TF_Status*);

// TF_ExecuteOperation will, if in eager mode, execute, if in graph mode, maybe
// capture some inputs and then add a node in the graph. The output tensors are
// returned through the provided TF_OutputList.
// Any active tape will observe the effects of this execution.
void TF_ExecuteOperation(TF_AbstractOp* op, int num_inputs,

 * parameter specified by `input_index`.
 *
 * I.e. if y = <output of the forward model> and w = inputs[input_index],
 * this will calculate dy/dw numerically.
 *
 * `use_function` indicates whether to use graph mode(true) or eager(false).
 *
 * `numerical_grad` is the pointer to the AbstractTensorHandle* which will
 * hold the numerical gradient data at the end of the function.
 */
Status CalcNumericalGrad(AbstractContext* ctx, Model forward,

  /// `max_age` is also 0, the cache will not be populated).
  ExpiringLRUCache(uint64_t max_age, size_t max_entries,
                   std::function<uint64_t()> timer_seconds = TF_NowSeconds)
      : max_age_(max_age),
        max_entries_(max_entries),
        timer_seconds_(timer_seconds) {}

  /// Insert `value` with key `key`. This will replace any previous entry with
  /// the same key.

#ifdef __cplusplus
extern "C" {
#endif

// Test only exports of the monitoring Cell Reader API which allows tests to
// read current values from streamz counters defined in other modules.
//
// The code under test will have created streamz counters like this:
// auto* streamz = tensorflow::monitoring::Counter<1>::New("name",
// "description", "label");
// and then incremented that counter for various values of label:

typedef struct TF_Thread TF_Thread;

typedef struct TF_ThreadOptions {
  // Thread stack size to use (in bytes), zero implies that the system default
  // will be used.
  size_t stack_size;

  // Guard area size to use near thread stacks to use (in bytes), zero implies
  // that the system default will be used.
  size_t guard_size;

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

// and `underlying_devices` is
//   {"/job:localhost/replica:0/task:0/device:GPU:0",
//    "/job:localhost/replica:0/task:0/device:GPU:1"}
// Then executing an operation on CUSTOM:0 will execute it on GPU:0 and GPU:1.
//
// Implicit copies onto `device_name` are allowed, replicating the value once
// per device in `underlying_devices`. Implicit copies off of the device throw
// an error.
//

  bool compose;
  absl::Mutex block_cache_lock;
  std::shared_ptr<RamFileBlockCache> file_block_cache
      ABSL_GUARDED_BY(block_cache_lock);
  uint64_t block_size;  // Reads smaller than block_size will trigger a read
                        // of block_size.
  std::unique_ptr<ExpiringLRUCache<GcsFileStat>> stat_cache;
  GCSFile(google::cloud::storage::Client&& gcs_client);
  // This constructor is used for testing purpose only.

#include "tensorflow/c/experimental/filesystem/filesystem_interface.h"

// Initialize the POSIX filesystem.
//
// In general, the `TF_InitPlugin` symbol doesn't need to be exposed in a header
// file, since the plugin registration will look for the symbol in the DSO file
// that provides the filesystem functionality. However, the POSIX filesystem
// needs to be statically registered in some tests and utilities for building

  virtual const string& Name() const = 0;

  // Returns the operation's device name.
  //
  // The value returned may be different from the one set by SetDeviceName, but
  // it will be compatible with it: the name will be updated by device placement
  // logic to refer to the specific device chosen.
  //
  // Example: If one calls `op->SetDeviceName("/device:GPU")`, the value

// capturing the return value in an 'auto' local variable. Most users will not
// need more sophisticated syntax than that.
//
// Example:
//   void func() {
//     FILE* fp = fopen("data.txt", "r");
//     if (fp == nullptr) return;
//     auto fp_cleaner = gtl::MakeCleanup([fp] { fclose(fp); });
//     // No matter what, fclose(fp) will happen.
//     DataObject d;
//     while (ReadDataObject(fp, &d)) {