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.
code: def __init__(
      self, nodes, binned_left_cat_bitsets, raw_left_cat_bitsets
  ):
      self.nodes = nodes
      self.binned_left_cat_bitsets = binned_left_cat_bitsets
      self.raw_left_cat_bitsets = raw_left_cat_bitsets
  
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 1 to create a new TreePredictor object  with example: obj = TreePredictor(nodes, binned_left_cat_bitsets, raw_left_cat_bitsets)