code: def init_optimizer(
      self,
      model_parameter_length=None,
      model_parameter=None,
      dtype=torch.float32,
  ):
      # allow group of parameter in future
      if model_parameter_length is not None:
          model_parameter = torch.nn.parameter.Parameter(
              torch.zeros(
                  (model_parameter_length, 1),
                  requires_grad=True,
                  dtype=dtype,
              )
          )
      self.model_parameter = model_parameter
      self.optimizer = optimizer_factory(
          [model_parameter], self.method, self.optim_param
      )
  
imports: import logging
  import numpy
  import torch
label_desc: Use image node_id 2 for calling the Optimizer obj's underlying member method code with example usage: obj.init_optimizer(model_parameter_length, model_parameter, dtype) without return types