code: def plot_knn_multilabels(
      embeddings, multilabels, samples_idx, filenames, n_neighbors=50
  ):
      """Plots multiple rows of random images with their nearest neighbors"""
      # Let0s look at the nearest neighbors for some samples using the sklearn library.
      nbrs = NearestNeighbors(n_neighbors=n_neighbors).fit(embeddings)
      _, indices = nbrs.kneighbors(embeddings)
  
      # Position the bars.
      bar_width = 0.4
      r1 = np.arange(multilabels.shape[1])
      r2 = r1 + bar_width
  
      # Loop through our randomly picked samples.
      for idx in samples_idx:
          fig = plt.figure()
  
          bars1 = multilabels[idx]
          bars2 = np.mean(multilabels[indices[idx]], axis=0)
  
          plt.title(filenames[idx])
          plt.bar(
              r1,
              bars1,
              color="steelblue",
              edgecolor="black",
              width=bar_width,
          )
          plt.bar(
              r2,
              bars2,
              color="lightsteelblue",
              edgecolor="black",
              width=bar_width,
          )
          plt.xticks(0.5 * (r1 + r2), classes, rotation=90)
          plt.tight_layout()
  
imports: import copy
  import os
  import matplotlib.pyplot
  import numpy
  import pandas
  import pytorch_lightning
  import torch
  import torch.nn
  import torchvision
  from PIL import Image
  from sklearn.neighbors import NearestNeighbors
  from sklearn.preprocessing import normalize
  from lightly.data import LightlyDataset
  from lightly.loss import NTXentLoss
  from lightly.models.modules.heads import MoCoProjectionHead
  from lightly.models.utils import batch_shuffle, batch_unshuffle, deactivate_requires_grad, update_momentum
  from lightly.transforms.multi_view_transform import MultiViewTransform
label_desc: Use image node_id 3 for calling a global function with example usage: plot_knn_multilabels(embeddings, multilabels, samples_idx, filenames, n_neighbors) without return types