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 forward(self, x: torch.Tensor) -> torch.Tensor:
      return torch.ops.quantized.linear_tanh(
          x,
          self._packed_params._packed_params,
          self.scale,
          self.zero_point,
      )
  
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 2 for calling the LinearTanh obj's underlying member method code with example usage: obj.forward(x) and returns: torch