code: def _get_metric_params_list(
      metric: str, n_features: int, seed: int = 1
  ):
      """Return list of dummy DistanceMetric kwargs for tests."""
  
      # Distinguishing on cases not to compute unneeded datastructures.
      rng = np.random.RandomState(seed)
  
      if metric == "minkowski":
          minkowski_kwargs = [
              dict(p=1.5),
              dict(p=2),
              dict(p=3),
              dict(p=np.inf),
              dict(p=3, w=rng.rand(n_features)),
          ]
  
          return minkowski_kwargs
  
      if metric == "seuclidean":
          return [dict(V=rng.rand(n_features))]
  
      # Case of: "euclidean", "manhattan", "chebyshev", "haversine" or any other metric.
      # In those cases, no kwargs is needed.
      return [{}]
  
imports: import itertools
  import re
  import warnings
  from functools import partial
  import numpy
  import pytest
  import threadpoolctl
  from scipy.spatial.distance import cdist
  from sklearn.metrics import euclidean_distances, pairwise_distances
  from sklearn.metrics._pairwise_distances_reduction import ArgKmin, ArgKminClassMode, BaseDistancesReductionDispatcher, RadiusNeighbors, RadiusNeighborsClassMode, sqeuclidean_row_norms
  from sklearn.utils._testing import assert_allclose, assert_array_equal, create_memmap_backed_data
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 1 for calling a global function with example usage: _get_metric_params_list(metric, n_features, seed) and returns: list, minkowski_kwargs, list