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.
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docstr: Predict raw values for binned data.
  
  Parameters
  ----------
  X : ndarray, shape (n_samples, n_features)
      The input samples.
  missing_values_bin_idx : uint8
      Index of the bin that is used for missing values. This is the
      index of the last bin and is always equal to max_bins (as passed
      to the GBDT classes), or equivalently to n_bins - 1.
  n_threads : int
      Number of OpenMP threads to use.
  
  Returns
  -------
  y : ndarray, shape (n_samples,)
      The raw predicted values.
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code: def predict_binned(self, X, missing_values_bin_idx, n_threads):
      """Predict raw values for binned data.
  
      Parameters
      ----------
      X : ndarray, shape (n_samples, n_features)
          The input samples.
      missing_values_bin_idx : uint8
          Index of the bin that is used for missing values. This is the
          index of the last bin and is always equal to max_bins (as passed
          to the GBDT classes), or equivalently to n_bins - 1.
      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_binned_data(
          self.nodes,
          X,
          self.binned_left_cat_bitsets,
          missing_values_bin_idx,
          n_threads,
          out,
      )
      return out
  
file_path: scikit-learn/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 PREDICTOR_RECORD_DTYPE, Y_DTYPE
label_desc: Use image node_id 5 for calling the TreePredictor obj's underlying member method code with example usage: obj.predict_binned(X, missing_values_bin_idx, n_threads) and returns: out