code: def _to_grid(self, images):
      return torchvision.utils.make_grid(
          tensor=images,
          nrow=self.nrow,
          padding=self.padding,
          normalize=self.normalize,
          value_range=self.value_range,
          scale_each=self.scale_each,
          pad_value=self.pad_value,
      )
  
imports: from os import path
  from typing import Optional, Tuple
  import torch
  import torch.nn.functional
  from lightning.pytorch import LightningDataModule, LightningModule, Trainer, callbacks, cli_lightning_logo
  from lightning.pytorch.cli import LightningCLI
  from lightning.pytorch.demos.mnist_datamodule import MNIST
  from lightning.pytorch.utilities import rank_zero_only
  from lightning.pytorch.utilities.imports import _TORCHVISION_AVAILABLE
  from torch import nn
  from torch.utils.data import DataLoader, random_split
label_desc: Use image node_id 2 for calling the ImageSampler obj's underlying member method code with example usage: obj._to_grid(images) and returns: torchvision