code: 
  from sklearn.linear_model.passive_aggressive import PassiveAggressiveRegressor as SKLModel
  import lale.helpers
  import lale.operators
  import lale.docstrings
  from numpy import nan, inf
  
  class PassiveAggressiveRegressorImpl():
  
      def __init__(self, C=1.0, fit_intercept=True, max_iter=None, tol=None, early_stopping=False, validation_fraction=0.1, n_iter_no_change=5, shuffle=True, verbose=0, loss='epsilon_insensitive', epsilon=0.1, random_state=None, warm_start=False, average=False, n_iter=None):
          self._hyperparams = {
              'C': C,
              'fit_intercept': fit_intercept,
              'max_iter': max_iter,
              'tol': tol,
              'early_stopping': early_stopping,
              'validation_fraction': validation_fraction,
              'n_iter_no_change': n_iter_no_change,
              'shuffle': shuffle,
              'verbose': verbose,
              'loss': loss,
              'epsilon': epsilon,
              'random_state': random_state,
              'warm_start': warm_start,
              'average': average,
              'n_iter': n_iter}
          self._wrapped_model = SKLModel(**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 PassiveAggressiveRegressor    Passive Aggressive Regressor',
      'allOf': [{
          'type': 'object',
          'required': ['C', 'fit_intercept', 'max_iter', 'tol', 'early_stopping', 'validation_fraction', 'n_iter_no_change', 'shuffle', 'verbose', 'loss', 'epsilon', 'random_state', 'warm_start', 'average', 'n_iter'],
          'relevantToOptimizer': ['fit_intercept', 'max_iter', 'tol', 'shuffle', 'loss', 'epsilon', 'n_iter'],
          'additionalProperties': False,
          'properties': {
              'C': {
                  'type': 'number',
                  'default': 1.0,
                  'description': 'Maximum step size (regularization)'},
              'fit_intercept': {
                  'type': 'boolean',
                  'default': True,
                  'description': 'Whether the intercept should be estimated or not'},
              'max_iter': {
                  'anyOf': [{
                      'type': 'integer',
                      'minimumForOptimizer': 10,
                      'maximumForOptimizer': 1000,
                      'distribution': 'uniform'}, {
                      'enum': [None]}],
                  'default': None,
                  'description': 'The maximum number of passes over the training data (aka epochs)'},
              'tol': {
                  'anyOf': [{
                      'type': 'number',
                      'minimumForOptimizer': 1e-08,
                      'maximumForOptimizer': 0.01,
                      'distribution': 'uniform'}, {
                      'enum': [None]}],
                  'default': None,
                  'description': 'The stopping criterion'},
              'early_stopping': {
                  'type': 'boolean',
                  'default': False,
                  'description': 'Whether to use early stopping to terminate training when validation'},
              'validation_fraction': {
                  'type': 'number',
                  'default': 0.1,
                  'description': 'The proportion of training data to set aside as validation set for early stopping'},
              'n_iter_no_change': {
                  'type': 'integer',
                  'default': 5,
                  'description': 'Number of iterations with no improvement to wait before early stopping'},
              'shuffle': {
                  'type': 'boolean',
                  'default': True,
                  'description': 'Whether or not the training data should be shuffled after each epoch.'},
              'verbose': {
                  'type': 'integer',
                  'default': 0,
                  'description': 'The verbosity level'},
              'loss': {
                  'enum': ['huber', 'squared_epsilon_insensitive', 'squared_loss', 'epsilon_insensitive'],
                  'default': 'epsilon_insensitive',
                  'description': 'The loss function to be used: epsilon_insensitive: equivalent to PA-I in the reference paper'},
              'epsilon': {
                  'type': 'number',
                  'minimumForOptimizer': 1e-08,
                  'maximumForOptimizer': 1.35,
                  'distribution': 'uniform',
                  'default': 0.1,
                  'description': 'If the difference between the current prediction and the correct label is below this threshold, the model is not updated.'},
              'random_state': {
                  'anyOf': [{
                      'type': 'integer'}, {
                      'type': 'object'}, {
                      'enum': [None]}],
                  'default': None,
                  'description': 'The seed of the pseudo random number generator to use when shuffling the data'},
              'warm_start': {
                  'type': 'boolean',
                  'default': False,
                  'description': 'When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution'},
              'average': {
                  'anyOf': [{
                      'type': 'boolean'}, {
                      'type': 'integer'}],
                  'default': False,
                  'description': 'When set to True, computes the averaged SGD weights and stores the result in the ``coef_`` attribute'},
              'n_iter': {
                  'anyOf': [{
                      'type': 'integer',
                      'minimumForOptimizer': 5,
                      'maximumForOptimizer': 1000,
                      'distribution': 'uniform'}, {
                      'enum': [None]}],
                  'default': None,
                  'description': 'The number of passes over the training data (aka epochs)'},
          }}, {
          'XXX TODO XXX': 'Parameter: max_iter > only impacts the behavior in the fit method'}, {
          'description': 'validation_fraction, only used if early_stopping is true',
          'anyOf': [{
              'type': 'object',
              'properties': {
                  'validation_fraction': {
                      'enum': [0.1]},
              }}, {
              'type': 'object',
              'properties': {
                  'early_stopping': {
                      'enum': [True]},
              }}]}],
  }
  _input_fit_schema = {
      '$schema': 'http://json-schema.org/draft-04/schema#',
      'description': 'Fit linear model with Passive Aggressive algorithm.',
      'type': 'object',
      'required': ['X', 'y'],
      'properties': {
          'X': {
              'type': 'array',
              'items': {
                  'type': 'array',
                  'items': {
                      'type': 'number'},
              },
              'description': 'Training data'},
          'y': {
              'type': 'array',
              'items': {
                  'type': 'number'},
              'description': 'Target values'},
          'coef_init': {
              'type': 'array',
              'items': {
                  'type': 'number'},
              'description': 'The initial coefficients to warm-start the optimization.'},
          'intercept_init': {
              'type': 'array',
              'items': {
                  'type': 'number'},
              'description': 'The initial intercept to warm-start the optimization.'},
      },
  }
  _input_predict_schema = {
      '$schema': 'http://json-schema.org/draft-04/schema#',
      'description': 'Predict using the linear model',
      'type': 'object',
      'required': ['X'],
      'properties': {
          'X': {
              'type': 'array',
              'items': {
                  'type': 'array',
                  'items': {
                      'type': 'number'},
              }},
      },
  }
  _output_predict_schema = {
      '$schema': 'http://json-schema.org/draft-04/schema#',
      'description': 'Predicted target values per element in X.',
      '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.PassiveAggressiveRegressor#sklearn-linear_model-passiveaggressiveregressor',
      'type': 'object',
      'tags': {
          'pre': [],
          'op': ['estimator', 'regressor'],
          'post': []},
      'properties': {
          'hyperparams': _hyperparams_schema,
          'input_fit': _input_fit_schema,
          'input_predict': _input_predict_schema,
          'output_predict': _output_predict_schema},
  }
  lale.docstrings.set_docstrings(PassiveAggressiveRegressorImpl, _combined_schemas)
  PassiveAggressiveRegressor = lale.operators.make_operator(PassiveAggressiveRegressorImpl, _combined_schemas)
  
  
apis: sklearn.linear_model.passive_aggressive.PassiveAggressiveRegressor