code: def _create_optimizer(self):
      import types
  
      if isinstance(self.optimizer_creator, types.FunctionType):
          self.optimizer = self.optimizer_creator(
              self.model, self.config
          )
      else:
          # use torch default parameter values if user pass optimizer name or optimizer class.
          try:
              self.optimizer = self.optimizer_creator(
                  self.model.parameters(),
                  lr=self.config.get(LR_NAME, DEFAULT_LR),
              )
          except:
              raise ValueError(
                  "We failed to generate an optimizer with specified optim "
                  "class/name. You need to pass an optimizer creator function."
              )
  
imports: import torch
  from torch.utils.data import TensorDataset, DataLoader
  import types
  import numpy
  import math
  import pandas
  import tempfile
  import os
  from zoo.orca.automl.model.abstract import BaseModel, ModelBuilder
  from zoo.orca.automl.metrics import Evaluator
  from zoo.orca.automl.pytorch_utils import LR_NAME, DEFAULT_LR
label_desc: Use image node_id 3 for calling the PytorchBaseModel obj's underlying member method code with example usage: obj._create_optimizer() without return types