text: from numpy import inf, nan
  from sklearn.kernel_approximation import Nystroem as Op
  
  from lale.docstrings import set_docstrings
  from lale.operators import make_operator
  
  
  class _NystroemImpl:
      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 Nystroem    Approximate a kernel map using a subset of the training data.",
      "allOf": [
          {
              "type": "object",
              "required": [
                  "kernel",
                  "gamma",
                  "coef0",
                  "degree",
                  "kernel_params",
                  "n_components",
                  "random_state",
              ],
              "relevantToOptimizer": ["kernel", "n_components"],
              "additionalProperties": False,
              "properties": {
                  "kernel": {
                      "anyOf": [
                          {"laleType": "callable", "forOptimizer": False},
                          {"enum": ["linear", "poly", "rbf", "sigmoid"]},
                      ],
                      "default": "rbf",
                      "description": "Kernel map to be approximated",
                  },
                  "gamma": {
                      "anyOf": [{"type": "number"}, {"enum": [None]}],
                      "default": None,
                      "description": "Gamma parameter for the RBF, laplacian, polynomial, exponential chi2 and sigmoid kernels",
                  },
                  "coef0": {
                      "anyOf": [{"type": "number"}, {"enum": [None]}],
                      "default": None,
                      "description": "Zero coefficient for polynomial and sigmoid kernels",
                  },
                  "degree": {
                      "anyOf": [{"type": "number"}, {"enum": [None]}],
                      "default": None,
                      "description": "Degree of the polynomial kernel",
                  },
                  "kernel_params": {
                      "XXX TODO XXX": "mapping of string to any, optional",
                      "description": "Additional parameters (keyword arguments) for kernel function passed as callable object.",
                      "enum": [None],
                      "default": None,
                  },
                  "n_components": {
                      "type": "integer",
                      "minimumForOptimizer": 2,
                      "maximumForOptimizer": 256,
                      "distribution": "uniform",
                      "default": 100,
                      "description": "Number of features to construct",
                  },
                  "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`.",
                  },
              },
          }
      ],
  }
  _input_fit_schema = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": "Fit estimator to data.",
      "type": "object",
      "required": ["X"],
      "properties": {
          "X": {
              "type": "array",
              "items": {"type": "array", "items": {"type": "number"}},
              "description": "Training data.",
          }
      },
  }
  _input_transform_schema = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": "Apply feature map to X.",
      "type": "object",
      "required": ["X"],
      "properties": {
          "X": {
              "type": "array",
              "items": {"type": "array", "items": {"type": "number"}},
              "description": "Data to transform.",
          }
      },
  }
  _output_transform_schema = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": "Transformed data.",
      "type": "array",
      "items": {"type": "array", "items": {"type": "number"}},
  }
  _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.kernel_approximation.Nystroem#sklearn-kernel_approximation-nystroem",
      "import_from": "sklearn.kernel_approximation",
      "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,
      },
  }
  Nystroem = make_operator(_NystroemImpl, _combined_schemas)
  
  set_docstrings(Nystroem)
  