class_docstr: Drift detector based on the Wasserstein distance.
   (see T. Viehmann: Partial Wasserstein and Maximum Mean Discrepancy distances
   for bridging the gap between outlier detection and drift detection,
   https://arxiv.org/abs/2106.01289 )
  
  Args:
        fraction_to_match: fraction of x probability mass to be matched
        n_perm: number of bootstrap permutations to run to compute p-value (None for not returning a p-value)
   
code: def __init__(
      self,
      *,
      return_p_value=False,
      n_perm=1000,
      fraction_to_match=1.0,
      wasserstein_p=2.0,
  ):
      super().__init__(return_p_value=return_p_value)
      self.fraction_to_match = fraction_to_match
      self.wasserstein_p = wasserstein_p
      self.n_perm = n_perm
      self.n_test = None
      self.scores = None
  
file_path: TorchDrift/torchdrift/detectors/wasserstein.py
imports: from typing import Optional
  import torch
  from .None import Detector
  import torchdrift.utils
label_desc: Use image node_id 1 to create a new WassersteinDriftDetector object from inherited base classes: Detector with example: obj = WassersteinDriftDetector()