code: def accumulate_gradients(
      compute_gradient_fn: Callable[
          [TrainState, Dict[str, jnp.ndarray], jnp.ndarray],
          Tuple[Any, jnp.ndarray],
      ],
      metrics_fn: Callable[
          [jnp.ndarray, Dict[str, jnp.ndarray]],
          Dict[str, Tuple[float, int]],
      ],
      train_state: TrainState,
      batch: Dict[str, jnp.ndarray],
      dropout_rng: jnp.ndarray,
      accum_steps: Optional[int],
  ) -> Tuple[
      Optional[jnp.ndarray],
      jnp.ndarray,
      jnp.ndarray,
      Dict[str, Tuple[float, int]],
  ]:
      """Accumulate gradients over multiple steps.
  
      This enables training with larger effective batch sizes.
      Note that currently, gradient accumulation is not supported when the
      `model_state` is used, e.g., for models that have batch normalization and
      store batch statistics in the `model_state`.
  
      Note that if `accum_steps` <= 1 or is None, then the gradient of a single step
      is simply returned.
  
      Args:
        compute_gradient_fn: Gradient function, e.g., `jax.value_and_grad(
          training_loss_fn, ...).
        metrics_fn: A metrics function that given logits and batch of data,
          calculates the metrics.
        train_state: An instance of TrainState that has the parameters of the model,
          state of the model, etc.
        batch: A single batch of data. The buffer of this argument can be donated to
          the computation.
        dropout_rng: JAX rng key used for dropout.
        accum_steps: Number of accumulating steps (number of micro batches). When
          set to None or =<1, no accumulation is done.
  
      Returns:
        A tuple of model_state (e.g., batch statistics),
          computed gradients, training loss, and calculated metrics.
      """
      params = train_state.params
      if accum_steps and accum_steps > 1:
          batch_size = next(iter(batch.values())).shape[0]
          microbatch_size = batch_size // accum_steps
          if batch_size % accum_steps != 0:
              raise ValueError(
                  f"Bad accum_steps {accum_steps} for batch size {batch_size}"
              )
          logging.info(
              "Using microbatches: %d microbatches, %d size",
              accum_steps,
              microbatch_size,
          )
  
          def get_microbatch(
              batch: Dict[str, jnp.ndarray], idx: int
          ) -> Dict[str, jnp.ndarray]:
              """Fetch microbatch slice from the given batch."""
              return jax.tree_map(
                  lambda x: x.reshape(
                      (-1, microbatch_size) + x.shape[1:]
                  )[idx],
                  batch,
              )
  
          def per_microbatch_compute_gradient_fn(
              loop_cnt: int,
              loop_state: Tuple[
                  jnp.ndarray,
                  jnp.ndarray,
                  jnp.ndarray,
                  Dict[str, Tuple[float, int]],
              ],
          ) -> Tuple[
              jnp.ndarray,
              jnp.ndarray,
              Dict[str, Tuple[float, int]],
              jnp.ndarray,
          ]:
              (
                  dropout_rng,
                  grad_accum,
                  train_loss_acc,
                  metrics_acc,
              ) = loop_state
              dropout_rng, sub_dropout_rng = jax.random.split(
                  dropout_rng
              )
              mbatch = get_microbatch(batch, loop_cnt)
              (train_loss, (_, mlogits)), grad = compute_gradient_fn(
                  params, mbatch, sub_dropout_rng
              )
              metrics = metrics_fn(mlogits, mbatch)
              # Accumulate gradients and metrics.
              grad = jax.tree_util.tree_map(jnp.add, grad_accum, grad)
              metrics = jax.tree_util.tree_map(
                  jnp.add, metrics, metrics_acc
              )
              train_loss = jax.tree_util.tree_map(
                  jnp.add, train_loss, train_loss_acc
              )
              return dropout_rng, grad, train_loss, metrics
  
          # Initialize gradient accumulation loop state.
          dropout_rng, sub_dropout_rng = jax.random.split(dropout_rng)
          init_mbatch = get_microbatch(batch, 0)
          (
              init_train_loss,
              (model_state, init_logits),
          ), grad_init = compute_gradient_fn(
              params, init_mbatch, sub_dropout_rng
          )
          if jax.tree_leaves(model_state):
              # If the model_state is not empty.
              raise ValueError(
                  "Gradient accumulation is not supported when the "
                  "model_state is in used (e.g. models w/ batch norm)."
              )
  
          metrics_init = metrics_fn(init_logits, init_mbatch)
          del init_logits, init_mbatch
  
          # Run gradient accumulation loop.
          loop_init = (
              dropout_rng,
              grad_init,
              init_train_loss,
              metrics_init,
          )
          _, grad_acc, train_loss, metrics_acc = jax.lax.fori_loop(
              1,
              accum_steps,
              per_microbatch_compute_gradient_fn,
              loop_init,
          )
          grad_acc = jax.tree_util.tree_map(
              lambda x: x / accum_steps, grad_acc
          )
          train_loss = jax.tree_util.tree_map(
              lambda x: x / accum_steps, train_loss
          )
          return model_state, grad_acc, train_loss, metrics_acc
      else:
          (
              train_loss,
              (model_state, logits),
          ), grad = compute_gradient_fn(params, batch, dropout_rng)
          metrics = metrics_fn(logits, batch)
          return model_state, grad, train_loss, metrics
  
imports: import collections.abc
  import copy
  import functools
  import os
  import re
  import time
  from typing import Any, Callable, Dict, Mapping, Optional, Sequence, Tuple, Union
  from absl import logging
  from clu import metric_writers
  import flax
  from flax import jax_utils
  from flax import struct
  import flax.linen
  from flax.training import checkpoints
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  import optax
  from scenic.common_lib import debug_utils
  from scenic.dataset_lib import dataset_utils
  from scenic.dataset_lib import datasets
  from scenic.train_lib import optimizers
  from tensorflow.io import gfile
label_desc: Use image node_id 20 for calling a global function with example usage: accumulate_gradients(compute_gradient_fn, metrics_fn, train_state, batch, dropout_rng, accum_steps) and returns: model_state,  grad_acc,  train_loss,  metrics_acc, model_state,  grad,  train_loss,  metrics, jax, dropout_rng,  grad,  train_loss,  metrics