code: def plot_series(X: TimeSeriesInstances):
      _check_soft_dependencies("matplotlib")
      import matplotlib.patches as mpatches
      import matplotlib.pyplot as plt
  
      if isinstance(X, pd.DataFrame):
          X = convert_to(X, "numpy3D")
      plt.figure(figsize=(5, 10))
      plt.rcParams["figure.dpi"] = 100
  
      fig, axes = plt.subplots(nrows=len(X), ncols=1)
      for i in range(len(X)):
          curr = X[i][0]
          curr_axes = axes[i]
          curr_axes.plot(curr, color="b")
  
      blue_patch = mpatches.Patch(
          color="blue", label="Series that belong to the cluster"
      )
      plt.legend(
          handles=[blue_patch],
          loc="upper center",
          bbox_to_anchor=(0.5, -0.40),
          fancybox=True,
          shadow=True,
          ncol=5,
      )
      plt.tight_layout()
      plt.show()
  
file_path: sktime/sktime/clustering/utils/plotting/_plot_partitions.py
imports: import numpy
  import pandas
  from sktime.clustering.base import TimeSeriesInstances
  from sktime.clustering.partitioning import TimeSeriesLloyds
  from sktime.datatypes import convert_to
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 3 for calling a global function with example usage: plot_series(X) without return types