RemoveFile_Locked(key.first);
    }
  }

  // Insert a new empty block, setting the bookkeeping to sentinel values
  // in order to update them as appropriate.
  auto new_entry = std::make_shared<Block>();
  lru_list_.push_front(key);
  lra_list_.push_front(key);
  new_entry->lru_iterator = lru_list_.begin();
  new_entry->lra_iterator = lra_list_.begin();
  new_entry->timestamp = timer_seconds_();

  if (ops_->new_random_access_file == nullptr)
    return errors::Unimplemented(tensorflow::strings::StrCat(
        "Filesystem for ", fname, " does not support NewRandomAccessFile()"));

  UniquePtrTo_TF_Status plugin_status(TF_NewStatus(), TF_DeleteStatus);
  auto file = std::make_unique<TF_RandomAccessFile>();
  std::string translated_name = TranslateName(fname);

  TF_DeleteExecutionContext(ctx);
}

TEST_P(UnifiedCAPI, TestBasicGraph) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);

  // Start a new function / execution context.
  string fn_name = "double";
  TF_ExecutionContext* graph_ctx =
      TF_CreateFunction(fn_name.c_str(), status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  TF_DeleteStatus(status);

  int port = tensorflow::testing::PickUnusedPortOrDie();
  // 3. Replace worker1 with a new worker in server_def_0 and server_def_1.
  ReplaceTaskInServerDef(&server_def_0, /*task_index=*/1, "localhost", port);
  ReplaceTaskInServerDef(&server_def_1, /*task_index=*/1, "localhost", port);
  // 4. Start a new task to replace worker1.
  server_def_0.set_task_index(1);
  worker_server1.release();

    calls++;
    memset(buffer, 'x', n);
    TF_SetStatus(status, TF_OK, "");
    return n;
  };
  std::vector<char> out;
  std::unique_ptr<NowSecondsEnv> env(new NowSecondsEnv);
  // Create a cache with max staleness of 2 seconds, and verify that it works as
  // expected.
  tf_gcs_filesystem::RamFileBlockCache cache1(
      8, 16, 2 /* max staleness */, fetcher,

  const char new_local_device_name[] =
      "/job:worker/replica:0/task:0/device:CPU:0";

  auto* h0_task1_new = TFE_TensorHandleCopyToDevice(
      h0_task0_new, ctx, new_remote_device_name, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TFE_DeleteTensorHandle(h0_task0_new);
  TFE_DeleteTensorHandle(h0_task1_new);

  CheckRemoteMatMulExecutesOK(ctx, new_remote_device_name,

                                                         TF_Status* status);

// Create a new TFE_TensorHandle with the same contents as 'h' but placed
// in the memory of the device name 'device_name'.
// If source and destination are the same device, then this creates a new handle
// that shares the underlying buffer. Otherwise, it currently requires at least

*   When you contribute a new feature to TensorFlow, the maintenance burden is
    (by default) transferred to the TensorFlow team. This means that the benefit
    of the contribution must be compared against the cost of maintaining the
    feature.
*   Full new features (e.g., a new op implementing a cutting-edge algorithm)
    typically will live in

// -----------------------------------------------------------------------------
// Dynamic cluster API.

// Update an existing context with a new set of servers defined in a ServerDef
// proto. Servers can be added to and removed from the list of remote workers
// in the context. A New set of servers identified by the ServerDef must be up
// when the context is updated.
//
// This API is for experimental usage and may be subject to change.

TF_Tensor* Int32Tensor(int32_t v) {
  const int num_bytes = sizeof(int32_t);
  int32_t* values = new int32_t[1];
  values[0] = v;
  return TF_NewTensor(TF_INT32, nullptr, 0, values, num_bytes,
                      &Int32Deallocator, nullptr);
}

TF_Tensor* DoubleTensor(double v) {
  const int num_bytes = sizeof(double);
  double* values = new double[1];
  values[0] = v;
  return TF_NewTensor(TF_DOUBLE, nullptr, 0, values, num_bytes,