code: def update_parallel(model, batch, learning_rate=0.001):
      grads = grad(loss)(model, batch)
  
      grads = lax.pmean(grads, axis_name="data")
  
      updated_model = []
  
      for model, updates in zip(model, grads):
          w, b = model
          dw, db = updates
  
          new_w = w - learning_rate * dw
          new_b = b - learning_rate * db
  
          updated_model.append((new_w, new_b))
  
      return updated_model
  
file_path: DALI/docs/examples/frameworks/jax/model.py
imports: from jax import jit, grad, lax, pmap
  from functools import partial
  import jax.numpy
  from jax.scipy.special import logsumexp
  import numpy.random
label_desc: Use image node_id 6 for calling a global function with example usage: update_parallel(model, batch, learning_rate) and returns: updated_model