class_docstr: For onednn backend only
  A LinearLeakyReLU module fused from Linear and LeakyReLU modules
  We adopt the same interface as :class:`torch.ao.nn.quantized.Linear`.
  Attributes:
      Same as torch.ao.nn.quantized.Linear
      + negative_slope
  Examples::
      >>> # xdoctest: +SKIP
      >>> m = nn.intrinsic.LinearLeakyReLU(20, 30, 0.01)
      >>> input = torch.randn(128, 20)
      >>> output = m(input)
      >>> print(output.size())
      torch.Size([128, 30])
code: def __init__(
      self,
      in_features,
      out_features,
      negative_slope,
      bias=True,
      dtype=torch.qint8,
  ):
      super().__init__(in_features, out_features, bias, dtype)
      self.negative_slope = negative_slope
  
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 LinearLeakyReLU object from inherited base classes: nnq with example: obj = LinearLeakyReLU(in_features, out_features, negative_slope, bias, dtype)