code: def jax_classifier():
      # Define model sizes
      layer_sizes = [784, 256, 128, 10]
  
      # Initialize random weights
      def random_init(m, n, k):
          w, b = random.split(k)
          rw, rb = (
              random.normal(w, (n, m)) * 0.01,
              random.normal(b, (n,)) * 0.01,
          )
          return rw, rb
  
      model = [
          random_init(m, n, k)
          for m, n, k in zip(
              layer_sizes[:-1],
              layer_sizes[1:],
              random.split(random.PRNGKey(0), 4),
          )
      ]
  
      # Forward function
      def forward(model, x):
          activation = jnp.reshape(x, 784)
          for w, b in model[:-1]:
              output = jnp.dot(w, activation) + b
              activation = jnp.maximum(0, output)
  
          f_w, f_b = model[-1]
          logit = jnp.dot(f_w, activation) + f_b
          return logit - logsumexp(logit)
  
      # Prediction function
      predict_func = vmap(forward, in_axes=(None, 0))
  
      # Loss function
      def loss_func(model, x, y):
          preds = predict_func(model, x)
          return -jnp.mean(preds * y)
  
      # Update function
      @jit
      def update_func(model, x, y):
          grads = grad(loss_func)(model, x, y)
          return [
              (w - 0.01 * dw, b - 0.01 * db)
              for (w, b), (dw, db) in zip(model, grads)
          ]
  
      classifier = JaxClassifier(
          model=model,
          predict_func=predict_func,
          loss_func=loss_func,
          update_func=update_func,
          input_shape=(28, 28, 1),
          nb_classes=10,
      )
  
      return classifier
  
file_path: adversarial-robustness-toolbox/tests/estimators/classification/test_jax.py
imports: import logging
  import numpy
  import pytest
  from jax import random
  from jax import grad, jit, vmap
  import jax.numpy
  from jax.scipy.special import logsumexp
  from art.experimental.estimators.classification.jax import JaxClassifier
  from tests.utils import ARTTestException
label_desc: Use image node_id 1 for calling a global function with example usage: jax_classifier() and returns: classifier, rw,  rb, unknown, unknown, unknown