EXPECT_TRUE(differencer.Compare(node_in, node_out));
}

TEST(CAPI, TestTensorNonScalarBytesAllocateDelete) {
  const int batch_size = 4;
  const int num_dims = 2;
  int64_t* dims = new int64_t[num_dims];
  int64_t num_elements = 1;
  dims[0] = batch_size;
  dims[1] = 1;
  for (int64_t i = 0; i < num_dims; ++i) {
    num_elements *= dims[i];
  }

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# Release 2.7.0

## Breaking Changes

*   `tf.keras`:

    *   The methods `Model.fit()`, `Model.predict()`, and `Model.evaluate()`
        will no longer uprank input data of shape `(batch_size,)` to become
        `(batch_size, 1)`. This enables `Model` subclasses to process scalar
        data in their `train_step()`/`test_step()`/`predict_step()` methods. \
        Note that this change may break certain subclassed models. You can

		} else if result.Error != nil {
			tx.AddError(result.Error)
		}

		if tx.Error != nil || int(result.RowsAffected) < batchSize {
			break
		}

		if totalSize > 0 {
			if totalSize <= int(rowsAffected) {
				break
			}
			if totalSize/batchSize == batch {
				batchSize = totalSize % batchSize
			}
		}

		// Optimize for-break
		resultsValue := reflect.Indirect(reflect.ValueOf(dest))

}

func (c createG[T]) Create(ctx context.Context, r *T) error {
	return c.g.apply(ctx).Create(r).Error
}

func (c createG[T]) CreateInBatches(ctx context.Context, r *[]T, batchSize int) error {
	return c.g.apply(ctx).CreateInBatches(r, batchSize).Error
}

type chainG[T any] struct {
	execG[T]
}

func (c chainG[T]) getInstance() *DB {
	var r T
	return c.g.apply(context.Background()).Model(r).getInstance()

	const BatchSize = 250 // We process the objects in batches - 250 is a reasonable default.
	var (
		metadata = make([]map[string]string, 0, BatchSize)
		buckets  = make([]string, 0, BatchSize)
		names    = make([]string, 0, BatchSize)
	)
	for len(objects) > 0 {
		N := min(len(objects), BatchSize)
		batch := objects[:N]

	}

	targetUsers := users[3:8]
	for i := 0; i < len(targetUsers); i++ {
		AssertEqual(t, results[i], targetUsers[i])
	}

	var sub1 []User
	// limit < batchSize
	if result := DB.Limit(5).Where("name = ?", users[0].Name).FindInBatches(&sub1, 10, func(tx *gorm.DB, batch int) error {
		return nil
	}); result.Error != nil || result.RowsAffected != 5 {