code: def sgd(step_size):
      """Construct optimizer triple for stochastic gradient descent.
  
      Args:
        step_size: positive scalar, or a callable representing a step size schedule
          that maps the iteration index to a positive scalar.
  
      Returns:
        An (init_fun, update_fun, get_params) triple.
      """
      step_size = make_schedule(step_size)
  
      def init(x0):
          return x0
  
      def update(i, g, x):
          return x - step_size(i) * g
  
      def get_params(x):
          return x
  
      return Optimizer(init, update, get_params)
  
imports: from __future__ import annotations
  from typing import Any, Callable, NamedTuple
  from collections import namedtuple
  import functools
  from functools import partial
  import jax.numpy
  from jax._src.util import safe_zip, safe_map, unzip2
  from jax import tree_util
  from jax.tree_util import tree_map, tree_flatten, tree_unflatten, register_pytree_node
label_desc: Use image node_id 2 for calling a global function with example usage: sgd(step_size) and returns: Optimizer, x0, unknown, x