          Perform clustering on `X` and returns cluster labels.
  
          Parameters
          ----------
          X : array-like of shape (n_samples, n_features)
              Input data.
  
          y : Ignored
              Not used, present for API consistency by convention.
  
          Returns
          -------
          labels : ndarray of shape (n_samples,), dtype=np.int64
              Cluster labels.
          """
          # non-optimized default implementation; override when a better
          # method is possible for a given clustering algorithm
          self.fit(X)
          return self.labels_
  
      def _more_tags(self):
          return {"preserves_dtype": []}
  
  
  class BiclusterMixin:
      """Mixin class for all bicluster estimators in scikit-learn."""
  
      @property
      def biclusters_(self):
          """Convenient way to get row and column indicators together.
  
          Returns the ``rows_`` and ``columns_`` members.
          """
          return self.rows_, self.columns_
  
      def get_indices(self, i):
          """Row and column indices of the `i`'th bicluster.
  
          Only works if ``rows_`` and ``columns_`` attributes exist.
  
          Parameters
          ----------
          i : int
              The index of the cluster.
  
          Returns
          -------
          row_ind : ndarray, dtype=np.intp
              Indices of rows in the dataset that belong to the bicluster.
          col_ind : ndarray, dtype=np.intp
              Indices of columns in the dataset that belong to the bicluster.
  
          """
          rows = self.rows_[i]
          columns = self.columns_[i]
          return np.nonzero(rows)[0], np.nonzero(columns)[0]
  
      def get_shape(self, i):
          """Shape of the `i`'th bicluster.
  
          Parameters
          ----------
          i : int
              The index of the cluster.
  
          Returns
          -------
          n_rows : int
              Number of rows in the bicluster.
  
          n_cols : int
              Number of columns in the bicluster.
          """
          indices = self.get_indices(i)
          return tuple(len(i) for i in indices)
  
      def get_submatrix(self, i, data):
          """Return the submatrix corresponding to bicluster `i`.
  
          Parameters
          ----------
          i : int
              The index of the cluster.
          data : array-like of shape (n_samples, n_features)
              The data.
  
          Returns
          -------
          submatrix : ndarray of shape (n_rows, n_cols)
              The submatrix corresponding to bicluster `i`.
  
          Notes
          -----
          Works with sparse matrices. Only works if ``rows_`` and
          ``columns_`` attributes exist.
          """
          from .utils.validation import check_array
  
          data = check_array(data, accept_sparse="csr")
          row_ind, col_ind = self.get_indices(i)
          return data[row_ind[:, np.newaxis], col_ind]
  
  
  class TransformerMixin:
      """Mixin class for all transformers in scikit-learn."""
  
      def fit_transform(self, X, y=None, **fit_params):
          """
          Fit to data, then transform it.
  
          Fits transformer to `X` and `y` with optional parameters `fit_params`
          and returns a transformed version of `X`.
  
          Parameters
          ----------
          X : array-like of shape (n_samples, n_features)
              Input samples.
  
          y :  array-like of shape (n_samples,) or (n_samples, n_outputs), \
                  default=None
              Target values (None for unsupervised transformations).
  
          **fit_params : dict
              Additional fit parameters.
  
          Returns
          -------
          X_new : ndarray array of shape (n_samples, n_features_new)
              Transformed array.
          """
          # non-optimized default implementation; override when a better
          # method is possible for a given clustering algorithm
          if y is None:
              # fit method of arity 1 (unsupervised transformation)
              return self.fit(X, **fit_params).transform(X)
          else:
              # fit method of arity 2 (supervised transformation)
              return self.fit(X, y, **fit_params).transform(X)
  
  
  class DensityMixin:
      """Mixin class for all density estimators in scikit-learn."""
  
      _estimator_type = "DensityEstimator"
  
      def score(self, X, y=None):
          """Return the score of the model on the data `X`.
  
