text: 
  from sklearn.decomposition.incremental_pca import IncrementalPCA as SKLModel
  import lale.helpers
  import lale.operators
  import lale.docstrings
  from numpy import nan, inf
  
  class IncrementalPCAImpl():
  
      def __init__(self, n_components=None, whiten=False, copy=True, batch_size=None):
          self._hyperparams = {
              'n_components': n_components,
              'whiten': whiten,
              'copy': copy,
              'batch_size': batch_size}
          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 transform(self, X):
          return self._wrapped_model.transform(X)
  _hyperparams_schema = {
      '$schema': 'http://json-schema.org/draft-04/schema#',
      'description': 'inherited docstring for IncrementalPCA    Incremental principal components analysis (IPCA).',
      'allOf': [{
          'type': 'object',
          'required': ['n_components', 'whiten', 'copy', 'batch_size'],
          'relevantToOptimizer': ['n_components', 'whiten', 'copy', 'batch_size'],
          'additionalProperties': False,
          'properties': {
              'n_components': {
                  'anyOf': [{
                      'type': 'integer',
                      'minimumForOptimizer': 2,
                      'maximumForOptimizer': 256,
                      'distribution': 'uniform'}, {
                      'enum': [None]}],
                  'default': None,
                  'description': 'Number of components to keep'},
              'whiten': {
                  'type': 'boolean',
                  'default': False,
                  'description': 'When True (False by default) the ``components_`` vectors are divided by ``n_samples`` times ``components_`` to ensure uncorrelated outputs with unit component-wise variances'},
              'copy': {
                  'type': 'boolean',
                  'default': True,
                  'description': 'If False, X will be overwritten'},
              'batch_size': {
                  'anyOf': [{
                      'type': 'integer',
                      'minimumForOptimizer': 3,
                      'maximumForOptimizer': 128,
                      'distribution': 'uniform'}, {
                      'enum': [None]}],
                  'default': None,
                  'description': 'The number of samples to use for each batch'},
          }}, {
          'XXX TODO XXX': 'Parameter: batch_size > only used when calling fit'}],
  }
  _input_fit_schema = {
      '$schema': 'http://json-schema.org/draft-04/schema#',
      'description': 'Fit the model with X, using minibatches of size batch_size.',
      'type': 'object',
      'required': ['X', 'y'],
      'properties': {
          'X': {
              'type': 'array',
              'items': {
                  'type': 'array',
                  'items': {
                      'type': 'number'},
              },
              'description': 'Training data, where n_samples is the number of samples and n_features is the number of features.'},
          'y': {
              
          }},
  }
  _input_transform_schema = {
      '$schema': 'http://json-schema.org/draft-04/schema#',
      'description': 'Apply dimensionality reduction to X.',
      'type': 'object',
      'required': ['X'],
      'properties': {
          'X': {
              'type': 'array',
              'items': {
                  'type': 'array',
                  'items': {
                      'type': 'number'},
              },
              'description': 'New data, where n_samples is the number of samples and n_features is the number of features.'},
      },
  }
  _output_transform_schema = {
      '$schema': 'http://json-schema.org/draft-04/schema#',
      'description': 'Apply dimensionality reduction to X.',
      '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.decomposition.IncrementalPCA#sklearn-decomposition-incrementalpca',
      '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},
  }
  lale.docstrings.set_docstrings(IncrementalPCAImpl, _combined_schemas)
  IncrementalPCA = lale.operators.make_operator(IncrementalPCAImpl, _combined_schemas)
  
  