Named tensors (experimental)
Cornell University’s Sasha Rush has argued that, despite its ubiquity in deep learning, the traditional implementation of tensors has significant shortcomings, such as exposing private dimensions, broadcasting based on absolute position, and keeping type information in documentation. He proposed named tensors as an alternative approach.
Today, we name and access dimensions by comment:
# Tensor[N, C, H, W]
 images = torch.randn(32, 3, 56, 56)
 images.sum(dim=1)
 images.select(dim=1, index=0)

But naming explicitly leads to more readable and maintainable code:
NCHW = [‘N’, ‘C’, ‘H’, ‘W’]
   images = torch.randn(32, 3, 56, 56, names=NCHW)
   images.sum('C')
   images.select('C', index=0)

Quantization (experimental)