class_docstr: Calculate trust score.
  
  Parameters
  ----------
  k_filter : int , default: 10
      Number of neighbors used during either kNN distance or probability filtering.
  alpha : float , default: 0.
      Fraction of instances to filter out to reduce impact of outliers.
  filter_type : str , default: distance_knn
      Filter method; either 'distance_knn' or 'probability_knn'
  leaf_size : int , default: 40
      Number of points at which to switch to brute-force. Affects speed and memory required to
      build trees. Memory to store the tree scales with n_samples / leaf_size.
  metric : str , default: euclidean
      Distance metric used for the tree. See sklearn's DistanceMetric class for a list of available
      metrics.
  dist_filter_type : str , default: point
      Use either the distance to the k-nearest point (dist_filter_type = 'point') or
      the average distance from the first to the k-nearest point in the data (dist_filter_type = 'mean').
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code: def __init__(
      self,
      k_filter: int = 10,
      alpha: float = 0.0,
      filter_type: str = "distance_knn",
      leaf_size: int = 40,
      metric: str = "euclidean",
      dist_filter_type: str = "point",
  ) -> None:
      super().__init__()
      self.k_filter = k_filter
      self.alpha = alpha
      self.filter = filter_type
      self.eps = 1e-12
      self.leaf_size = leaf_size
      self.metric = metric
      self.dist_filter_type = dist_filter_type
  
file_path: deepchecks/deepchecks/utils/distribution/trust_score.py
imports: from typing import Tuple
  import numpy
  from sklearn.neighbors import KDTree, KNeighborsClassifier
  from deepchecks.utils.logger import get_logger
label_desc: Use image node_id 1 to create a new TrustScore object  with example: obj = TrustScore(k_filter, alpha, filter_type, leaf_size, metric, dist_filter_type)