code: def get_image_as_np_array_with_frame(filename: str, w: int = 5):
      """Returns an image as a numpy array with a black frame of width w."""
      img = get_image_as_np_array(filename)
      ny, nx, _ = img.shape
      # create an empty image with padding for the frame
      framed_img = np.zeros((w + ny + w, w + nx + w, 3))
      framed_img = framed_img.astype(np.uint8)
      # put the original image in the middle of the new one
      framed_img[w:-w, w:-w] = img
      return framed_img
  
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 3 for calling a global function with example usage: get_image_as_np_array_with_frame(filename, w) and returns: framed_img