class_docstr: Optimizer that implements the Momentum algorithm.
  
  Computes (if `use_nesterov = False`):
  
  ```
  accumulation = momentum * accumulation + gradient
  variable -= learning_rate * accumulation
  ```
  
  Note that in the dense version of this algorithm, `accumulation` is updated
  and applied regardless of a gradient's value, whereas the sparse version (when
  the gradient is an `IndexedSlices`, typically because of `tf.gather` or an
  embedding) only updates variable slices and corresponding `accumulation` terms
  when that part of the variable was used in the forward pass.
  
  @compatibility(TF2)
  tf.compat.v1.train.MomentumOptimizer is compatible with eager mode and
  `tf.function`.
  When eager execution is enabled, `learning_rate`,`momentum`, can each be a
  callable that takes no arguments and returns the actual value to use. This
  can be useful for changing these values across different invocations of
  optimizer functions.
  
  To switch to native TF2 style, please directly use
  [`tf.keras.optimizers.SGD`]
  (https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/SGD)
  with the `momentum` argument.
  
  #### Structural mapping to native TF2
  
  Before:
  
  ```python
  optimizer = tf.compat.v1.train.MomentumOptimizer(
    learning_rate=learning_rate,
    momentum=momentum,
    use_nesterov=use_nesterov)
  ```
  
  After:
  
  ```python
  optimizer = tf.keras.optimizers.SGD(
    learning_rate=learning_rate,
    momentum=momentum,
    nesterov=use_nesterov)
  ```
  
  #### How to map arguments
  | TF1 Arg Name       | TF2 Arg Name   | Note                             |
  | ------------------ | -------------  | -------------------------------  |
  | `learning_rate`    | `learning_rate`| Be careful of setting           |
  : : : learning_rate tensor value computed from the global step.          :
  : : : In TF1 this was usually meant to imply a dynamic learning rate and :
  : : : would recompute in each step. In TF2 (eager + function) it will    :
  : : : treat it as a scalar value that only gets computed once instead of :
  : : : a symbolic placeholder to be computed each time.                   :
  | `momentum`         | `momentum`     | -                                |
  | `use_locking`      | -              | Not applicable in TF2.           |
  | `use_nesterov`     | `nesterov`     | -                                |
  
  #### Before & after usage example
  Before:
  
  ```python
  x = tf.Variable([1,2,3], dtype=tf.float32)
  grad = tf.constant([0.1, 0.2, 0.3])
  optimizer = tf.compat.v1.train.MomentumOptimizer(
    learning_rate=0.001,
    momentum=0.9,
    use_nesterov=False)
  optimizer.apply_gradients(zip([grad], [x]))
  ```
  
  After:
  
  ```python
  x = tf.Variable([1,2,3], dtype=tf.float32)
  grad = tf.constant([0.1, 0.2, 0.3])
  optimizer = tf.keras.optimizers.SGD(
    learning_rate=0.001,
    momentum=0.9,
    nesterov=False)
  optimizer.apply_gradients(zip([grad], [x]))
  ```
  
  @end_compatibility
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code: def _apply_sparse(self, grad, var):
      mom = self.get_slot(var, "momentum")
      return gen_training_ops.sparse_apply_momentum(
          var,
          mom,
          math_ops.cast(
              self._learning_rate_tensor, var.dtype.base_dtype
          ),
          grad.values,
          grad.indices,
          math_ops.cast(self._momentum_tensor, var.dtype.base_dtype),
          use_locking=self._use_locking,
          use_nesterov=self._use_nesterov,
      ).op
  
file_path: tensorflow-upstream/tensorflow/python/training/momentum.py
imports: from tensorflow.python.framework import ops
  from tensorflow.python.ops import gen_training_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.training import optimizer
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 6 for calling the MomentumOptimizer obj's underlying member method code with example usage: obj._apply_sparse(grad, var) and returns: gen_training_ops