// Eager Executor APIs.
typedef struct TFE_Executor TFE_Executor;

// Creates a new eager Executor. Nodes in one executor are guaranteed to be
// executed in sequence. Assigning nodes to different executors allows executing
// nodes in parallel.
// in_flight_nodes_limit: when is_async is true, this value controls the
// maximum number of in flight async nodes. Enqueuing of additional async ops

// TF_SessionRun will no longer extend the session on every call.
//
// We expose this here to allow fine-grained synchronization in multi-threaded
// workloads, which is required since the Python implementation depends on the
// above mutation methods. This allows us to prevent modifications to nodes in
// the graph after the session has been made aware of them.

  bool ShouldRecord(absl::Span<const int64_t> tensor_ids,
                    absl::Span<const tensorflow::DataType> dtypes);

  // Temporarily push or pop transient state for this accumulator.
  //
  // Allows an accumulator which is currently processing an operation to
  // temporarily reset its state. Without pushing and popping, accumulators
  // ignore operations executed as a direct result of their own jvp
  // computations.

#include "tensorflow/c/tf_status.h"
#include "xla/pjrt/c/pjrt_c_api.h"

// --------------------------------------------------------------------------
// C API for device. The API is under active development and eventually
// should allow registering a plugin device with TensorFlow.

#ifdef __cplusplus
extern "C" {
#endif

// TF_Device is a C wrapper to the C++ TF Device class. This is to be passed
// through TF_OpKernelContext, and is opaque to plugin.