code: from numpy import inf, nan
  from sklearn.preprocessing import QuantileTransformer as Op
  
  from lale.docstrings import set_docstrings
  from lale.operators import make_operator
  
  
  class _QuantileTransformerImpl:
      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 transform(self, X):
          return self._wrapped_model.transform(X)
  
  
  _hyperparams_schema = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": "inherited docstring for QuantileTransformer    Transform features using quantiles information.",
      "allOf": [
          {
              "type": "object",
              "required": [
                  "n_quantiles",
                  "output_distribution",
                  "ignore_implicit_zeros",
                  "subsample",
                  "random_state",
                  "copy",
              ],
              "relevantToOptimizer": [
                  "n_quantiles",
                  "output_distribution",
                  "subsample",
                  "copy",
              ],
              "additionalProperties": False,
              "properties": {
                  "n_quantiles": {
                      "type": "integer",
                      "minimumForOptimizer": 1000,
                      "maximumForOptimizer": 1001,
                      "distribution": "uniform",
                      "default": 1000,
                      "description": "Number of quantiles to be computed",
                  },
                  "output_distribution": {
                      "enum": ["normal", "uniform"],
                      "default": "uniform",
                      "description": "Marginal distribution for the transformed data",
                  },
                  "ignore_implicit_zeros": {
                      "type": "boolean",
                      "default": False,
                      "description": "Only applies to sparse matrices",
                  },
                  "subsample": {
                      "type": "integer",
                      "minimumForOptimizer": 1,
                      "maximumForOptimizer": 100000,
                      "distribution": "uniform",
                      "default": 100000,
                      "description": "Maximum number of samples used to estimate the quantiles for computational efficiency",
                  },
                  "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",
                  },
                  "copy": {
                      "type": "boolean",
                      "default": True,
                      "description": "Set to False to perform inplace transformation and avoid a copy (if the input is already a numpy array).",
                  },
              },
          },
          {
              "XXX TODO XXX": "Parameter: ignore_implicit_zeros > only applies to sparse matrices"
          },
      ],
  }
  _input_fit_schema = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": "Compute the quantiles used for transforming.",
      "type": "object",
      "required": ["X"],
      "properties": {
          "X": {
              "laleType": "Any",
              "XXX TODO XXX": "ndarray or sparse matrix, shape (n_samples, n_features)",
              "description": "The data used to scale along the features axis",
          }
      },
  }
  _input_transform_schema = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": "Feature-wise transformation of the data.",
      "type": "object",
      "required": ["X"],
      "properties": {
          "X": {
              "laleType": "Any",
              "XXX TODO XXX": "ndarray or sparse matrix, shape (n_samples, n_features)",
              "description": "The data used to scale along the features axis",
          }
      },
  }
  _output_transform_schema = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": "The projected data.",
      "laleType": "Any",
      "XXX TODO XXX": "ndarray or sparse matrix, shape (n_samples, n_features)",
  }
  _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.preprocessing.QuantileTransformer#sklearn-preprocessing-quantiletransformer",
      "import_from": "sklearn.preprocessing",
      "type": "object",
      "tags": {"pre": [], "op": ["transformer"], "post": []},
      "properties": {
          "hyperparams": _hyperparams_schema,
          "input_fit": _input_fit_schema,
          "input_transform": _input_transform_schema,
          "output_transform": _output_transform_schema,
      },
  }
  QuantileTransformer = make_operator(_QuantileTransformerImpl, _combined_schemas)
  
  set_docstrings(QuantileTransformer)
  
apis: sklearn.preprocessing.QuantileTransformer