code: def init_optimizer(self, kvstore='local', optimizer='sgd',
                         optimizer_params=(('learning_rate', 0.01),), force_init=False):
          
  
          # Init dict for storing average of full gradients for each device
          self._param_dict = [{key: mx.nd.zeros(shape=value.shape, ctx=self._context[i])
                               for key, value in self.get_params()[0].items()} for i in range(self._ctx_len)]
  
          svrg_optimizer = self._create_optimizer(_SVRGOptimizer.__name__, default_opt=optimizer,
                                                  kvstore=kvstore, optimizer_params=optimizer_params)
  
          super(SVRGModule, self).init_optimizer(kvstore=kvstore, optimizer=svrg_optimizer,
                                                 optimizer_params=optimizer_params, force_init=force_init)
  
          # Init additional keys for accumulating full grads in KVStore
          if self._kvstore:
              for idx, param_on_devs in enumerate(self._exec_group.param_arrays):
                  name = self._exec_group.param_names[idx]
                  self._kvstore.init(name + "_full", mx.nd.zeros(shape=self._arg_params[name].shape))
                  if self._update_on_kvstore:
                      self._kvstore.pull(name + "_full", param_on_devs, priority=-idx)
docs: Installs and initializes SVRGOptimizer. The SVRGOptimizer is a wrapper class for a regular optimizer that is
          passed in and a special AssignmentOptimizer to accumulate the full gradients.  If KVStore is 'local' or None,
          the full gradients will be accumulated locally without pushing to the KVStore. Otherwise, additional keys will
          be pushed to accumulate the full gradients in the KVStore.
  
          Parameters
          ----------
          kvstore : str or KVStore
              Default `'local'`.
          optimizer : str or Optimizer
              Default `'sgd'`
          optimizer_params : dict
              Default `(('learning_rate', 0.01),)`. The default value is not a dictionary,
              just to avoid pylint warning of dangerous default values.
          force_init : bool
              Default ``False``, indicating whether we should force re-initializing the
              optimizer in the case an optimizer is already installed.