code: def plot_nearest_neighbors_3x3(example_image: str, i: int):
      """Plots the example image and its eight nearest neighbors."""
      n_subplots = 9
      # initialize empty figure
      fig = plt.figure()
      fig.suptitle(f"Nearest Neighbor Plot {i + 1}")
      #
      example_idx = filenames.index(example_image)
      # get distances to the cluster center
      distances = embeddings - embeddings[example_idx]
      distances = np.power(distances, 2).sum(-1).squeeze()
      # sort indices by distance to the center
      nearest_neighbors = np.argsort(distances)[:n_subplots]
      # show images
      for plot_offset, plot_idx in enumerate(nearest_neighbors):
          ax = fig.add_subplot(3, 3, plot_offset + 1)
          # get the corresponding filename
          fname = os.path.join(path_to_data, filenames[plot_idx])
          if plot_offset == 0:
              ax.set_title(f"Example Image")
              plt.imshow(get_image_as_np_array_with_frame(fname))
          else:
              plt.imshow(get_image_as_np_array(fname))
          # let's disable the axis
          plt.axis("off")
  
imports: import math
  import numpy
  import torch
  import torch.nn
  import torchvision
  from lightly.data import LightlyDataset
  from lightly.loss import NegativeCosineSimilarity
  from lightly.models.modules.heads import SimSiamPredictionHead, SimSiamProjectionHead
  from lightly.transforms import SimCLRTransform, utils
  import os
  import matplotlib.offsetbox
  import matplotlib.pyplot
  import torchvision.transforms.functional
  from matplotlib import rcParams
  from PIL import Image
  from sklearn import random_projection
label_desc: Use image node_id 4 for calling a global function with example usage: plot_nearest_neighbors_3x3(example_image, i) without return types