class_docstr: An ensemble of totally random trees.
  
  An unsupervised transformation of a dataset to a high-dimensional
  sparse representation. A datapoint is coded according to which leaf of
  each tree it is sorted into. Using a one-hot encoding of the leaves,
  this leads to a binary coding with as many ones as there are trees in
  the forest.
  
  The dimensionality of the resulting representation is
  ``n_out <= n_estimators * max_leaf_nodes``. If ``max_leaf_nodes == None``,
  the number of leaf nodes is at most ``n_estimators * 2 ** max_depth``.
  
  Read more in the :ref:`User Guide <random_trees_embedding>`.
  
  Parameters
  ----------
  n_estimators : integer, optional (default=10)
      Number of trees in the forest.
  
  max_depth : integer, optional (default=5)
      The maximum depth of each tree. If None, then nodes are expanded until
      all leaves are pure or until all leaves contain less than
      min_samples_split samples.
  
  min_samples_split : int, float, optional (default=2)
      The minimum number of samples required to split an internal node:
  
      - If int, then consider `min_samples_split` as the minimum number.
      - If float, then `min_samples_split` is a percentage and
        `ceil(min_samples_split * n_samples)` is the minimum
        number of samples for each split.
  
      .. versionchanged:: 0.18
         Added float values for percentages.
  
  min_samples_leaf : int, float, optional (default=1)
      The minimum number of samples required to be at a leaf node:
  
      - If int, then consider `min_samples_leaf` as the minimum number.
      - If float, then `min_samples_leaf` is a percentage and
        `ceil(min_samples_leaf * n_samples)` is the minimum
        number of samples for each node.
  
      .. versionchanged:: 0.18
         Added float values for percentages.
  
  min_weight_fraction_leaf : float, optional (default=0.)
      The minimum weighted fraction of the sum total of weights (of all
      the input samples) required to be at a leaf node. Samples have
      equal weight when sample_weight is not provided.
  
  max_leaf_nodes : int or None, optional (default=None)
      Grow trees with ``max_leaf_nodes`` in best-first fashion.
      Best nodes are defined as relative reduction in impurity.
      If None then unlimited number of leaf nodes.
  
  min_impurity_split : float, optional (default=1e-7)
      Threshold for early stopping in tree growth. A node will split
      if its impurity is above the threshold, otherwise it is a leaf.
  
      .. versionadded:: 0.18
  
  sparse_output : bool, optional (default=True)
      Whether or not to return a sparse CSR matrix, as default behavior,
      or to return a dense array compatible with dense pipeline operators.
  
  n_jobs : integer, optional (default=1)
      The number of jobs to run in parallel for both `fit` and `predict`.
      If -1, then the number of jobs is set to the number of cores.
  
  random_state : int, RandomState instance or None, optional (default=None)
      If int, random_state is the seed used by the random number generator;
      If RandomState instance, random_state is the random number generator;
      If None, the random number generator is the RandomState instance used
      by `np.random`.
  
  verbose : int, optional (default=0)
      Controls the verbosity of the tree building process.
  
  warm_start : bool, optional (default=False)
      When set to ``True``, reuse the solution of the previous call to fit
      and add more estimators to the ensemble, otherwise, just fit a whole
      new forest.
  
  Attributes
  ----------
  estimators_ : list of DecisionTreeClassifier
      The collection of fitted sub-estimators.
  
  References
  ----------
  .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
         Machine Learning, 63(1), 3-42, 2006.
  .. [2] Moosmann, F. and Triggs, B. and Jurie, F.  "Fast discriminative
         visual codebooks using randomized clustering forests"
         NIPS 2007
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docstr: Fit estimator.
  
  Parameters
  ----------
  X : array-like or sparse matrix, shape=(n_samples, n_features)
      The input samples. Use ``dtype=np.float32`` for maximum
      efficiency. Sparse matrices are also supported, use sparse
      ``csc_matrix`` for maximum efficiency.
  
  Returns
  -------
  self : object
      Returns self.
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code: def fit(self, X, y=None, sample_weight=None):
      """Fit estimator.
  
      Parameters
      ----------
      X : array-like or sparse matrix, shape=(n_samples, n_features)
          The input samples. Use ``dtype=np.float32`` for maximum
          efficiency. Sparse matrices are also supported, use sparse
          ``csc_matrix`` for maximum efficiency.
  
      Returns
      -------
      self : object
          Returns self.
  
      """
      self.fit_transform(X, y, sample_weight=sample_weight)
      return self
  
imports: from __future__ import division
  import warnings
  from warnings import warn
  from abc import ABCMeta, abstractmethod
  import numpy
  from scipy.sparse import issparse
  from scipy.sparse import hstack
  from ..base import ClassifierMixin, RegressorMixin
  from ..externals.joblib import Parallel, delayed
  from ..externals import six
  from ..feature_selection.from_model import _LearntSelectorMixin
  from ..metrics import r2_score
  from ..preprocessing import OneHotEncoder
  from ..tree import DecisionTreeClassifier, DecisionTreeRegressor, ExtraTreeClassifier, ExtraTreeRegressor
  from ..tree._tree import DTYPE, DOUBLE
  from ..utils import check_random_state, check_array, compute_sample_weight
  from ..exceptions import DataConversionWarning, NotFittedError
  from .base import BaseEnsemble, _partition_estimators
  from ..utils.fixes import bincount, parallel_helper
  from ..utils.multiclass import check_classification_targets
label_desc: Use image node_id 3 for calling the RandomTreesEmbedding obj's underlying member method code with example usage: obj.fit(X, y, sample_weight) and returns: self