code: def __call__(self, x: jnp.ndarray, training: bool) -> jnp.ndarray:
      # Normalize the input
      x = x.astype(jnp.float32) / 255.0
  
      # Block 1
      x = linen.Conv(32, [3, 3], strides=[2, 2])(x)
      x = linen.Dropout(0.05, deterministic=not training)(x)
      x = jax.nn.relu(x)
  
      # Block 2
      x = linen.Conv(64, [3, 3], strides=[2, 2])(x)
      x = linen.BatchNorm(use_running_average=not training)(x)
      x = linen.Dropout(0.1, deterministic=not training)(x)
      x = jax.nn.relu(x)
  
      # Block 3
      x = linen.Conv(128, [3, 3], strides=[2, 2])(x)
  
      # Global average pooling
      x = x.mean(axis=(1, 2))
  
      # Classification layer
      x = linen.Dense(10)(x)
  
      return x
  
file_path: elegy/examples/flax/mnist_conv.py
imports: import os
  import typing
  from datetime import datetime
  import datasets
  import jax
  import jax.numpy
  import matplotlib.pyplot
  import numpy
  import optax
  import typer
  from datasets import load_dataset
  from flax import linen
  import elegy
label_desc: Use image node_id 1 for calling the CNN obj's underlying member method code with example usage: obj.__call__(x, training) and returns: x