class_docstr: Tree class used for predictions.
  
  Parameters
  ----------
  nodes : ndarray of PREDICTOR_RECORD_DTYPE
      The nodes of the tree.
  binned_left_cat_bitsets : ndarray of shape (n_categorical_splits, 8),             dtype=uint32
      Array of bitsets for binned categories used in predict_binned when a
      split is categorical.
  raw_left_cat_bitsets : ndarray of shape (n_categorical_splits, 8),             dtype=uint32
      Array of bitsets for raw categories used in predict when a split is
      categorical.
docstr: Predict raw values for non-binned data.
  
  Parameters
  ----------
  X : ndarray, shape (n_samples, n_features)
      The input samples.
  
  known_cat_bitsets : ndarray of shape (n_categorical_features, 8)
      Array of bitsets of known categories, for each categorical feature.
  
  f_idx_map : ndarray of shape (n_features,)
      Map from original feature index to the corresponding index in the
      known_cat_bitsets array.
  
  n_threads : int
      Number of OpenMP threads to use.
  
  Returns
  -------
  y : ndarray, shape (n_samples,)
      The raw predicted values.
code: def predict(self, X, known_cat_bitsets, f_idx_map, n_threads):
      """Predict raw values for non-binned data.
  
      Parameters
      ----------
      X : ndarray, shape (n_samples, n_features)
          The input samples.
  
      known_cat_bitsets : ndarray of shape (n_categorical_features, 8)
          Array of bitsets of known categories, for each categorical feature.
  
      f_idx_map : ndarray of shape (n_features,)
          Map from original feature index to the corresponding index in the
          known_cat_bitsets array.
  
      n_threads : int
          Number of OpenMP threads to use.
  
      Returns
      -------
      y : ndarray, shape (n_samples,)
          The raw predicted values.
      """
      out = np.empty(X.shape[0], dtype=Y_DTYPE)
      _predict_from_raw_data(
          self.nodes,
          X,
          self.raw_left_cat_bitsets,
          known_cat_bitsets,
          f_idx_map,
          n_threads,
          out,
      )
      return out
  
file_path: catboost/contrib/python/scikit-learn/py3/sklearn/ensemble/_hist_gradient_boosting/predictor.py
imports: import numpy
  from ._predictor import _compute_partial_dependence, _predict_from_binned_data, _predict_from_raw_data
  from .common import Y_DTYPE
label_desc: Use image node_id 4 for calling the TreePredictor obj's underlying member method code with example usage: obj.predict(X, known_cat_bitsets, f_idx_map, n_threads) and returns: out