text: from numpy import inf, nan
  from sklearn.ensemble import RandomForestClassifier as Op
  
  from lale.docstrings import set_docstrings
  from lale.operators import make_operator
  
  
  class _RandomForestClassifierImpl:
      def __init__(self, **hyperparams):
          self._hyperparams = hyperparams
          self._wrapped_model = Op(**self._hyperparams)
  
      def fit(self, X, y=None):
          if y is not None:
              self._wrapped_model.fit(X, y)
          else:
              self._wrapped_model.fit(X)
          return self
  
      def predict(self, X):
          return self._wrapped_model.predict(X)
  
      def predict_proba(self, X):
          return self._wrapped_model.predict_proba(X)
  
  
  _hyperparams_schema = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": "inherited docstring for RandomForestClassifier    A random forest classifier.",
      "allOf": [
          {
              "type": "object",
              "required": [
                  "n_estimators",
                  "criterion",
                  "max_depth",
                  "min_samples_split",
                  "min_samples_leaf",
                  "min_weight_fraction_leaf",
                  "max_features",
                  "max_leaf_nodes",
                  "min_impurity_decrease",
                  "min_impurity_split",
                  "bootstrap",
                  "oob_score",
                  "n_jobs",
                  "random_state",
                  "verbose",
                  "warm_start",
                  "class_weight",
              ],
              "relevantToOptimizer": [
                  "n_estimators",
                  "criterion",
                  "max_depth",
                  "min_samples_split",
                  "min_samples_leaf",
                  "max_features",
              ],
              "additionalProperties": False,
              "properties": {
                  "n_estimators": {
                      "type": "integer",
                      "minimumForOptimizer": 10,
                      "maximumForOptimizer": 100,
                      "distribution": "uniform",
                      "default": 10,
                      "description": "The number of trees in the forest",
                  },
                  "criterion": {
                      "enum": ["entropy", "gini"],
                      "default": "gini",
                      "description": "The function to measure the quality of a split",
                  },
                  "max_depth": {
                      "anyOf": [
                          {
                              "type": "integer",
                              "minimumForOptimizer": 3,
                              "maximumForOptimizer": 5,
                              "distribution": "uniform",
                          },
                          {"enum": [None]},
                      ],
                      "default": None,
                      "description": "The maximum depth of the tree",
                  },
                  "min_samples_split": {
                      "anyOf": [
                          {
                              "type": "integer",
                              "minimumForOptimizer": 2,
                              "maximumForOptimizer": 5,
                              "distribution": "uniform",
                          },
                          {
                              "type": "number",
                              "minimumForOptimizer": 2,
                              "maximumForOptimizer": 5,
                              "distribution": "uniform",
                          },
                      ],
                      "default": 2,
                      "description": "The minimum number of samples required to split an internal node:  - If int, then consider `min_samples_split` as the minimum number",
                  },
                  "min_samples_leaf": {
                      "anyOf": [
                          {
                              "type": "integer",
                              "minimumForOptimizer": 1,
                              "maximumForOptimizer": 5,
                              "distribution": "uniform",
                          },
                          {
                              "type": "number",
                              "minimumForOptimizer": 1,
                              "maximumForOptimizer": 5,
                              "distribution": "uniform",
                          },
                      ],
                      "default": 1,
                      "description": "The minimum number of samples required to be at a leaf node",
                  },
                  "min_weight_fraction_leaf": {
                      "type": "number",
                      "default": 0.0,
                      "description": "The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node",
                  },
                  "max_features": {
                      "anyOf": [
                          {"type": "integer", "forOptimizer": False},
                          {
                              "type": "number",
                              "minimumForOptimizer": 0.01,
                              "maximumForOptimizer": 1.0,
                              "distribution": "uniform",
                          },
                          {"type": "string", "forOptimizer": False},
                          {"enum": [None]},
                      ],
                      "default": "auto",
                      "description": "The number of features to consider when looking for the best split:  - If int, then consider `max_features` features at each split",
                  },
                  "max_leaf_nodes": {
                      "anyOf": [{"type": "integer"}, {"enum": [None]}],
                      "default": None,
                      "description": "Grow trees with ``max_leaf_nodes`` in best-first fashion",
                  },
                  "min_impurity_decrease": {
                      "type": "number",
                      "default": 0.0,
                      "description": "A node will be split if this split induces a decrease of the impurity greater than or equal to this value",
                  },
                  "min_impurity_split": {
                      "anyOf": [{"type": "number"}, {"enum": [None]}],
                      "default": None,
                      "description": "Threshold for early stopping in tree growth",
