code: def get_scatter_plot_with_thumbnails():
      """Creates a scatter plot with image overlays."""
      # initialize empty figure and add subplot
      fig = plt.figure()
      fig.suptitle("Scatter Plot of the Sentinel-2 Dataset")
      ax = fig.add_subplot(1, 1, 1)
      # shuffle images and find out which images to show
      shown_images_idx = []
      shown_images = np.array([[1.0, 1.0]])
      iterator = [i for i in range(embeddings_2d.shape[0])]
      np.random.shuffle(iterator)
      for i in iterator:
          # only show image if it is sufficiently far away from the others
          dist = np.sum((embeddings_2d[i] - shown_images) ** 2, 1)
          if np.min(dist) < 2e-3:
              continue
          shown_images = np.r_[shown_images, [embeddings_2d[i]]]
          shown_images_idx.append(i)
  
      # plot image overlays
      for idx in shown_images_idx:
          thumbnail_size = int(rcp["figure.figsize"][0] * 2.0)
          path = os.path.join(path_to_data, filenames[idx])
          img = Image.open(path)
          img = functional.resize(img, thumbnail_size)
          img = np.array(img)
          img_box = osb.AnnotationBbox(
              osb.OffsetImage(img, cmap=plt.cm.gray_r),
              embeddings_2d[idx],
              pad=0.2,
          )
          ax.add_artist(img_box)
  
      # set aspect ratio
      ratio = 1.0 / ax.get_data_ratio()
      ax.set_aspect(ratio, adjustable="box")
  
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 1 for calling a global function with example usage: get_scatter_plot_with_thumbnails() without return types