class_docstr: ONNXRuntime will use numpy as data type.
  ONNXRuntime quantization in torch will use torchmetrics
class_docstr_tok: ["ONNXRuntime","will","use","numpy","as","data","type",".","ONNXRuntime","quantization","in","torch","will","use","torchmetrics"]
code: def stack(self, preds, labels):
      # calculate accuracy
      preds, labels = super().stack(preds, labels)
      preds = torch.from_numpy(preds)
      labels = torch.from_numpy(labels)
      return preds, labels
  
imports: from ..metric import ONNXRuntimeINCMetic
  import torch
label_desc: Use image node_id 1 for calling the PytorchONNXRuntimeINCMetic obj's underlying member method code with example usage: obj.stack(preds, labels) and returns: preds,  labels