class_docstr: Gradient descent (with momentum) optimizer.
  
  Update rule for parameter `w` with gradient `g` when `momentum` is 0:
  
  ```python
  w = w - learning_rate * g
  ```
  
  Update rule when `momentum` is larger than 0:
  
  ```python
  velocity = momentum * velocity - learning_rate * g
  w = w + velocity
  ```
  
  When `nesterov=True`, this rule becomes:
  
  ```python
  velocity = momentum * velocity - learning_rate * g
  w = w + momentum * velocity - learning_rate * g
  ```
  
  Args:
      learning_rate: A float, a
          `keras.optimizers.schedules.LearningRateSchedule` instance, or
          a callable that takes no arguments and returns the actual value to
          use. The learning rate. Defaults to `0.01`.
      momentum: float hyperparameter >= 0 that accelerates gradient descent in
          the relevant direction and dampens oscillations. 0 is vanilla
          gradient descent. Defaults to `0.0`.
      nesterov: boolean. Whether to apply Nesterov momentum.
          Defaults to `False`.
      {{base_optimizer_keyword_args}}
code: def get_config(self):
      config = super().get_config()
      config.update(
          {
              "momentum": self.momentum,
              "nesterov": self.nesterov,
          }
      )
      return config
  
file_path: keras/keras/optimizers/sgd.py
imports: from keras import ops
  from keras.api_export import keras_export
  from keras.optimizers import optimizer
label_desc: Use image node_id 4 for calling the SGD obj's underlying member method code with example usage: obj.get_config() and returns: config