class_docstr: Applies Quantized Instance Normalization over a 4D input
class_docstr_tok: ["Applies","Quantized","Instance","Normalization","over","a","4D","input"]
code: def __init__(
      self,
      num_features: int,
      eps: float = 1e-5,
      momentum: float = 0.1,
      affine: bool = False,
      track_running_stats: bool = False,
      **kwargs,
  ):
      super(QuantInstanceNorm2d, self).__init__(
          num_features, eps, momentum, affine, track_running_stats
      )
      quant_desc_input = _utils.pop_quant_desc_in_kwargs(
          self.__class__, input_only=True, **kwargs
      )
      self.init_quantizer(quant_desc_input)
  
imports: from torch.nn.modules.batchnorm import _NormBase
  import torch.nn.functional
  from torch.nn.modules import instancenorm
  from csrc.pytorch.pytorch_quantization.nn import TensorQuantizer
  from csrc.pytorch.pytorch_quantization import tensor_quant
  from .None import _utils
label_desc: Use image node_id 1 to create a new QuantInstanceNorm2d object from inherited base classes: instancenorm, _utils with example: obj = QuantInstanceNorm2d(num_features, eps, momentum, affine, track_running_stats)