code: def test_kmeans_default(kmeans_default):
      cl = kmeans_default
      assert isinstance(
          cl.show(), matplotlib.axes.Axes
      ), "Default show() didn't return a mpl Axes object"
      assert isinstance(cl, ClusterWidget)
      assert isinstance(
          cl.estimator, KMeans
      ), "Saved cluster estimator was not KMeans"
      assert hasattr(
          cl, "input_data"
      ), "Widget does not have input data"
      assert _is_dataframe(
          cl.input_data
      ), "Input data is not a data frame"
      assert hasattr(
          cl, "scaled_data"
      ), "Widget does not have standardized data"
      assert _is_dataframe(
          cl.scaled_data
      ), "Scaled data is not a data frame"
      assert hasattr(
          cl, "viz_data"
      ), "Widget does not have visualization (reduced) data"
      assert _is_dataframe(cl.viz_data), "Viz data is not a data frame"
      assert cl.clusters is not None, "Widget is missing cluster labels"
      assert (
          cl.n_clusters == 19
      ), "Expected number of clusters found to be 19"
      assert isinstance(
          cl.cluster_range, tuple
      ), "Widget is missing cluster range tuple"
      assert (
          len(cl.cluster_range) == 2
      ), "Cluster range tuple had the wrong shape"
      assert isinstance(cl.cluster_range[0], int) and isinstance(
          cl.cluster_range[1], int
      ), "Cluster range tuple does not contain integers"
      assert (
          cl.cluster_range[0] < cl.cluster_range[1]
      ), "Cluster range had an invalid default; the maximum is <= the minimum"
      assert (
          cl.metric == "silhouette_score"
      ), "Default search metric was not silhouette_score"
      assert (
          cl.scores is not None
      ), "Widget is missing cluster search scores"
      assert (
          cl.search
      ), "Widget is missing boolean attribute 'search' for cluster search"
      assert isinstance(cl.show(), mpl_plot)
      assert hasattr(cl, "cluster_search_plot")
      assert isinstance(cl.cluster_search_plot(), mpl_plot)
      assert hasattr(cl, "reductor")
  
imports: import numpy
  import pytest
  import matplotlib
  from matplotlib.axes import Axes
  from sklearn.cluster import KMeans
  from sklearn.preprocessing import StandardScaler
  import plotly
  import data_describe
  from data_describe.compat import _is_dataframe
  from data_describe.core.clustering import ClusterWidget, _pandas_compute_cluster, _run_kmeans, _find_clusters, _fit_kmeans, _run_hdbscan
label_desc: Use image node_id 6 for calling a global function with example usage: test_kmeans_default(kmeans_default) without return types