//
// On success, place OK in status and return the newly created library handle.
// The caller owns the library handle.
//
// On failure, place an error status in status and return NULL.
TF_CAPI_EXPORT extern TF_Library* TF_LoadLibrary(const char* library_filename,
                                                 TF_Status* status);

TEST(DLPack, HandleFromDLPackStrides) {
  TF_Status* status = TF_NewStatus();
  TFE_ContextOptions* opts = TFE_NewContextOptions();
  TFE_Context* ctx = TFE_NewContext(opts, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteContextOptions(opts);

  TestHandleFromDLPack(status, ctx, {}, {});
  TestHandleFromDLPack(status, ctx, {4}, {});
  TestHandleFromDLPack(status, ctx, {4}, {1});

absl::Status SetAttrInt(AbstractOperation*, const char* attr_name,
                        int64_t value, ForwardOperation*);
absl::Status SetAttrFloat(AbstractOperation*, const char* attr_name,
                          float value, ForwardOperation*);
absl::Status SetAttrBool(AbstractOperation*, const char* attr_name, bool value,
                         ForwardOperation*);

  }
}

void* TFE_HandleToDLPack(TFE_TensorHandle* h, TF_Status* status) {
  auto tf_dlm_context = GetDlContext(h, status);
  if (!status->status.ok()) {
    return nullptr;
  }

  auto* tf_dlm_data = TFE_TensorHandleDevicePointer(h, status);
  if (!status->status.ok()) {
    return nullptr;
  }

  const Tensor* tensor = GetTensorFromHandle(h, status);
  TF_DataType data_type = static_cast<TF_DataType>(tensor->dtype());

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

    matmul = TFE_NewOp(ctx, "MatMulFunction", status);
    ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
    TFE_OpAddInput(matmul, h0_task0, status);
    ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);

//
// On success, returns the newly created library handle and places OK in status.
// The caller owns the library handle.
//
// On failure, returns nullptr and places an error status in status.
TF_CAPI_EXPORT extern TF_Library* TF_LoadPluggableDeviceLibrary(
    const char* library_filename, TF_Status* status);

// Frees the memory associated with the library handle.
// Does NOT unload the library.

    void testFileNotFoundStatus() {
        // Given
        int status = NtStatus.NT_STATUS_OBJECT_NAME_NOT_FOUND;

        // When/Then
        assertEquals((int) 0xC0000034L, status);
        // Should be an error status (0xC prefix)
        assertTrue((status & (int) 0xC0000000L) == (int) 0xC0000000L);
    }

    @Test
    @DisplayName("Should handle sharing violation status")

  if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return nullptr;
  }
  return tensorflow::unwrap(h)->BackingDeviceName(&status->status);
}

TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_TensorHandleCopySharingTensor(
    TFE_TensorHandle* h, TF_Status* status) {
  if (h == nullptr) {

the server side, will be replicated if destination also supports encryption.

Replication status can be seen in the metadata on the source and destination objects. On the source side, the `X-Amz-Replication-Status` changes from `PENDING` to `COMPLETED` or `FAILED` after replication attempt either succeeded or failed respectively. On the destination side, a `X-Amz-Replication-Status` status of `REPLICA` indicates that the object was replicated successfully. Any replication failures are automatically...

                                  <Status>Enabled</Status>
                                </VersioningConfiguration>`,
			err: nil,
		},
		{
			input: `<VersioningConfiguration xmlns="http://s3.amazonaws.com/doc/2006-03-01/">
                                  <Status>Enabled</Status>
                                  <ExcludedPrefixes>