code: def __init__(
      self,
      given_optimizer,
      total_classes,
      embed_dimension,
      model,
      variable_name,
      dtype=torch.float,
  ):
      self.given_optimizer = given_optimizer
      self.total_classes = total_classes
      self.embed_dimension = embed_dimension
      self.model = model
      self.variable_name = variable_name
      self.dtype = dtype
      optimizer_index = None
  
      # Some optiizers do not initialize its state until after first step
      # So they need to initialized here
      for group in given_optimizer.param_groups:
          for p in group["params"]:
              state = given_optimizer.state[p]
              # State initialization
  
              if (
                  given_optimizer.__str__().split(" ", 1)[0]
                  == "SparseAdam"
              ):
                  # State initialization
                  if len(state) == 0:
                      state["step"] = 0
                      state["exp_avg"] = torch.zeros_like(p.data)
                      state["exp_avg_sq"] = torch.zeros_like(p.data)
                  self.optVarList = ["exp_avg", "exp_avg_sq"]
              elif (
                  given_optimizer.__str__().split(" ", 1)[0]
                  == "Adagrad"
              ):
                  self.optVarList = ["sum"]
              elif (
                  given_optimizer.__str__().split(" ", 1)[0]
                  == "Adadelta"
              ):
                  if len(state) == 0:
                      state["step"] = 0
                      state["square_avg"] = torch.zeros_like(p.data)
                      state["acc_delta"] = torch.zeros_like(p.data)
                  self.optVarList = ["square_avg", "acc_delta"]
              elif (
                  given_optimizer.__str__().split(" ", 1)[0] == "Adamax"
              ):
                  if len(state) == 0:
                      state["step"] = 0
                      state["exp_avg"] = torch.zeros_like(p.data)
                      state["exp_inf"] = torch.zeros_like(p.data)
                  self.optVarList = ["exp_avg", "exp_inf"]
              elif (
                  given_optimizer.__str__().split(" ", 1)[0]
                  == "RMSprop"
              ):
                  if len(state) == 0:
                      state["step"] = 0
                      state["square_avg"] = torch.zeros_like(p.data)
                      if group["momentum"] > 0:
                          state["momentum_buffer"] = torch.zeros_like(
                              p.data
                          )
                      if group["centered"]:
                          state["grad_avg"] = torch.zeros_like(p.data)
                  self.optVarList = ["square_avg"]
                  if group["momentum"] > 0:
                      self.optVarList.append("momentum_buffer")
                  if group["centered"]:
                      self.optVarList.append("grad_avg")
              elif (
                  given_optimizer.__str__().split(" ", 1)[0] == "Rprop"
              ):
                  if p.grad is None:
                      print("Error, gradients are empty")
                      print(
                          "For Rprop, need to first run at least 1 training step that has gradients"
                      )
                      return
                  if len(state) == 0:
                      state["step"] = 0
                      state["prev"] = torch.zeros_like(p.data)
                      # For now, do now know how to Not initialize this due to len(state)==0 in optimizer
                      state["step_size"] = (
                          grad.new().resize_as_(grad).fill_(group["lr"])
                      )
                  self.optVarList = ["prev"]
              elif given_optimizer.__str__().split(" ", 1)[0] == "ASGD":
                  if len(state) == 0:
                      state["step"] = 0
                      state["eta"] = group["lr"]
                      state["mu"] = 1
                      state["ax"] = torch.zeros_like(p.data)
                      self.optVarList = ["ax"]
              elif (
                  given_optimizer.__str__().split(" ", 1)[0] == "AdamW"
              ):
                  amsgrad = group["amsgrad"]
                  if len(state) == 0:
                      state["step"] = 0
                      state["exp_avg"] = torch.zeros_like(p.data)
                      state["exp_avg_sq"] = torch.zeros_like(p.data)
                      if amsgrad:
                          state["max_exp_avg_sq"] = torch.zeros_like(
                              p.data
                          )
                  self.optVarList = ["exp_avg", "exp_avg_sq"]
                  if amsgrad:
                      self.optVarList.append("max_exp_avg_sq")
              elif given_optimizer.__str__().split(" ", 1)[0] == "Adam":
                  amsgrad = group["amsgrad"]
                  if len(state) == 0:
                      state["step"] = 0
                      state["exp_avg"] = torch.zeros_like(p.data)
                      state["exp_avg_sq"] = torch.zeros_like(p.data)
                      if amsgrad:
                          state["max_exp_avg_sq"] = torch.zeros_like(
                              p.data
                          )
                  self.optVarList = ["exp_avg", "exp_avg_sq"]
                  if amsgrad:
                      self.optVarList.append("max_exp_avg_sq")
              else:
                  print(
                      "This optimizer is not currently supported. Please choose a different optimizer"
                  )
                  return
  
      # Figure out which index for given variable
      for i, item in enumerate(self.model.named_parameters()):
          if item[0][:-7] == self.variable_name:
              optimizer_index = i
      if optimizer_index == None:
          print(
              "Error: No variable with that name is in Model. Please initialize again with correct name"
          )
          return
  
      optimizerKeyList = list(
          self.given_optimizer.state_dict()["state"].keys()
      )
      self.optimizerKey = optimizerKeyList[optimizer_index]
  
imports: import torch
  import os
  import numpy
label_desc: Use image node_id 1 to create a new CPUPytorchOptimizerFactory object from inherited base classes: _CPUPytorchCommon with example: obj = CPUPytorchOptimizerFactory(given_optimizer, total_classes, embed_dimension, model, variable_name, dtype)