code: def loca_train_step(
      train_state: utils.TrainState,
      batch: Batch,
      *,
      flax_model: nn.Module,
      momentum_parameter_scheduler: Callable[[int], float],
      loss_fn: Any,
      metrics_fn: Any,
      config: ml_collections.ConfigDict,
  ) -> Tuple[utils.TrainState, Dict[str, Tuple[float, int]]]:
      """Runs a single step of training.
  
      Given the state of the training and a batch of data, computes
      the loss and updates the parameters of the model.
  
      Args:
        train_state: The state of training including the current
          global_step, model_state, rng, and optimizer. The buffer of this argument
          can be donated to the computation.
        batch: A single batch of data. The buffer of this argument can be donated to
          the computation.
        flax_model: A Flax model.
        momentum_parameter_scheduler: Momentum parameter scheduler for EMA update.
        loss_fn: The cross-entropy loss function.
        metrics_fn: Reports relative position loss and accuracy.
        config: Configurations of the experiment.
  
      Returns:
        The updated state of training.
      """
      # Some preparations.
      new_rng, dropout_rng, droptok_rng = jax.random.split(
          train_state.rng, num=3
      )
      dropout_rng = train_utils.bind_rng_to_host_device(
          dropout_rng, axis_name="batch", bind_to="device"
      )
      droptok_rng = train_utils.bind_rng_to_host_device(
          droptok_rng, axis_name="batch", bind_to="device"
      )
      step = train_state.global_step
      momentum_parameter = momentum_parameter_scheduler(step)
      n_pos = config.n_ref_positions  # Number of reference positions.
      bs = batch["reference"].shape[0]  # Per-device batch size.
      n_q_foc = config.dataset_configs.number_of_focal_queries
      batch = utils.prepare_input(batch, config)
  
      def training_loss_fn(params):
          # Step 1): Forward pass on the REFERENCE view.
          use_ema = config.apply_cluster_loss
          drop_moment = "late" if config.apply_cluster_loss else "early"
          _, r_feat_targets, r_patch_features, _ = flax_model.apply(
              {"params": train_state.ema_params if use_ema else params},
              batch["reference"],
              seqlen=config.reference_seqlen,
              seqlen_selection=config.reference_seqlen_selection,
              drop_moment=drop_moment,
              train=True,
              rngs={"dropout": dropout_rng, "droptok": droptok_rng},
          )
  
          # Step 2): Forward pass on the QUERY views.
          use_pe = True if config.apply_cluster_loss else False
          #      2) a) Query with `random`-style.
          (
              q_rand_loc_pred,
              q_rand_feat_pred,
              _,
              q_rand_idx_kept,
          ) = flax_model.apply(
              {"params": params},
              batch["query0"],
              inputs_kv=r_patch_features,
              seqlen=config.query_max_seqlen,
              use_pe=use_pe,
              train=True,
              rngs={"dropout": dropout_rng, "droptok": droptok_rng},
          )
          #      2) b) Queries with `focal`-style.
          q_foc_loc_pred, q_foc_feat_pred, _, _ = flax_model.apply(
              {"params": params},
              batch["queries"],
              inputs_kv=jnp.tile(r_patch_features, (n_q_foc, 1, 1)),
              use_pe=use_pe,
              train=True,
              rngs={"dropout": dropout_rng},
          )
          #      2) c) Batch the `random` and `focal` queries together: for both
          #            predictions (`q_loc_pred`) and targets (`q_loc_targets`).
          #
          # q_loc_pred is position logits for all the patches of the queries.
          q_loc_pred = jnp.concatenate(
              [
                  q_rand_loc_pred.reshape(-1, n_pos),
                  q_foc_loc_pred.reshape(-1, n_pos),
              ],
              axis=0,
          )
          q_rand_loc_targets = batch["query0_target_position"].reshape(
              bs, -1
          )
          # If tokens were dropped in the query0 (i.e. `random`-style query):
          if len(q_rand_idx_kept) < q_rand_loc_targets.shape[1]:
              # then drop the corresponding target positions.
              q_rand_loc_targets = jnp.take(
                  q_rand_loc_targets, q_rand_idx_kept, axis=1
              )
          q_foc_loc_targets = batch["target_positions"]
          q_loc_targets = jnp.concatenate(
              [
                  q_rand_loc_targets.reshape(-1),
                  q_foc_loc_targets.reshape(-1),
              ],
              axis=0,
          )
          q_r_intersect = (
              q_loc_targets != -1
          )  # intersection of reference and queries
          # q_loc_targets are the position to predict for all the patches of the
          # queries.
          q_loc_targets = jax.nn.one_hot(q_loc_targets, n_pos)
  
