code: def mixup_modalities(
      batch: Dict["str", Any],
      alpha: float = 1.0,
      batch_first: bool = True,
      mixmod: bool = False,
      rng: Optional[Any] = None,
  ) -> Dict["str", jnp.ndarray]:
      """Mixes multimodal inputs and labels within a single batch.
  
      For more details, please see https://arxiv.org/abs/1710.09412.
  
      This function supports both using `numpy` to do mixup in the input-pipeline
      and `jax.numpy` to do mixup within a jitted/pmapped function (e.g. within
      a pmapped train step to apply mixup on device patch).
  
      Results in a batch with:
        mixed_inputs[idx] = weight * inputs[idx] + (1-weight) * inputs[-(idx+1)],
        where weight is sampled from a beta distribution with parameter alpha.
  
      Args:
        batch: dict; A batch of data with 'inputs' and 'label'. batch['inputs'] has
          field like 'rgb' or 'spectrogram'.
        alpha: float; Used to control the beta distribution that weight is sampled
          from.
        batch_first: bool; Batch is the first dimension or the last dimension.
        mixmod: bool; If True, applies mixup to each modality separately.
        rng: JAX rng key. If given, JAX numpy will be used as the backend, and if
          None (default value), normal numpy will be used.
  
      Returns:
        Tuple (mixed_images, mixed_labels).
      """
      inputs, labels = batch["inputs"], batch["label"]
      batch["label"] = {}
      num_modalities = len(inputs)
  
      if labels.shape[-1] == 1:
          raise ValueError("Mixup requires one-hot targets.")
  
      batch_size = labels.shape[0]
  
      # Setup the the numpy backend and prepare mixup weights.
      if rng is None:
          np_backend = np  # Ordinary numpy
          if mixmod:
              weights = list(
                  np_backend.random.beta(
                      alpha, alpha, size=num_modalities
                  )
              )
          else:
              weights = [
                  np_backend.random.beta(alpha, alpha)
              ] * num_modalities
      else:
          np_backend = jnp  # JAX numpy
          if mixmod:
              weights = list(
                  jax.random.beta(
                      rng, alpha, alpha, shape=[num_modalities]
                  )
              )
          else:
              weights = [
                  jax.random.beta(rng, alpha, alpha)
              ] * num_modalities
      for i in range(num_modalities):
          weights[i] *= np_backend.ones((batch_size, 1))
  
      # Mixup inputs.
      # Shape calculations use np to avoid device memory fragmentation:
      for modality, values in inputs.items():
          weight = weights[len(batch["label"])]
          # Mixup labels.
          batch["label"][modality] = (
              weight * labels + (1.0 - weight) * labels[::-1]
          )
          weight_shape = np.ones((values.ndim))
          if batch_first:
              weight_shape[0] = batch_size
          else:
              weight_shape[-1] = batch_size
          weight = np_backend.reshape(
              weight, weight_shape.astype(np_backend.int32)
          )
          reverse = []
          for i in range(values.ndim):
              if (i == 0 and batch_first) or (
                  i == values.ndim - 1 and not batch_first
              ):
                  reverse.append(slice(-1, None, -1))
              else:
                  reverse.append(slice(values.shape[i]))
          batch["inputs"][modality] = (
              weight * values + (1.0 - weight) * values[tuple(reverse)]
          )
      if num_modalities == 1 or not mixmod:
          batch["label"]["all"] = (
              weights[0] * labels + (1.0 - weights[0]) * labels[::-1]
          )
  
      return batch
  
imports: import copy
  import functools
  from typing import Any, Callable, Dict, Optional, Tuple, Union
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  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.dataset_lib import dataset_utils
  from scenic.projects.mbt import train_utils
  from scenic.projects.vivit import evaluation_lib
  from scenic.projects.vivit 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 1 for calling a global function with example usage: mixup_modalities(batch, alpha, batch_first, mixmod, rng) and returns: batch