                  },
                  "bootstrap": {
                      "type": "boolean",
                      "default": True,
                      "description": "Whether bootstrap samples are used when building trees",
                  },
                  "oob_score": {
                      "type": "boolean",
                      "default": False,
                      "description": "Whether to use out-of-bag samples to estimate the generalization accuracy.",
                  },
                  "n_jobs": {
                      "anyOf": [{"type": "integer"}, {"enum": [None]}],
                      "default": 4,
                      "description": "The number of jobs to run in parallel for both `fit` and `predict`",
                  },
                  "random_state": {
                      "anyOf": [
                          {"type": "integer"},
                          {"laleType": "numpy.random.RandomState"},
                          {"enum": [None]},
                      ],
                      "default": None,
                      "description": "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": {
                      "type": "integer",
                      "default": 0,
                      "description": "Controls the verbosity when fitting and predicting.",
                  },
                  "warm_start": {
                      "type": "boolean",
                      "default": False,
                      "description": "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",
                  },
                  "class_weight": {
                      "XXX TODO XXX": 'dict, list of dicts, "balanced", "balanced_subsample" or     None, optional (default=None)',
                      "description": "Weights associated with classes in the form ``{class_label: weight}``",
                      "enum": ["balanced"],
                      "default": "balanced",
                  },
              },
          },
          {
              "XXX TODO XXX": "Parameter: min_samples_leaf > only be considered if it leaves at least min_samples_leaf training samples in each of the left and right branches"
          },
      ],
  }
  _input_fit_schema = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": "Build a forest of trees from the training set (X, y).",
      "type": "object",
      "required": ["X", "y"],
      "properties": {
          "X": {
              "anyOf": [
                  {
                      "type": "array",
                      "items": {"laleType": "Any", "XXX TODO XXX": "item type"},
                      "XXX TODO XXX": "array-like or sparse matrix of shape = [n_samples, n_features]",
                  },
                  {
                      "type": "array",
                      "items": {"type": "array", "items": {"type": "number"}},
                  },
              ],
              "description": "The training input samples",
          },
          "y": {
              "anyOf": [
                  {"type": "array", "items": {"type": "number"}},
                  {
                      "type": "array",
                      "items": {"type": "array", "items": {"type": "number"}},
                  },
              ],
              "description": "The target values (class labels in classification, real numbers in regression).",
          },
          "sample_weight": {
              "anyOf": [{"type": "array", "items": {"type": "number"}}, {"enum": [None]}],
              "description": "Sample weights",
          },
      },
  }
  _input_predict_schema = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": "Predict class for X.",
      "type": "object",
      "required": ["X"],
      "properties": {
          "X": {
              "anyOf": [
                  {
                      "type": "array",
                      "items": {"laleType": "Any", "XXX TODO XXX": "item type"},
                      "XXX TODO XXX": "array-like or sparse matrix of shape = [n_samples, n_features]",
                  },
                  {
                      "type": "array",
                      "items": {"type": "array", "items": {"type": "number"}},
                  },
              ],
              "description": "The input samples",
          }
      },
  }
  _output_predict_schema = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": "The predicted classes.",
      "anyOf": [
          {"type": "array", "items": {"type": "number"}},
          {"type": "array", "items": {"type": "array", "items": {"type": "number"}}},
      ],
  }
  _input_predict_proba_schema = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": "Predict class probabilities for X.",
      "type": "object",
      "required": ["X"],
      "properties": {
          "X": {
              "anyOf": [
                  {
                      "type": "array",
                      "items": {"laleType": "Any", "XXX TODO XXX": "item type"},
                      "XXX TODO XXX": "array-like or sparse matrix of shape = [n_samples, n_features]",
                  },
                  {
                      "type": "array",
                      "items": {"type": "array", "items": {"type": "number"}},
                  },
              ],
              "description": "The input samples",
          }
      },
  }
  _output_predict_proba_schema = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": "such arrays if n_outputs > 1",
      "laleType": "Any",
      "XXX TODO XXX": "array of shape = [n_samples, n_classes], or a list of n_outputs",
  }
  _combined_schemas = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": "Combined schema for expected data and hyperparameters.",
      "documentation_url": "https://scikit-learn.org/0.20/modules/generated/sklearn.ensemble.RandomForestClassifier#sklearn-ensemble-randomforestclassifier",
      "import_from": "sklearn.ensemble",
      "type": "object",
      "tags": {"pre": [], "op": ["estimator", "classifier"], "post": []},
      "properties": {
          "hyperparams": _hyperparams_schema,
          "input_fit": _input_fit_schema,
          "input_predict": _input_predict_schema,
          "output_predict": _output_predict_schema,
          "input_predict_proba": _input_predict_proba_schema,
          "output_predict_proba": _output_predict_proba_schema,
      },
  }
  RandomForestClassifier = make_operator(_RandomForestClassifierImpl, _combined_schemas)
  
  set_docstrings(RandomForestClassifier)
  