class_docstr: A LinearReLU module fused from Linear and ReLU 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.LinearReLU(20, 30)
      >>> input = torch.randn(128, 20)
      >>> output = m(input)
      >>> print(output.size())
      torch.Size([128, 30])
code: def __init__(
      self, in_features, out_features, bias=True, dtype=torch.qint8
  ):
      super().__init__(in_features, out_features, bias, dtype)
  
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 1 to create a new LinearReLU object from inherited base classes: nnq with example: obj = LinearReLU(in_features, out_features, bias, dtype)