class_docstr: Mixin for k-neighbors searches on Time Series.
code: def _get_metric_params(self):
      if self.metric_params is None:
          metric_params = {}
      else:
          metric_params = self.metric_params.copy()
      if "gamma_sdtw" in metric_params.keys():
          metric_params["gamma"] = metric_params["gamma_sdtw"]
          del metric_params["gamma_sdtw"]
      if "n_jobs" in metric_params.keys():
          del metric_params["n_jobs"]
      if "verbose" in metric_params.keys():
          del metric_params["verbose"]
      return metric_params
  
imports: import numpy
  from sklearn import neighbors
  from sklearn.neighbors import KNeighborsClassifier, NearestNeighbors, KNeighborsRegressor
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from scipy.spatial.distance import cdist
  from tslearn.metrics import cdist_dtw, cdist_ctw, cdist_soft_dtw, cdist_sax, TSLEARN_VALID_METRICS
  from tslearn.piecewise import SymbolicAggregateApproximation
  from tslearn.utils import to_time_series_dataset, to_sklearn_dataset, check_dims
  from tslearn.bases import BaseModelPackage
label_desc: Use image node_id 2 for calling the KNeighborsTimeSeriesMixin obj's underlying member method code with example usage: obj._get_metric_params() and returns: metric_params