#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

// Abstract interface to a TensorHandle.
//
// A TensorHandle is management class around a Tensor which may track additional
// metadata and synchronization.
//
// This allows us to hide concrete implementations of TensorHandle from header

computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks....

      other entities that control, are controlled by, or are under common
      control with that entity. For the purposes of this definition,
      "control" means (i) the power, direct or indirect, to cause the
      direction or management of such entity, whether by contract or
      otherwise, or (ii) ownership of fifty percent (50%) or more of the
      outstanding shares, or (iii) beneficial ownership of such entity.

  TF_RETURN_IF_ERROR(ValidateOperations(&info->ops[index]));
  TF_RETURN_IF_ERROR(RegisterFileSystem(info, index));
  return OkStatus();
}

// Ensures that the plugin provides the required memory management operations.
static Status ValidatePluginMemoryRoutines(
    const TF_FilesystemPluginInfo* info) {
  if (info->plugin_memory_allocate == nullptr)
    return errors::FailedPrecondition(

// Get an attribute type given an op name; a fusion of TFE_NewOp and
// TFE_OpGetAttrType for use from Python without the overhead of the individual
// calls and memory management of TFE_Op.
TF_CAPI_EXPORT extern TF_AttrType TFE_OpNameGetAttrType(
    TFE_Context* ctx, const char* op_or_function_name, const char* attr_name,
    unsigned char* is_list, TF_Status* status);

        internal to AutoGraph.
    *   Improve shape inference for `tf.function` input arguments to unlock more
        Grappler optimizations in TensorFlow 2.x.
    *   Improve automatic control dependency management of resources by allowing
        resource reads to occur in parallel and synchronizing only on writes.
    *   Fix execution order of multiple stateful calls to `experimental_run_v2`
        in `tf.function`.