I have a dataset for which every object has multiple features, and some of those features exist in tuples, like as in list of $(x, y)$ coordinates for each object. I know I can create an input layer of number of neurons equal to number of features of each object, but how do I pass these 'tuple' sets to the input. Do I break them and append to create a longer list of features, but will that not lead to loss in some correlation between these 2 sets of points from tuples?
This sounds like an ideal situation for a complex valued neural net. A readable introduction is https://makeyourownneuralnetwork.blogspot.com/2016/05/complex-valued-neural-networks.html
You can try dimensionality reduction on those tuples.
Draw each tuple-valued feature separately on a 2-dimensional plane. If the (x,y) points follow a line (maybe a curved line), then the tuple can be replaced with a single value. One point on the line is taken as an origin, and all other (x, y)-points are replaced with the distances to the origin along the line. If the line is curved, the distances are also curved, not the Euclidean distances.
If (x,y) points in a feature don't follow a line, but spread inside a two dimensional region then it is safe to treat them as two separate features.