code: def _to_tensor(self):
      input = self.model_inputs[0]
      rank = len(input.zvalue.shape)
  
      if rank == 4:
          epsilon = (
              float(self.onnx_attr["epsilon"])
              if "epsilon" in self.onnx_attr
              else 0.001
          )
          momentum = float(
              self.onnx_attr["momentum"]
              if "momentum" in self.onnx_attr
              else 0.99
          )
          dim_ordering = "th"
          if len(self._input_list) == 5:
              mean = self._input_list[3].zvalue
              variance = self._input_list[4].zvalue
          else:
              mean = self._input_list[1].zvalue
              variance = self._input_list[2].zvalue
          batch_norm = zlayers.BatchNormalization(
              epsilon=epsilon,
              momentum=momentum,
              dim_ordering=dim_ordering,
          )
          norm_tensor = batch_norm(input.zvalue)
          norm_tensor.node.element().set_running_mean(mean)
          norm_tensor.node.element().set_running_std(variance)
          return norm_tensor
      else:
          raise Exception("not supported.")
  
file_path: analytics-zoo/pyzoo/zoo/pipeline/api/onnx/mapper/batchnormalization.py
imports: from zoo.pipeline.api.onnx.mapper.operator_mapper import OperatorMapper
  from zoo.pipeline.api.onnx.onnx_helper import OnnxHelper
  import zoo.pipeline.api.keras.layers
  import zoo.pipeline.api.autograd
  import numpy
label_desc: Use image node_id 5 for calling the BatchNormalizationMapper obj's underlying member method code with example usage: obj._to_tensor() and returns: norm_tensor