code: def get_image_grid(
      targets: jnp.ndarray,
      predictions: jnp.ndarray,
      token_mask: jnp.ndarray,
      config: ml_collections.ConfigDict,
      target_modality: str,
      input_size: Optional[
          Union[Tuple[int, int, int], Tuple[int, int, int, int]]
      ] = None,
  ) -> Optional[jnp.ndarray]:
      """Returns an image grid for summary writing."""
  
      image_grid = None
  
      n_columns = config.masked_feature_loss.get(
          "summary_num_columns", 1
      )
      if (
          len(config.model.patches.size) == 2
          or target_modality == "spectrogram"
      ):
          patch_size = (
              tuple(config.model.patches.size[:2])
              if (target_modality == "spectrogram")
              else tuple(config.model.patches.size)
          )
  
          image_grid = avmae_train_utils.generate_image_grid(
              targets,
              predictions,
              token_mask,
              patch_size,
              n_columns=n_columns,
              input_size=input_size,
              modality=target_modality,
          )
      elif len(config.model.patches.size) == 3:
          num_img_in_column = config.masked_feature_loss.get(
              "number_of_img_in_column", 16
          )
          select_central_frame = config.masked_feature_loss.get(
              "select_central_frame", True
          )
  
          assert (
              input_size is not None
          ), "Input size must be provided for video!"
  
          image_grid = avmae_train_utils.generate_image_grid_from_video(
              targets,
              predictions,
              token_mask,
              tuple(config.model.patches.size),
              input_size,
              num_img_in_column=num_img_in_column,
              select_central_frame=select_central_frame,
          )
      else:
          raise ValueError(
              "The visualization is not implemented for"
              f"{config.model.patches} patches!"
          )
      return image_grid
  
imports: import functools
  from typing import Any, Callable, Dict, Iterator, Mapping, Optional, Tuple, Type, Union
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  import flax
  from flax import jax_utils
  import flax.linen
  import jax
  from jax.example_libraries.optimizers import clip_grads
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  from scenic.common_lib import debug_utils
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.av_mae import base_model
  from scenic.projects.av_mae import train_utils
  from scenic.train_lib_deprecated import lr_schedules
  from scenic.train_lib_deprecated import optimizers
  from scenic.train_lib_deprecated import pretrain_utils
  from scenic.train_lib_deprecated import train_utils
label_desc: Use image node_id 6 for calling a global function with example usage: get_image_grid(targets, predictions, token_mask, config, target_modality, input_size) and returns: image_grid