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 = np.concatenate(preds)
      labels = np.concatenate(labels)
      return preds, labels
  
imports: from ..core import BaseINCMetric
  import numpy
label_desc: Use image node_id 1 for calling the ONNXRuntimeINCMetic obj's underlying member method code with example usage: obj.stack(preds, labels) and returns: preds,  labels