code: def compute_metrics(*, labels: Array, logits: Array) -> Metrics:
      """Computes the metrics, summed across the batch if a batch is provided."""
      if (
          labels.ndim == 1
      ):  # Prevent the labels from broadcasting over the logits.
          labels = jnp.expand_dims(labels, axis=1)
      loss = sigmoid_cross_entropy_with_logits(
          labels=labels, logits=logits
      )
      binary_predictions = logits >= 0.0
      binary_accuracy = jnp.equal(binary_predictions, labels)
      return Metrics(
          loss=jnp.sum(loss),
          accuracy=jnp.sum(binary_accuracy),
          count=logits.shape[0],
      )
  
imports: from typing import Any, Callable, Dict, Iterable, Optional, Sequence, Tuple, Union
  from absl import logging
  from flax import struct
  from flax.metrics import tensorboard
  from flax.training import train_state
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  import optax
  import tensorflow
  import input_pipeline
  import models
label_desc: Use image node_id 4 for calling a global function with example usage: compute_metrics() and returns: Metrics