How to apply supervised machine learning when the target label depends on multiple input rows?
What about doing a concatenation of your rows (i.e. Attr1 -> Attr12) , such that you now have 3*4 features (because 4 rows of 3 features) as an input to a multiclass classification model?

For instance, first sample would be described by :

X = [1.1, 1.4, 2.5, 2.3, 2.5, 2.7, 1.1, 1.6, 1.9, 1.5, 1.6, 1.7]
y = "A"


Otherwise, there is no issue in giving 2D or 3D inputs towards a classifier. Take for example convolutional neural networks that do operations on images!