Set the pattern to configure.
  The pattern can be a float module, functional operator, pytorch
  operator, or a tuple combination of the above. Tuple patterns
  are treated as sequential patterns, and currently only tuples of
  2 or 3 elements are supported.

  Return type:
     *BackendPatternConfig*

set_qat_module(qat_module)
  Set the module that represents the QAT implementation for this
  pattern.

  Return type:
     *BackendPatternConfig*

set_reference_quantized_module(reference_quantized_module)
  Set the module that represents the reference quantized
  implementation for this pattern's root module.

  For more detail, see "set_root_module()".

  Return type:
     *BackendPatternConfig*

set_root_module(root_module)
  Set the module that represents the root for this pattern.

  When we construct the reference quantized model during the
  convert phase, the root modules (e.g. torch.nn.Linear for
