typedef std::map<Key, std::shared_ptr<Block>> BlockMap;

  /// Prune the cache by removing files with expired blocks.
  void Prune() ABSL_LOCKS_EXCLUDED(mu_);

  bool BlockNotStale(const std::shared_ptr<Block>& block)
      ABSL_EXCLUSIVE_LOCKS_REQUIRED(mu_);

  /// Look up a Key in the block cache.
  std::shared_ptr<Block> Lookup(const Key& key) ABSL_LOCKS_EXCLUDED(mu_);

		{"driver.Valuer not equal", ModifyAt{Time: now, Valid: true}, ModifyAt{Time: now.Add(time.Second), Valid: true}, false},
		{"driver.Valuer equal (ptr to nil ptr)", (*ModifyAt)(nil), &ModifyAt{}, false},
	}
	for _, test := range assertEqualTests {
		t.Run(test.name, func(t *testing.T) {
			if out := AssertEqual(test.src, test.dst); test.out != out {

class Cluster;

struct TFStatusDeleter {
  void operator()(TF_Status* s) const { TF_DeleteStatus(s); }
};
using OwnedTFStatus = std::unique_ptr<TF_Status, TFStatusDeleter>;

struct TFBufferDeleter {
  void operator()(TF_Buffer* buf) const { TF_DeleteBuffer(buf); }
};
using OwnedTFBuffer = std::unique_ptr<TF_Buffer, TFBufferDeleter>;

class CGraphOptimizer : public CustomGraphOptimizer {
 public:

  server_def.set_job_name("worker");
  server_def.set_task_index(0);
  std::unique_ptr<tensorflow::ServerInterface> w0;
  ASSERT_TRUE(
      factory->NewServer(server_def, ServerFactory::Options(), &w0).ok());
  ASSERT_TRUE(w0->Start().ok());
  server_def.set_task_index(1);
  std::unique_ptr<tensorflow::ServerInterface> w1;
  ASSERT_TRUE(

    Status s = StatusFromTF_Status(status.get());
    CHECK_EQ(errors::OK, s.code()) << s.message();
  }
};

TEST_P(UnifiedCAPI, TestBasicEager) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TFE_ContextOptions* opts = TFE_NewContextOptions();
  TFE_ContextOptionsSetTfrt(opts, std::get<1>(GetParam()));

  Tape tape(/*persistent=*/false);
  tape.Watch(inputs[0]);  // Watch x.
  AbstractTensorHandle* exp_output;
  TF_RETURN_IF_ERROR(ops::Exp(ctx, inputs[0], &exp_output, "Exp"));
  std::unique_ptr<GradientFunction> gradient_function(
      new PassThroughGradientFunction);
  tape.RecordOperation(inputs, {exp_output}, gradient_function.release());
  TF_RETURN_IF_ERROR(tape.ComputeGradient(ctx,

#include <stdlib.h>

// gmp 5.0.0+ changed the type of the 3rd argument to mp_bitcnt_t,
// so, to support older versions, we wrap these two functions.
void _mpz_mul_2exp(mpz_ptr a, mpz_ptr b, unsigned long n) {
	mpz_mul_2exp(a, b, n);
}
void _mpz_div_2exp(mpz_ptr a, mpz_ptr b, unsigned long n) {
	mpz_div_2exp(a, b, n);
}
*/
import "C"

import (
	"os"
	"unsafe"
)

/*
 * one of a kind
 */

  google::cloud::storage::Client gcs_client;  // owned
  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);

	if reflect.DeepEqual(x, y) {
		return true
	}
	if x == nil || y == nil {
		return false
	}

	xval := reflect.ValueOf(x)
	yval := reflect.ValueOf(y)
	if xval.Kind() == reflect.Ptr && xval.IsNil() ||
		yval.Kind() == reflect.Ptr && yval.IsNil() {
		return false
	}

	if valuer, ok := x.(driver.Valuer); ok {
		x, _ = valuer.Value()
	}
	if valuer, ok := y.(driver.Valuer); ok {
		y, _ = valuer.Value()
	}

Status TestTensorHandleWithDims(AbstractContext* ctx, const T* data,
                                const int64_t* dims, int num_dims,
                                AbstractTensorHandle** tensor) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TFE_Context* eager_ctx =
      TF_ExecutionContextGetTFEContext(wrap(ctx), status.get());