code: def plot_masks(mask_1, mask_2, mask_3):
      """Plots three masks"""
      fig, ((ax1, ax2, ax3)) = plt.subplots(1, 3, figsize=(12, 5))
      for ax in [ax1, ax2, ax3]:
          ax.grid(False)
          ax.set_xticks([])
          ax.set_yticks([])
      ax1.set_title("Type 1")
      ax1.imshow(mask_1, cmap="viridis")
      ax2.set_title("Type 2")
      ax2.imshow(mask_2, cmap="viridis")
      ax3.set_title("Type 3")
      ax3.imshow(mask_3, cmap="viridis")
      return ax1, ax2, ax3
  
file_path: skorch/examples/nuclei_image_segmentation/utils.py
imports: import torch
  import torch.nn
  from torch.nn.functional import binary_cross_entropy_with_logits
  from torchvision.transforms.functional import to_pil_image
  import matplotlib.pyplot
label_desc: Use image node_id 4 for calling a global function with example usage: plot_masks(mask_1, mask_2, mask_3) and returns: ax1,  ax2,  ax3