torch.quantize_per_channel
torch.quantize_per_channel(input, scales, zero_points, axis, dtype) -> Tensor
Converts a float tensor to a per-channel quantized tensor with
   given scales and zero points.
Parameters:
      * input (Tensor) -- float tensor to quantize
  * **scales** (*Tensor*) -- float 1D tensor of scales to use,
    size should match "input.size(axis)"

  * **zero_points** (*int*) -- integer 1D tensor of offset to use,
    size should match "input.size(axis)"

  * **axis** (*int*) -- dimension on which apply per-channel
    quantization

  * **dtype** ("torch.dtype") -- the desired data type of returned
    tensor. Has to be one of the quantized dtypes: "torch.quint8",
    "torch.qint8", "torch.qint32"

Returns:
      A newly quantized tensor
Return type:
      Tensor
Example:
  >>> x = torch.tensor([[-1.0, 0.0], [1.0, 2.0]])
