*   New endpoints in `tf.image` namespace: `tf.image.extract_image_patches`
    *   New endpoints in `tf.debugging` namespace:
        `tf.debugging.check_numerics`, `tf.debugging.is_finite`,
        `tf.debugging.is_inf`, `tf.debugging.is_nan`.
    *   New endpoints in `tf.dtypes` namespace: `tf.dtypes.as_string`.
    *   New endpoints in `tf.io` namespace: `tf.io.decode_base64`,

### Expanded CLI support for debugging workloads and nodes

SIG CLI expanded on debugging with `kubectl` to support two new debugging workflows: debugging workloads by creating a copy, and debugging nodes by creating a container in host namespaces. These can be convenient to:

                        - destination
                        type: object
                      type: array
                    name:
                      description: The name assigned to the route for debugging purposes.
                      type: string
                    redirect:
                      description: A HTTP rule can either return a direct_response,

                        - destination
                        type: object
                      type: array
                    name:
                      description: The name assigned to the route for debugging purposes.
                      type: string
                    redirect:
                      description: A HTTP rule can either return a direct_response,

                        - destination
                        type: object
                      type: array
                    name:
                      description: The name assigned to the route for debugging purposes.
                      type: string
                    redirect:
                      description: A HTTP rule can either return a direct_response,

tensor.

Example usage:

``` python
a = tf.Variable(1.0)
tf.debugging.check_numerics(a, message='')

b = tf.Variable(np.nan)
try:
  tf.debugging.check_numerics(b, message='Checking b')
except Exception as e:
  assert "Checking b : Tensor had NaN values" in e.message

c = tf.Variable(np.inf)
try:
  tf.debugging.check_numerics(c, message='Checking c')
except Exception as e:

      },
      "io.k8s.api.core.v1.EphemeralContainer": {