code: def create_optimizer(
      name="adam",
      learning_rate=6.25e-5,
      beta1=0.9,
      beta2=0.999,
      eps=1.5e-4,
      centered=False,
  ):
      """Create an optimizer for training.
  
      Currently, only the Adam and RMSProp optimizers are supported.
  
      Args:
        name: str, name of the optimizer to create.
        learning_rate: float, learning rate to use in the optimizer.
        beta1: float, beta1 parameter for the optimizer.
        beta2: float, beta2 parameter for the optimizer.
        eps: float, epsilon parameter for the optimizer.
        centered: bool, centered parameter for RMSProp.
  
      Returns:
        An optax optimizer.
      """
      if name == "adam":
          logging.info(
              "Creating Adam optimizer with settings lr=%f, beta1=%f, "
              "beta2=%f, eps=%f",
              learning_rate,
              beta1,
              beta2,
              eps,
          )
          return optax.adam(learning_rate, b1=beta1, b2=beta2, eps=eps)
      elif name == "rmsprop":
          logging.info(
              "Creating RMSProp optimizer with settings lr=%f, beta2=%f, "
              "eps=%f",
              learning_rate,
              beta2,
              eps,
          )
          return optax.rmsprop(
              learning_rate, decay=beta2, eps=eps, centered=centered
          )
      elif name == "sgd":
          logging.info(
              "Creating SGD optimizer with settings " "lr=%f",
              learning_rate,
          )
          return optax.sgd(learning_rate)
      else:
          raise ValueError("Unsupported optimizer {}".format(name))
  
imports: import collections
  import functools
  import inspect
  import math
  import time
  from absl import logging
  from dopamine.agents.dqn import dqn_agent
  from dopamine.jax import losses
  from dopamine.jax import networks
  from dopamine.metrics import statistics_instance
  from dopamine.replay_memory import circular_replay_buffer
  from dopamine.replay_memory import prioritized_replay_buffer
  import gin
  import jax
  import jax.numpy
  import numpy
  import optax
  import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: create_optimizer(name, learning_rate, beta1, beta2, eps, centered) and returns: optax, optax, optax