code: def batch_norm_extract():
      consts = make_consts(
          "float32",
          [
              (32,),  # 0
              (32,),  # 1
              (32,),  # 2
              (32,),  # 3
          ],
      )
      metatable = {"relay.Constant": consts}
      mod = tvm.relay.parse(
          """
          #[version = "0.0.5"]
          def @main(%FunctionVar_0: Tensor[(1, 32, 112, 112), float32]) -> Tensor[(1, 32, 112, 112), float32] {
            %3 = nn.batch_norm(%FunctionVar_0, meta[relay.Constant][0], meta[relay.Constant][1], meta[relay.Constant][2], meta[relay.Constant][3]);
            %3.0
          }
          """,
          "from_string",
          None,
          metatable,
      )
      return {
          "name": "batch_norm_extract",
          "input_shapes": {"FunctionVar_0": [1, 32, 112, 112]},
          "input_dtypes": {"FunctionVar_0": "float32"},
          "mod": mod,
          "params": None,
          "main_dtype": "float32",
      }
  
imports: import tvm
  import onnx
  import numpy
  import logging
  import tvm.contrib.target.onnx
label_desc: Use image node_id 19 for calling a global function with example usage: batch_norm_extract() and returns: dict