# Define an Adam optimizer with learning rate 0.001
optimizer = flax.optim.Adam(learning_rate=0.001)  
# Define the loss function
loss_fn = flax.nn.logits_cross_entropy_loss  
def train_step(optimizer, batch):
  def loss_fn(model):
    logits = model(batch['inputs'])
    # Compute the loss
    loss = loss_fn(labels=batch['labels'], logits=logits)  
    return loss.mean()
  # Compute the gradient function
  grad_fn = jax.grad(loss_fn)  
  # Compute the gradients
  grad = grad_fn(optimizer.target)  
  # Update the model parameters
  optimizer = optimizer.apply_gradient(grad)  
  return optimizer

for batch in dataset:
  # Perform a training step
  optimizer = train_step(optimizer, batch)

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