code: def sinkhorn(x, num_itr=3, distributed=True):
      """Sinkhorn-Knopp algorithm."""
      for _ in range(num_itr):
          # Total weight per prototype per device.
          weight_per_proto = jnp.sum(x, axis=0, keepdims=True)
          if distributed:
              # Globally.
              weight_per_proto = jax.lax.psum(
                  weight_per_proto, axis_name="batch"
              )
          x /= weight_per_proto
  
          # Total weight per sample.
          weight_per_sample = jnp.sum(x, axis=-1, keepdims=True)
          # x sums to 1 for each sample (it is an assignment).
          x /= weight_per_sample
      return x
  
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 5 for calling a global function with example usage: sinkhorn(x, num_itr, distributed) and returns: x