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 from_float(cls, mod):
      assert (
          type(mod) == nni.LinearLeakyReLU
      ), "Input float module should be LinearLeakyReLU"
      assert hasattr(
          mod, "qconfig"
      ), "Input float module must have qconfig defined"
      activation_post_process = mod.activation_post_process
      leaky_relu = mod[1]
      mod = mod[0]
      weight_post_process = mod.qconfig.weight()
      weight_post_process(mod.weight)
      dtype = weight_post_process.dtype
      act_scale, act_zp = activation_post_process.calculate_qparams()  # type: ignore[union-attr,operator]
      assert (
          dtype == torch.qint8
      ), "Weight observer must have dtype torch.qint8"
      qweight = _quantize_weight(
          mod.weight.float(), weight_post_process
      )
      qlinear_leaky_relu = cls(
          mod.in_features,
          mod.out_features,
          leaky_relu.negative_slope,
          dtype=dtype,
      )
      qlinear_leaky_relu.set_weight_bias(qweight, mod.bias)
      qlinear_leaky_relu.scale = float(act_scale)
      qlinear_leaky_relu.zero_point = int(act_zp)
      return qlinear_leaky_relu
  
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 4 for calling the LinearLeakyReLU obj's underlying member method code with example usage: obj.from_float(cls, mod) and returns: qlinear_leaky_relu