code: def parse_batchnorm_layer(
      reader, node, inputs_map, input_shapes, graph, config
  ):
      layer = {}
  
      layer["class_name"] = "BatchNormalization"
      layer["data_format"] = "channels_first"
      layer["name"] = node.name
      layer["inputs"] = get_onnx_input_name(node, graph)
  
      # Other attributes
      layer["epsilon"] = get_onnx_attribute(node, "epsilon")
      layer["momentum"] = get_onnx_attribute(node, "momentum")
  
      reader.add_input(layer["name"], node.input)
  
      in_size = 1
      for dim in input_shapes[0][1:]:
          in_size *= dim
  
      layer["n_in"] = layer["n_out"] = in_size
  
      if len(input_shapes[0]) == 2:
          layer["n_filt"] = -1
      elif len(input_shapes[0]) > 2:
          layer["n_filt"] = input_shapes[0][
              1
          ]  # Always channel first for onnx
  
      return layer, [shape for shape in input_shapes[0]]
  
label_desc: Use image node_id 3 for calling a global function with example usage: parse_batchnorm_layer(reader, node, inputs_map, input_shapes, graph, config) and returns: layer,  unknown