class_docstr: Stochastic gradient descent optimizer.
  
  V = bV + aG
  W = W - V
  
  Reference: https://paperswithcode.com/method/sgd-with-momentum
code: def compile(self, weights):
      for W in weights:
          self.Vs[id(W)] = [
              tfe.define_private_variable(
                  np.zeros(list(map(int, W[i].shape.as_list()))),
                  apply_scaling=True,
                  share_type=W[i].share_type,
                  factory=W[i].backing_dtype,
              )
              for i in range(len(W))
          ]
  
file_path: tf-encrypted/tf_encrypted/keras/optimizers.py
imports: import numpy
  import tensorflow
  import tf_encrypted
label_desc: Use image node_id 2 for calling the SGD obj's underlying member method code with example usage: obj.compile(weights) without return types