class_docstr: An abstract base class for a model for which data consists of a
  features matrix and a labels vector, namely for (one-class
  supervised learning)
  
  Attributes
  ----------
  features : `numpy.ndarray`, shape=(n_samples, n_features) (read-only)
      The features matrix
  
  labels : `numpy.ndarray`, shape=(n_samples,)  (read-only)
      The labels vector
  
  n_samples : `int`  (read-only)
      Number of samples
  
  n_features : `int` (read-only)
      Number of features
  
  dtype : `{'float64', 'float32'}`
      Type of the data arrays used.
  
  Notes
  -----
  This class should be not used by end-users, it is intended for
  development only.
docstr: Set the data into the model object
  
  Parameters
  ----------
  features : {`numpy.ndarray`, 'scipy.sparse.csr_matrix'}, shape=(n_samples, n_features)
      The features matrix, either dense or sparse
  
  labels : `numpy.ndarray`, shape=(n_samples,)
      The labels vector
  
  Returns
  -------
  output : a child of `ModelLabelsFeatures`
      The current instance with given data
code: def fit(self, features: np.ndarray, labels: np.ndarray) -> Model:
      """Set the data into the model object
  
      Parameters
      ----------
      features : {`numpy.ndarray`, 'scipy.sparse.csr_matrix'}, shape=(n_samples, n_features)
          The features matrix, either dense or sparse
  
      labels : `numpy.ndarray`, shape=(n_samples,)
          The labels vector
  
      Returns
      -------
      output : a child of `ModelLabelsFeatures`
          The current instance with given data
      """
      # The fit from Model calls the _set_data below
      return Model.fit(self, features, labels)
  
file_path: tick/tick/base_model/model_labels_features.py
imports: import numpy
  from .None import Model
  from tick.preprocessing.utils import safe_array
label_desc: Use image node_id 2 for calling the ModelLabelsFeatures obj's underlying member method code with example usage: obj.fit(features, labels) and returns: Model