*   `tf.nn.embedding_lookup_sparse`

    *   Optimized this function for some cases by fusing internal operations.

*   `tf.saved_model.SaveOptions`

$$v_t := \beta_2 \cdot v_{t-1} + (1 - \beta_2) \cdot g^2$$
$$\text{var} := \begin{cases} \text{var} - (m_t \beta_1 + g \cdot (1 - \beta_1))\cdot\text{lr}_t/(\sqrt{v_t} + \epsilon), &\text{if use_nesterov}\\\\  \text{var} - m_t \cdot \text{lr}_t /(\sqrt{v_t} + \epsilon), &\text{otherwise} \end{cases}$$
  }];

  let arguments = (ins

  - list
  - update
  - delete
- apiGroups:
  - ""
  resources:
  - configmaps
  verbs:
  - delete
- apiGroups:
  - coordination.k8s.io
  resources:
  - leases
  verbs:
  - get
  - update
  - patch
  - create

---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  labels:

          }
        ]
      },
      "io.k8s.apimachinery.pkg.apis.meta.v1.StatusCause": {
        "description": "StatusCause provides more information about an api.Status failure, including cases when multiple errors are encountered.",
        "properties": {
          "field": {

          }
        ]
      },
      "io.k8s.apimachinery.pkg.apis.meta.v1.StatusCause": {
        "description": "StatusCause provides more information about an api.Status failure, including cases when multiple errors are encountered.",
        "properties": {
          "field": {