BackendConfig
class torch.ao.quantization.backend_config.BackendConfig(name='')
Config that defines the set of patterns that can be quantized on a
   given backend, and how reference quantized models can be produced
   from these patterns.
A pattern in this context refers to a module, a functional, an
   operator, or a directed acyclic graph of the above. Each pattern
   supported on the target backend can be individually configured
   through "BackendPatternConfig" in terms of:


The supported input/output activation, weight, and bias data
      types


How observers and quant/dequant ops are inserted in order to
      construct the reference pattern, and


(Optionally) Fusion, QAT, and reference module mappings.


The format of the patterns is described in: https://github.com/pyt
   orch/pytorch/blob/master/torch/ao/quantization/backend_config/READ
   ME.md
Example usage:
  import torch
  from torch.ao.quantization.backend_config import (