          Parameters
          ----------
          X : array-like of shape (n_samples, n_features)
              Test samples.
  
          y : Ignored
              Not used, present for API consistency by convention.
  
          Returns
          -------
          score : float
          """
          pass
  
  
  class OutlierMixin:
      """Mixin class for all outlier detection estimators in scikit-learn."""
  
      _estimator_type = "outlier_detector"
  
      def fit_predict(self, X, y=None):
          """Perform fit on X and returns labels for X.
  
          Returns -1 for outliers and 1 for inliers.
  
          Parameters
          ----------
          X : {array-like, sparse matrix, dataframe} of shape \
              (n_samples, n_features)
  
          y : Ignored
              Not used, present for API consistency by convention.
  
          Returns
          -------
          y : ndarray of shape (n_samples,)
              1 for inliers, -1 for outliers.
          """
          # override for transductive outlier detectors like LocalOulierFactor
          return self.fit(X).predict(X)
  
  
  class MetaEstimatorMixin:
      _required_parameters = ["estimator"]
      """Mixin class for all meta estimators in scikit-learn."""
  
  
  class MultiOutputMixin:
      """Mixin to mark estimators that support multioutput."""
  
      def _more_tags(self):
          return {"multioutput": True}
  
  
  class _UnstableArchMixin:
      """Mark estimators that are non-determinstic on 32bit or PowerPC"""
  
      def _more_tags(self):
          return {
              "non_deterministic": (
                  _IS_32BIT or platform.machine().startswith(("ppc", "powerpc"))
              )
          }
  
  
  def is_classifier(estimator):
      """Return True if the given estimator is (probably) a classifier.
  
      Parameters
      ----------
      estimator : object
          Estimator object to test.
  
      Returns
      -------
      out : bool
          True if estimator is a classifier and False otherwise.
      """
      return getattr(estimator, "_estimator_type", None) == "classifier"
  
  
  def is_regressor(estimator):
      """Return True if the given estimator is (probably) a regressor.
  
      Parameters
      ----------
      estimator : estimator instance
          Estimator object to test.
  
      Returns
      -------
      out : bool
          True if estimator is a regressor and False otherwise.
      """
      return getattr(estimator, "_estimator_type", None) == "regressor"
  
  
  def is_outlier_detector(estimator):
      """Return True if the given estimator is (probably) an outlier detector.
  
      Parameters
      ----------
      estimator : estimator instance
          Estimator object to test.
  
      Returns
      -------
      out : bool
          True if estimator is an outlier detector and False otherwise.
      """
      return getattr(estimator, "_estimator_type", None) == "outlier_detector"
  
  
  def _is_pairwise(estimator):
      """Returns True if estimator is pairwise.
  
      - If the `_pairwise` attribute and the tag are present and consistent,
        then use the value and not issue a warning.
      - If the `_pairwise` attribute and the tag are present and not
        consistent, use the `_pairwise` value and issue a deprecation
        warning.
      - If only the `_pairwise` attribute is present and it is not False,
        issue a deprecation warning and use the `_pairwise` value.
  
      Parameters
      ----------
      estimator : object
          Estimator object to test.
  
      Returns
      -------
      out : bool
          True if the estimator is pairwise and False otherwise.
      """
      with warnings.catch_warnings():
          warnings.filterwarnings("ignore", category=FutureWarning)
          has_pairwise_attribute = hasattr(estimator, "_pairwise")
          pairwise_attribute = getattr(estimator, "_pairwise", False)
      pairwise_tag = _safe_tags(estimator, key="pairwise")
  
      if has_pairwise_attribute:
          if pairwise_attribute != pairwise_tag:
              warnings.warn(
                  "_pairwise was deprecated in 0.24 and will be removed in 1.1 "
                  "(renaming of 0.26). Set the estimator tags of your estimator "
                  "instead",
                  FutureWarning,
              )
          return pairwise_attribute
  
      # use pairwise tag when the attribute is not present
      return pairwise_tag
  