class_docstr: This is the quantized version of :class:`~torch.nn.InstanceNorm3d`.
  
  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.InstanceNorm3d","`",".","Additional","args",":","*","*","*","scale","*","*","-","quantization","scale","of","the","output",",","type",":","double",".","*","*","*","zero_point","*","*","-","quantization","zero","point","of","the","output",",","type",":","long","."]
code: def from_float(cls, mod):
      (
          scale,
          zero_point,
      ) = mod.activation_post_process.calculate_qparams()
      new_mod = cls(
          mod.num_features,
          mod.weight,
          mod.bias,
          float(scale),
          int(zero_point),
          mod.eps,
          mod.affine,
      )
      return new_mod
  
imports: import torch
label_desc: Use image node_id 4 for calling the InstanceNorm3d obj's underlying member method code with example usage: obj.from_float(cls, mod) and returns: new_mod