text: from abc import ABC, abstractmethod
  from copy import deepcopy
  
  import numpy as np
  from numpoy.linalg import norm
  
  
  class OptimizerBase(ABC):
      def __init__(self, lr, scheduler=None):
          from initalizers import SchedulerInitializer
  
          self.cache = {}
          self.cur_step = 0
          self.hyperparameters = {}
          self.lr_scheduler = SchedulerInitializer(Scheduler, lr=lr)()
  
      def __call__(self, param, param_grad, param_name, cur_loss=None):
          return self.update(param, param_grad, param_name, cur_loss)
      
      def step(self):
          """Increment the optimizer step counter by 1"""
          self.cur_step += 1
      
      def reset_step(self):
          """Reset the step counter to zero"""
          self.cur_step = 0
      
      def copy(self):
          """Return a copy of the optimizer object"""
          return deepcopy
      
      def set_param(self, hparam_dict=None, cache_dict=None):
          """Set the parameters of the optimizer object from a dictionary"""
          from initializers import SchedulerInitializer
  
          if hparam_dict is not None:
              for k, v in hparam_dict.items():
                  if k in self.hyperparameters:
                      self.hyperparameters[k] = v
                      if k == "lr_scheduler":
                          self.lr_scheduler = SchedulerInitializer(v, lr=None)()
          
          if cache_dict is not None:
              for k, v in cache_dict.items():
                  if k in self.cache:
                      self.cache[k] = v
          
          @abstractmethod
          def update(self, param, param_grad, param_name, cur_loss=None):
              raise NotImplementedError
  
  
  class SGD(OptimizerBase):
      def __init__(
          self, lr=0.01, momentum=0.0, clip_norm=None, lr_scheduler=None, **kwargs
      ): 
          pass
  
      def __str__(self):
          pass
      
      def update(self, param, param_grad, param_name, cur_loss=None):
          pass
  
  
  class AdaGrad(OptimizerBase):
      def __init__(
          self, lr=0.01, eps=1e-7, clip_norm=None, lr_scheduler=None, **kwargs
      ): 
          pass
  
      def __str__(self):
          pass
      
      def update(self, param, param_grad, param_name, cur_loss=None):
          pass
  
  
  class RMSProp(OptimizerBase):
      def __init__(
          self, lr=0.001, decay=0.9, eps=1e-7, clip_norm=None, lr_scheduler=None, **kwargs
      ): 
          pass
  
      def __str__(self):
          pass
      
      def update(self, param, param_grad, param_name, cur_loss=None):
          pass
  
  
  class Adam(OptimizerBase):
      def __init__(
          self, lr=0.001, decay1=0.9, decay2=0.999, eps=1e-7, clip_norm=None, lr_scheduler=None, **kwargs
      ): 
          pass
      
      def __str__(self):
          pass
      
      def update(self, param, param_grad, param_name, cur_loss=None):
          pass