code: def _seaborn_viz_cluster_search_plot(
      cluster_range: Tuple[int, int],
      scores: List[Union[int, float]],
      metric: str,
      **kwargs,
  ):
      """Visualize the cluster search plot for K-means clusters.
  
      Args:
          cluster_range (Tuple[int, int]): The range of n_clusters (k)
              searched as (min_cluster, max_cluster)
          scores (List[Union[int, float]]): The scores from the evaluation
              metric used to determine the "optimal" n_clusters.
          metric (str): The evaluation metric used.
          **kwargs: Keyword arguments.
  
      Returns:
          Seaborn plot
      """
      n_clusters = list(range(*cluster_range))
      plt.figure(
          figsize=(
              get_option("display.matplotlib.fig_width"),
              get_option("display.matplotlib.fig_height"),
          )
      )
      ax = sns.lineplot(n_clusters, scores)
      ax.set_title("Optimal Number of Clusters")
      ax.xaxis.set_major_locator(MaxNLocator(integer=True))
      plt.xlabel("Number of Clusters")
      plt.ylabel(f"{' '.join(metric.split('_'))}")
      return ax
  
imports: from typing import Optional, Tuple, List, Union
  import pandas
  import numpy
  import seaborn
  import sklearn
  from sklearn.cluster import KMeans
  import matplotlib.pyplot
  from matplotlib.ticker import MaxNLocator
  from sklearn.preprocessing import StandardScaler
  import plotly.graph_objs
  import plotly.offline
  from data_describe.config._config import get_option
  from data_describe._widget import BaseWidget
  from data_describe.compat import _is_dataframe, _compat, _requires, _in_notebook
  from data_describe.backends import _get_viz_backend, _get_compute_backend
  from data_describe.dimensionality_reduction.dimensionality_reduction import dim_reduc
label_desc: Use image node_id 9 for calling a global function with example usage: _seaborn_viz_cluster_search_plot(cluster_range, scores, metric) and returns: ax