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', 'flow', spectrogram', 'waveform' or 'text'.
        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]
  
      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] *= jnp.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 = jnp.reshape(weight, weight_shape.astype(jnp.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 functools
  from typing import Any, Callable, Dict, Optional, Tuple, Union
  from absl import logging
  from clu import platform
  import flax.linen
  import jax
  import jax.numpy
  import matplotlib.pyplot
  import numpy
  from scenic.model_lib.base_models import model_utils
  from scenic.model_lib.layers import nn_ops
  from scenic.train_lib import train_utils
  from scenic.train_lib_deprecated import train_utils
label_desc: Use image node_id 11 for calling a global function with example usage: mixup_modalities(batch, alpha, batch_first, mixmod, rng) and returns: batch