code: def plot_mask_cells(mask_cells, padding=16):
      """Plots cells with their true mask, predicted mask.
  
      Parameters
      ----------
      mask_cells: list of tuples (`true_mask`, `predicted_mask`, `cell`)
      padding: int (default=16)
          Padding around mask to remove.
      """
      fig, axes = plt.subplots(len(mask_cells), 3, figsize=(12, 10))
      for idx, (axes, mask_cell) in enumerate(zip(axes, mask_cells), 1):
          ax1, ax2, ax3 = axes
          true_mask, predicted_mask, cell = mask_cell
          plot_mask_cell(
              true_mask,
              predicted_mask,
              cell,
              "Type {}".format(idx),
              ax1,
              ax2,
              ax3,
              padding=padding,
          )
      fig.tight_layout()
      return fig, axes
  
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 2 for calling a global function with example usage: plot_mask_cells(mask_cells, padding) and returns: fig,  axes