QuantTensor quantization invariant op on per tensor vs per channel

[volcacius]

Currently we don't check whether a QuantTensor is per channel or per tensor when we do certain ops like flatten or shuffle/unshuffle that are sensitive to it.

[ScXfjiang]

PyTorch Tensor has the qscheme attribute to specify how to quantize a tensor, which includes:

• torch.per_tensor_affine
• torch.per_tensor_symmetric
• torch.per_channel_affine
• torch.per_channel_symmetric

In Brevitas, if granularity is our only concern so far, maybe we can assess it by examining the shape of the quantization parameters (scale, zero point):

• shape [1] --> per_tensor quantization
• otherwise --> per_channel quantization

#891