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))
      # Use momentum if set
      self.w_updt = (
          self.momentum * self.w_updt + (1 - self.momentum) * grad_wrt_w
      )
      # Move against the gradient to minimize loss
      return w - self.learning_rate * 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 StochasticGradientDescent obj's underlying member method code with example usage: obj.update(w, grad_wrt_w) and returns: unknown