`tensorflow` pip package has a new, optional installation method for Linux that installs necessary Nvidia CUDA libraries through pip. As long as the Nvidia driver is already installed on the system, you may now run `pip install tensorflow[and-cuda]` to install TensorFlow's Nvidia CUDA library dependencies in the Python environment. Aside from the Nvidia driver, no other pre-existing Nvidia CUDA packages are necessary.

*   Enable JIT-compiled i64-indexed kernels on GPU for large tensors with...

                        entries to status populated with their ControllerName are cleaned up when they are no
                        longer necessary.
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      "io.k8s.api.apps.v1.StatefulSetUpdateStrategy": {
        "description": "StatefulSetUpdateStrategy indicates the strategy that the StatefulSet controller will use to perform updates. It includes any additional parameters necessary to perform the update for the indicated strategy.",
        "properties": {
          "rollingUpdate": {
            "allOf": [
              {

        "properties": {
          "nodeName": {
            "description": "NodeName is the name of the node providing the necessary resources if the resources are local to a node.",
            "type": "string"
          },
          "results": {
            "description": "Results lists all allocated driver resources.",
            "items": {

In the above example, the input Tensor with the shape of `[1, 3]`
is broadcasted to output Tensor with shape of `[2, 3]`.

When broadcasting, if a tensor has fewer axes than necessary its shape is
padded on the left with ones. So this gives the same result as the previous
example:

>>> x = tf.constant([1, 2, 3])   # Shape (3,)
>>> y = tf.broadcast_to(x, [2, 3])