It depends on the tensor and how you would define “features”.
Based on the tensor shape it could be an intermediate activation in the shape [batch_size, channels, height, width].
If you are flattening it and passing to e.g. a linear layers, the usual approach would be to use: x = x.view(x.size(0), -1), which would result in 2048*25*19=972800 “features”.