code: def to_bigdl_optim_method(koptim_method):
      # koptim_method is always an object
      lr = float(K.eval(koptim_method.lr))
      decay = float(K.eval(koptim_method.decay))
      if isinstance(koptim_method, koptimizers.Adagrad):
          warnings.warn("For Adagrad, we don't support epsilon for now")
          return boptimizer.Adagrad(
              learningrate=lr, learningrate_decay=decay
          )
      elif isinstance(koptim_method, koptimizers.SGD):
          momentum = float(K.eval(koptim_method.momentum))
          return boptimizer.SGD(
              learningrate=lr,
              learningrate_decay=decay,
              momentum=momentum,
              nesterov=koptim_method.nesterov,
          )
      elif isinstance(koptim_method, koptimizers.Adam):
          beta1 = float(K.eval(koptim_method.beta_1))
          beta2 = float(K.eval(koptim_method.beta_2))
          return boptimizer.Adam(
              learningrate=lr,
              learningrate_decay=decay,
              beta1=beta1,
              beta2=beta2,
              epsilon=koptim_method.epsilon,
          )
      elif isinstance(koptim_method, koptimizers.RMSprop):
          rho = float(K.eval(koptim_method.rho))
          return boptimizer.RMSprop(
              learningrate=lr,
              learningrate_decay=decay,
              decayrate=rho,
              epsilon=koptim_method.epsilon,
          )
      elif isinstance(koptim_method, koptimizers.Adadelta):
          warnings.warn(
              "For Adadelta, we don't support learning rate and learning rate decay for"
              " now"
          )
          return boptimizer.Adadelta(
              decayrate=koptim_method.rho, epsilon=koptim_method.epsilon
          )
      elif isinstance(koptim_method, koptimizers.Adamax):
          beta1 = float(K.eval(koptim_method.beta_1))
          beta2 = float(K.eval(koptim_method.beta_2))
          warnings.warn(
              "For Adamax, we don't support learning rate decay for now"
          )
          return boptimizer.Adamax(
              learningrate=lr,
              beta1=beta1,
              beta2=beta2,
              epsilon=koptim_method.epsilon,
          )
      else:
          unsupport_exp(koptim_method)
  
file_path: BigDL/python/dllib/src/bigdl/dllib/nn/keras/optimization.py
imports: import keras.optimizers
  import bigdl.dllib.nn.criterion
  import bigdl.dllib.optim.optimizer
  from bigdl.dllib.utils.common import to_list
  from bigdl.dllib.keras.converter import *
  from keras.objectives import *
  import six
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 3 for calling the OptimConverter obj's underlying member method code with example usage: obj.to_bigdl_optim_method(koptim_method) and returns: boptimizer, boptimizer, boptimizer, boptimizer, boptimizer, boptimizer