code: def compute_loss(labels, logits):
      """Compute loss for a single batch of data, given the precomputed logits and
      expected labels. The returned loss is normalized by the batch size.
  
      """
      cur_batch_size = tf.cast(labels.shape[0], tf.float32)
      cce = tf.losses.SparseCategoricalCrossentropy(
          from_logits=True, reduction=tf.losses.Reduction.SUM
      )
  
      # loss here is the cce, normalized by the batch size.
      return cce(labels, logits) / cur_batch_size
  
imports: from __future__ import division, print_function
  import getpass
  from typing import Dict, Optional
  import fs
  import tensorflow
  import tensorflow_datasets
  import tqdm
  import uv
  import uv.tensorflow.reporter
  import uv.util
  from absl import app
  from uv.fs.reporter import FSReporter
  from uv.mlflow.reporter import MLFlowReporter
  from trainer import cli
label_desc: Use image node_id 4 for calling a global function with example usage: compute_loss(labels, logits) and returns: unknown