text: from numpy import inf, nan
  from sklearn.linear_model import LinearRegression as Op
  
  from lale.docstrings import set_docstrings
  from lale.operators import make_operator
  
  
  class _LinearRegressionImpl:
      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)
  
  
  _hyperparams_schema = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": "inherited docstring for LinearRegression    Ordinary least squares Linear Regression.",
      "allOf": [
          {
              "type": "object",
              "required": ["fit_intercept", "normalize", "copy_X", "n_jobs"],
              "relevantToOptimizer": ["fit_intercept", "normalize", "copy_X"],
              "additionalProperties": False,
              "properties": {
                  "fit_intercept": {
                      "type": "boolean",
                      "default": True,
                      "description": "whether to calculate the intercept for this model",
                  },
                  "normalize": {
                      "type": "boolean",
                      "default": False,
                      "description": "This parameter is ignored when ``fit_intercept`` is set to False",
                  },
                  "copy_X": {
                      "type": "boolean",
                      "default": True,
                      "description": "If True, X will be copied; else, it may be overwritten.",
                  },
                  "n_jobs": {
                      "anyOf": [{"type": "integer"}, {"enum": [None]}],
                      "default": 1,
                      "description": "The number of jobs to use for the computation",
                  },
              },
          },
          {
              "XXX TODO XXX": "Parameter: n_jobs > only provide speedup for n_targets > 1 and sufficient large problems"
          },
      ],
  }
  _input_fit_schema = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": "Fit linear model.",
      "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, shape (n_samples, n_features)",
                  },
                  {
                      "type": "array",
                      "items": {"type": "array", "items": {"type": "number"}},
                  },
              ],
              "description": "Training data",
          },
          "y": {
              "type": "array",
              "items": {"type": "array", "items": {"type": "number"}},
              "description": "Target values",
          },
          "sample_weight": {
              "type": "array",
              "items": {"type": "number"},
              "description": "Individual weights for each sample  ",
          },
      },
  }
  _input_predict_schema = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": "Predict using the linear model",
      "type": "object",
      "required": ["X"],
      "properties": {
          "X": {
              "anyOf": [
                  {
                      "type": "array",
                      "items": {"laleType": "Any", "XXX TODO XXX": "item type"},
                      "XXX TODO XXX": "array_like or sparse matrix, shape (n_samples, n_features)",
                  },
                  {
                      "type": "array",
                      "items": {"type": "array", "items": {"type": "number"}},
                  },
              ],
              "description": "Samples.",
          }
      },
  }
  _output_predict_schema = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": "Returns predicted values.",
      "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.linear_model.LinearRegression#sklearn-linear_model-linearregression",
      "import_from": "sklearn.linear_model",
      "type": "object",
      "tags": {"pre": [], "op": ["estimator"], "post": []},
      "properties": {
          "hyperparams": _hyperparams_schema,
          "input_fit": _input_fit_schema,
          "input_predict": _input_predict_schema,
          "output_predict": _output_predict_schema,
      },
  }
  LinearRegression = make_operator(_LinearRegressionImpl, _combined_schemas)
  
  set_docstrings(LinearRegression)
  