class_docstr: This is the quantized version of :class:`~torch.nn.InstanceNorm1d`.
  
  Additional args:
      * **scale** - quantization scale of the output, type: double.
      * **zero_point** - quantization zero point of the output, type: long.
class_docstr_tok: ["This","is","the","quantized","version","of",":","class",":","`","~torch.nn.InstanceNorm1d","`",".","Additional","args",":","*","*","*","scale","*","*","-","quantization","scale","of","the","output",",","type",":","double",".","*","*","*","zero_point","*","*","-","quantization","zero","point","of","the","output",",","type",":","long","."]
code: def __init__(
      self,
      num_features,
      weight,
      bias,
      scale,
      zero_point,
      eps=1e-5,
      momentum=0.1,
      affine=False,
      track_running_stats=False,
      device=None,
      dtype=None,
  ) -> None:
      factory_kwargs = {"device": device, "dtype": dtype}
      super().__init__(
          num_features,
          eps,
          momentum,
          affine,
          track_running_stats,
          **factory_kwargs,
      )
      self.weight = weight
      self.bias = bias
      self.register_buffer(
          "scale", torch.tensor(scale, **factory_kwargs)
      )
      self.register_buffer(
          "zero_point", torch.tensor(zero_point, **factory_kwargs)
      )
  
imports: import torch
label_desc: Use image node_id 1 to create a new InstanceNorm1d object from inherited base classes: torch.nn with example: obj = InstanceNorm1d(num_features, weight, bias, scale, zero_point, eps, momentum, affine, track_running_stats, device, dtype)