class_docstr: A LinearTanh module fused from Linear and Tanh modules
  
  We adopt the same interface as :class:`torch.ao.nn.quantized.Linear`.
  
  Attributes:
      Same as torch.ao.nn.quantized.Linear
  
  Examples::
  
      >>> # xdoctest: +SKIP
      >>> m = nn.intrinsic.LinearTanh(20, 30)
      >>> input = torch.randn(128, 20)
      >>> output = m(input)
      >>> print(output.size())
      torch.Size([128, 30])
code: def from_reference(cls, ref_mod, output_scale, output_zero_point):
      linear = ref_mod[0]
      qlinear_tanh = cls(linear.in_features, linear.out_features)
      qweight = linear.get_quantized_weight()
      qlinear_tanh.set_weight_bias(qweight, linear.bias)
      qlinear_tanh.scale = float(output_scale)
      qlinear_tanh.zero_point = int(output_zero_point)
      return qlinear_tanh
  
imports: import torch
  import torch.ao.nn.quantized
  import torch.ao.nn.intrinsic
  from torch.ao.nn.quantized.modules.utils import _quantize_weight
label_desc: Use image node_id 5 for calling the LinearTanh obj's underlying member method code with example usage: obj.from_reference(cls, ref_mod, output_scale, output_zero_point) and returns: qlinear_tanh