void* c_optimizer_;
};

// Registration function to register a CGraphOptimizer along with plugin configs
// and device type.
void CGraphOptimizerRegister(
    const PluginGraphOptimizerRegistry::Creator& creator,
    const TP_OptimizerConfigs tp_configs, const char* device_type);

}  // namespace grappler
}  // namespace tensorflow

  // Returns the device which created the handle.
  virtual const char* DeviceName(Status* status) const = 0;
  // Returns the device where the tensor was placed.
  virtual const char* BackingDeviceName(Status* status) const = 0;
  // Returns the device type which created the handle.
  virtual const char* DeviceType(Status* status) const = 0;
  // Returns the device ID which created the handle.

#define TENSORFLOW_C_EAGER_C_API_REMOTE_TEST_UTIL_H_

// Run a function containing a MatMul op and check its output.
// If heavy_load_on_streaming_rpc is true, send some rpc requests before the one
// which creates a remote input, to simulate a scenario that the remote input
// is not ready when we start running an op or a function.
void TestRemoteExecuteSilentCopies(bool async, bool remote, bool func,

#include "tensorflow/core/platform/status.h"

namespace tensorflow {

// Builds and returns a `TracingContext` using the default tracing impl.
AbstractContext* BuildFunction(const char* fn_name);

// Creates parameters (placeholders) in the tracing `ctx` using the shape and
// dtype of `inputs`.
Status CreateParamsForInputs(AbstractContext* ctx,
                             absl::Span<AbstractTensorHandle* const> inputs,

// Creation and deletion functions are optional.
typedef struct TP_Optimizer {
  size_t struct_size;
  void* ext;  // reserved for future use

  // [Optional]
  // Create function for optimizer.
  void* (*create_func)();

  // Optimizer function for optimizer. The first param is an optimizer created
  // by create_func. The second param is input graph. The third param is

void TF_SetTracingImplementation(const char* name, TF_Status*);

// Creates a new TensorFlow function. A Function is an execution context, and as
// such it can trace operations through TF_ExecuteOperation. After completing
// tracing, a function can be obtained by TF_FinalizeFunction.
TF_ExecutionContext* TF_CreateFunction(const char* fn_name, TF_Status* status);

// Creates a context for eager execution of operations.

    TF_Status* status);

// -----------------------------------------------------------------------------
// 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.

  size_t guard_size;

  // The NUMA node to use, -1 implies that there should be no NUMA affinity for
  // this thread.
  int numa_node;
} TF_ThreadOptions;

// Creates the specified directory. Typical status code are:
//  * TF_OK - successfully created the directory
//  * TF_ALREADY_EXISTS - directory already exists
//  * TF_PERMISSION_DENIED - dirname is not writable

namespace tensorflow {

// Abstract interface to a context.
//
// This serves as a factory for creating `AbstractOperation`s and for
// registering traced functions.
// Operations creation within a context can only be executed in that context
// (for now at least).
// Implementations of the context may contain some state e.g. an execution
// environment, a traced representation etc.
class AbstractContext {

  const TF_Output* const body_inputs;
  TF_Output* const body_outputs;

  // Unique null-terminated name for this while loop. This is used as a prefix
  // for created operations.
  const char* name;
} TF_WhileParams;

// Creates a TF_WhileParams for creating a while loop in `g`. `inputs` are
// outputs that already exist in `g` used as initial values for the loop
// variables.
//