}

TF_Tensor* BoolTensor(bool v) {
  const int num_bytes = sizeof(bool);
  bool* values = new bool[1];
  values[0] = v;
  return TF_NewTensor(TF_BOOL, nullptr, 0, values, num_bytes, &BoolDeallocator,
                      nullptr);
}

TF_Tensor* Int8Tensor(const int64_t* dims, int num_dims, const char* values) {
  int64_t num_values = 1;
  for (int i = 0; i < num_dims; ++i) {
    num_values *= dims[i];
  }

namespace tensorflow {
namespace gradients {
Status NotDifferentiableGradientFunction::Compute(
    AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> grad_outputs,
    absl::Span<AbstractTensorHandle*> grad_inputs) {
  for (int i = 0; i < grad_inputs.size(); i++) {
    grad_inputs[i] = nullptr;
  }
  return OkStatus();
}

Status RegisterNotDifferentiable(GradientRegistry* registry, const string& op) {

      MatMulOp(context, hcpu, hdevice), TFE_DeleteOp);
  TFE_OpSetDevice(matmul.get(), name, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  TFE_TensorHandle* retval;
  int num_retvals = 1;
  TFE_Execute(matmul.get(), &retval, &num_retvals, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  ASSERT_TRUE(executed);

  TFE_DeleteTensorHandle(retval);

  if (ops == nullptr)
    return errors::FailedPrecondition(
        "Trying to register filesystem without operations");

  if (ops->init == nullptr)
    return errors::FailedPrecondition(
        "Trying to register filesystem without `init` operation");

  if (ops->cleanup == nullptr)
    return errors::FailedPrecondition(
        "Trying to register filesystem without `cleanup` operation");

TF_CAPI_EXPORT extern void TFE_ContextSetServerDefWithTimeout(
    TFE_Context* ctx, int keep_alive_secs, const void* proto, size_t proto_len,
    int64_t init_timeout_in_ms, TF_Status* status,
    bool clear_existing_contexts) {
  TFE_ContextSetServerDefWithTimeoutAndRetries(
      ctx, keep_alive_secs, proto, proto_len, init_timeout_in_ms,
      /*retries=*/0, status, clear_existing_contexts);
}

  }
  return server_def;
}

tensorflow::ServerDef GetServerDef(int num_tasks) {
  return GetServerDef("localhost", num_tasks);
}

tensorflow::ServerDef GetMultiClientServerDef(const std::string& job_name,
                                              int num_tasks,
                                              int num_virtual_gpus) {
  tensorflow::ServerDef server_def;
  server_def.set_protocol("grpc");

  ops->filesystem_ops = static_cast<TF_FilesystemOps*>(
      plugin_memory_allocate(TF_FILESYSTEM_OPS_SIZE));
  ops->filesystem_ops->init = tf_gcs_filesystem::Init;
  ops->filesystem_ops->cleanup = tf_gcs_filesystem::Cleanup;
  ops->filesystem_ops->new_random_access_file =
      tf_gcs_filesystem::NewRandomAccessFile;

namespace tf_posix_filesystem {

// Transfers up to `size` bytes from `dst_fd` to `src_fd`.
//
// This method uses `sendfile` specific to linux after 2.6.33.
int CopyFileContents(int dst_fd, int src_fd, off_t size) {
  off_t offset = 0;
  int bytes_transferred = 0;
  int rc = 1;
  // When `sendfile` returns 0 we stop copying and let callers handle this.
  while (offset < size && rc > 0) {
    // Use uint64 for safe compare SSIZE_MAX

    return absl::OkStatus();
  }
  Status SetAttrShapeList(const char* attr_name, const int64_t** dims,
                          const int* num_dims, int num_values) override {
    std::vector<PartialTensorShape> shapes;
    shapes.reserve(num_values);
    for (int i = 0; i < num_values; ++i) {
      if (num_dims[i] < 0) {
        shapes.emplace_back();
      } else {
        shapes.emplace_back(ArraySlice<int64_t>(

      TF_RETURN_IF_ERROR(fn_op->AddInput(input));
    }
    int retvals = outputs.size() - null_indices.size();
    std::vector<AbstractTensorHandle*> fn_outputs(retvals);
    TF_RETURN_IF_ERROR(fn_op->Execute(
        absl::Span<AbstractTensorHandle*>(fn_outputs.data(), fn_outputs.size()),
        &retvals));
    int skipped_indices = 0;
    for (int i = 0; i < outputs.size(); i++) {
      if (!null_indices.contains(i)) {