code: def plot_mask_cell(
      true_mask, predicted_mask, cell, suffix, ax1, ax2, ax3, padding=16
  ):
      """Plots a single cell with a its true mask and predicuted mask"""
      for ax in [ax1, ax2, ax3]:
          ax.grid(False)
          ax.set_xticks([])
          ax.set_yticks([])
      ax1.imshow(
          true_mask[padding:-padding, padding:-padding], cmap="viridis"
      )
      ax1.set_title("True Mask - {}".format(suffix))
      ax2.imshow(
          predicted_mask[padding:-padding, padding:-padding],
          cmap="viridis",
      )
      ax2.set_title("Predicted Mask - {}".format(suffix))
      ax3.imshow(convert_cell_to_img(cell, padding=padding))
      ax3.set_title("Image - {}".format(suffix))
      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 3 for calling a global function with example usage: plot_mask_cell(true_mask, predicted_mask, cell, suffix, ax1, ax2, ax3, padding) and returns: ax1,  ax2,  ax3