conditions["object-lock-mode"] = []string{string(retMode)}
	conditions["object-lock-retain-until-date"] = []string{retDate.UTC().Format(time.RFC3339)}
	if retDays > 0 {
		conditions["object-lock-remaining-retention-days"] = []string{strconv.Itoa(retDays)}
	}
	if retMode == objectlock.RetGovernance && byPassSet {
		byPassSet = globalIAMSys.IsAllowed(policy.Args{
			AccountName:     cred.AccessKey,

  TFE_TensorHandle* retvals[1] = {nullptr};
  int num_retvals = 1;
  TFE_Execute(func, &retvals[0], &num_retvals, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  ASSERT_EQ(1, num_retvals);
  TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteTensorHandle(retvals[0]);
  float sum = 0;

		key.Extension = "." + vals[1]
		key.Name = strings.TrimSuffix(key.Name, key.Extension)
	}
	return
}

// replayItems - Reads the items from the store and replays.
func replayItems[I any](store Store[I], doneCh <-chan struct{}, log logger, id string) <-chan Key {
	keyCh := make(chan Key)

	go func() {
		defer xioutil.SafeClose(keyCh)

  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TFE_TensorHandle* retvals[1];
  int num_retvals = 1;
  TFE_Execute(matmul, &retvals[0], &num_retvals, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  auto* retval_task0 =
      TFE_TensorHandleCopyToDevice(retvals[0], ctx, local_device_name, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

                     int* num_retvals, ForwardOperation* forward_op_,
                     Tape* tape, const GradientRegistry& registry) {
  TF_RETURN_IF_ERROR(op_->Execute(retvals, num_retvals));
  for (int i = 0; i < *num_retvals; i++) {
    // TODO(srbs): Manage refcount of ForwardOperation's inputs/outputs.
    forward_op_->outputs.push_back(retvals[i]);
  }

                       ImmediateExecutionTensorHandle** retvals,
                       int* num_retvals) override {
    std::vector<TFE_TensorHandle*> outputs(*num_retvals);
    TF_Status status;
    device_.execute(tensorflow::wrap(op), num_retvals, outputs.data(), &status,
                    info_);
    if (status.status.ok()) {
      for (int i = 0; i < *num_retvals; ++i) {
        retvals[i] = tensorflow::unwrap(outputs[i]);

                                 ForwardOperation*);

// Make the call to `Tape::RecordOperation`.
absl::Status Execute(AbstractOperation*, AbstractContext*,
                     absl::Span<AbstractTensorHandle*> retvals,
                     int* num_retvals, ForwardOperation*, Tape*,
                     const GradientRegistry&);

}  // namespace internal
}  // namespace gradients
}  // namespace tensorflow

      }
      tf_outputs[i] = t->output_;
    }
    TF_AddInputList(op_.get(), tf_outputs.data(), tf_outputs.size());
    return absl::OkStatus();
  }
  absl::Status Execute(absl::Span<AbstractTensorHandle*> retvals,
                       int* num_retvals) override {
    auto* tf_opdesc = op_.release();
    if (tf_opdesc == nullptr) {
      return errors::InvalidArgument("AbstractOp is incomplete.");
    }

  virtual absl::Status AddInput(AbstractTensorHandle* input) = 0;
  virtual absl::Status AddInputList(
      absl::Span<AbstractTensorHandle* const> inputs) = 0;
  virtual absl::Status Execute(absl::Span<AbstractTensorHandle*> retvals,
                               int* num_retvals) = 0;

  virtual absl::Status SetAttrString(const char* attr_name, const char* data,
                                     size_t length) = 0;