code: def prepare_input(
      inputs: Dict[str, jnp.ndarray], config: ml_collections.ConfigDict
  ) -> Dict[str, jnp.ndarray]:
      """Prepare the different views for LOCA training."""
      # Reference view.
      batch = dict(reference=inputs["reference"])
  
      # A bunch of queries.
      n_focal_queries = config.dataset_configs.number_of_focal_queries
      # This one will have "random" dropping.
      batch["query0"] = inputs["query0"]
      batch["query0_target_position"] = inputs["query0_mask"]
      # Those ones have had "focal" dropping during data processing (i.e. cropping).
      batch["queries"] = jnp.concatenate(
          [
              inputs["query" + str(i)]
              for i in range(1, 1 + n_focal_queries)
          ]
      )
      target_pos = jnp.concatenate(
          [
              inputs["query" + str(i) + "_mask"]
              for i in range(1, 1 + n_focal_queries)
          ]
      )
      batch["target_positions"] = target_pos.reshape(
          target_pos.shape[0], -1
      )
      return batch
  
imports: import os
  from typing import Any, Dict, Tuple, Optional
  import flax
  from flax import jax_utils
  from flax import struct
  from flax.training import checkpoints
  import jax
  import jax.numpy
  import jax.profiler
  import ml_collections
  import optax
  from scenic.dataset_lib import dataset_utils
  from tensorflow.io import gfile
label_desc: Use image node_id 4 for calling a global function with example usage: prepare_input(inputs, config) and returns: batch