code: def maml_loss(x1, y1, x2, y2, alpha=0.1):
      net_vars = net.vars()
      original_weights = net_vars.tensors()  # Save original weights
      g_x1y1 = gv(x1, y1)[0]  # Compute gradient at (x1, y1)
      # Apply gradient update using SGD
      net_vars.assign(
          [v - alpha * g for v, g in zip(original_weights, g_x1y1)]
      )
      loss_x2y2 = loss(x2, y2)
      net_vars.assign(original_weights)  # Restore original weights
      return loss_x2y2
  
file_path: objax/examples/maml/maml.py
imports: import jax.numpy
  import matplotlib.pyplot
  import numpy
  from tqdm import trange
  import objax
label_desc: Use image node_id 6 for calling a global function with example usage: maml_loss(x1, y1, x2, y2, alpha) and returns: loss_x2y2