TF_CAPI_EXPORT extern void TF_DeleteFunction(TF_Function* func);

// Attempts to evaluate `output`. This will only be possible if `output` doesn't
// depend on any graph inputs (this function is safe to call if this isn't the
// case though).
//
// If the evaluation is successful, this function returns true and `output`s
// value is returned in `result`. Otherwise returns false. An error status is

TF_CAPI_EXPORT extern TF_Tensor* TF_CheckpointReaderGetTensor(
    TF_CheckpointReader* reader, const char* name, TF_Status* status);

// TF_NewAttrBuilder() returns an object that you can set attributes on as
// though it were an op. This allows querying properties of that op for
// type-checking purposes like if the op will run on a particular device type.
typedef struct TF_AttrBuilder TF_AttrBuilder;

# release while SASS is only forward compatible inside the current
# major release. Example: sm_80 kernels can run on sm_89 GPUs but
# not on sm_90 GPUs. compute_80 kernels though can also run on sm_90 GPUs.
build:cuda_clang --repo_env=TF_CUDA_COMPUTE_CAPABILITIES="sm_60,sm_70,sm_80,sm_89,compute_90"

# Set up compilation CUDA version and paths and use the CUDA Clang toolchain.

//   * allows having newer plugins than the current core TensorFlow: the
//     additional entries in the plugin's table are just discarded;
//   * allows having older plugins than the current core TensorFlow (though
//     we are still warning users): the entries that core TensorFlow expects
//     but plugins didn't provide will be set to `nullptr` values and core
//     TensorFlow will know to not call these on behalf of users;

        TensorFlow.

*   `tf.experimental.dtensor`: Added DTensor, an extension to TensorFlow for
    large-scale modeling with minimal changes to user code. You are welcome to
    try it out, though be aware that the DTensor API is experimental and up-to
    backward-incompatible changes. DTensor and Keras integration is published
    under `tf.keras.dtensor` in this release (refer to the `tf.keras` entry).