code: def update(self, w, grad_wrt_w):
      # If not initialized
      if self.w_updt is None:
          self.w_updt = np.zeros(np.shape(w))
          self.E_w_updt = np.zeros(np.shape(w))
          self.E_grad = np.zeros(np.shape(grad_wrt_w))
  
      # Update average of gradients at w
      self.E_grad = self.rho * self.E_grad + (1 - self.rho) * np.power(
          grad_wrt_w, 2
      )
  
      RMS_delta_w = np.sqrt(self.E_w_updt + self.eps)
      RMS_grad = np.sqrt(self.E_grad + self.eps)
  
      # Adaptive learning rate
      adaptive_lr = RMS_delta_w / RMS_grad
  
      # Calculate the update
      self.w_updt = adaptive_lr * grad_wrt_w
  
      # Update the running average of w updates
      self.E_w_updt = self.rho * self.E_w_updt + (
          1 - self.rho
      ) * np.power(self.w_updt, 2)
  
      return w - self.w_updt
  
file_path: ML-From-Scratch/mlfromscratch/deep_learning/optimizers.py
imports: import numpy
  from mlfromscratch.utils import make_diagonal, normalize
label_desc: Use image node_id 2 for calling the Adadelta obj's underlying member method code with example usage: obj.update(w, grad_wrt_w) and returns: unknown