Hi,

Although SampleBatch brings convenience and efficiency, it forces us to develop transformers that are compatible with it.

Imagine the simple transformer bellow:

class FakeTransformer(TransformerMixin, BaseEstimator):
    def fit(self, X, y=None):
        return self

    def transform(self, X):
        return X + 1

    def _more_tags(self):
        return {"stateless": True, "requires_fit": False}

I can easily use it with numpy arrays as input.

    transformer = FakeTransformer()
    X = np.zeros(shape=(3, 160, 160))    
    transformed_X = transformer.transform(X)

However, I run into problems once I wrap it as a sample

    sample = Sample(X)
    transformer_sample = wrap(["sample"], transformer)
    my_beautiful_sample = [s.data for s in transformer_sample.transform([sample])]
    # THIS DOESN'T WORK

With this wrap, the input X of FakeTransformer.transform will be SampleBatch and not numpy array. Hence, I can't do X+1.

I can approach this issue in my transformer by doing this:

    def transform(self, X):
        X = np.asarray(X)
        return X + 1

However, this is a blocker if we want to use estimators developed by other people outside of our circle.

Do you think it is sensible to have X wrapped as a SampleBatch once SampleTransform is used? It breaks encapsulation.

Thanks