          # Step 3): Position prediction loss.
          localization_loss = loss_fn(
              q_loc_pred, q_loc_targets, q_r_intersect
          )
  
          # Step 4): Patch cluster prediction loss.
          feature_loss = 0
          if config.apply_cluster_loss:
              k = r_feat_targets.shape[
                  -1
              ]  # Output dimension for feature pred loss.
              q_feat_pred = (
                  jnp.concatenate(
                      [q_rand_feat_pred, q_foc_feat_pred], axis=0
                  )
                  / config.model.temperature
              )
              # Feature targets.
              r_feat_targets = nn.softmax(
                  r_feat_targets / config.sharpening, axis=-1
              )
              # We adjust the targets with Optimal Transport to prevent collapse.
              r_feat_targets = utils.sinkhorn(
                  r_feat_targets, distributed=True
              )
              # (bs*N) x k -> bs x N x k
              r_feat_targets = r_feat_targets.reshape(bs, -1, k)
              # Feature targets for the random query.
              q_rand_feat_targets = jnp.take_along_axis(
                  r_feat_targets,
                  jnp.expand_dims(q_rand_loc_targets, axis=-1),
                  axis=1,
              )
              q_rand_feat_targets = q_rand_feat_targets.reshape(-1, k)
              # Feature targets for the focal queries.
              r_feat_targets = jnp.tile(r_feat_targets, (n_q_foc, 1, 1))
              q_foc_feat_targets = jnp.take_along_axis(
                  r_feat_targets,
                  jnp.expand_dims(q_foc_loc_targets, axis=-1),
                  axis=1,
              )
              q_foc_feat_targets = q_foc_feat_targets.reshape(-1, k)
              # Concatenate the targets for the random and focal queries.
              q_feat_targets = jnp.concatenate(
                  [q_rand_feat_targets, q_foc_feat_targets], axis=0
              )
              feature_loss = loss_fn(
                  q_feat_pred, q_feat_targets, q_r_intersect
              )
              # `me-max` regularization.
              avg_prediction = jnp.mean(
                  nn.softmax(q_feat_pred, axis=-1), axis=0
              )
              avg_prediction = jax.lax.pmean(
                  avg_prediction, axis_name="batch"
              )
              feature_loss += jnp.sum(
                  avg_prediction * jnp.log(avg_prediction)
              )
  
          total_loss = localization_loss + feature_loss
          return total_loss, (
              {"label": q_loc_targets, "batch_mask": q_r_intersect},
              q_loc_pred,
              feature_loss,
          )
  
      compute_gradient_fn = jax.value_and_grad(
          training_loss_fn, has_aux=True
      )
      (
          total_loss,
          (batch, logits, feature_loss),
      ), grad = compute_gradient_fn(train_state.params)
      metrics = metrics_fn(logits, batch)
      metrics.update(
          dict(
              total_loss=(total_loss, 1), feature_loss=(feature_loss, 1)
          )
      )
  
      # Update the network parameters.
      grad = jax.lax.pmean(grad, axis_name="batch")
      if config.get("max_grad_norm", None) is not None:
          grad = optimizers.clip_grads(grad, config.max_grad_norm)
      new_train_state = train_state
      if train_state.tx is not None:
          updates, new_opt_state = train_state.tx.update(
              grad, train_state.opt_state, train_state.params
          )
          new_params = optax.apply_updates(train_state.params, updates)
  
          # update the teacher weights
          new_ema_params = jax.tree_map(
              lambda s, t: momentum_parameter * t
              + (1 - momentum_parameter) * s,
              new_params,
              train_state.ema_params,
          )
  
          new_train_state = (
              train_state.replace(  # pytype: disable=attribute-error
                  global_step=step + 1,
                  opt_state=new_opt_state,
                  params=new_params,
                  ema_params=new_ema_params,
                  rng=new_rng,
              )
          )
      return new_train_state, metrics
  
imports: import copy
  import functools
  from typing import Any, Callable, Dict, Tuple
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from clu import platform
  from flax import jax_utils
  import flax.linen
  import jax
  from jax.example_libraries import optimizers
  import jax.numpy
  import jax.profiler
  import ml_collections
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.projects.loca import utils
  from scenic.projects.loca import vit
  from scenic.train_lib import lr_schedules
  from scenic.train_lib import train_utils
label_desc: Use image node_id 1 for calling a global function with example usage: loca_train_step(train_state, batch) and returns: new_train_state,  metrics, total_loss,  dict,  q_loc_pred,  feature_loss