code: def convert_random_normal(node, **kwargs):
      
      name, input_nodes, attrs = get_inputs(node, kwargs)
  
      # Converting to float32
      mean = float(attrs.get("loc", 0))
      scale = float(attrs.get("scale", 1.0))
      shape = convert_string_to_list(attrs.get('shape', '[]'))
      dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(attrs.get('dtype', 'float32'))]
  
      node = onnx.helper.make_node(
          'RandomNormal',
          input_nodes,
          [name],
          mean=mean,
          scale=scale,
          dtype=dtype,
          shape=shape,
          name=name
      )
      return [node]
docs: Map MXNet's random_normal operator attributes to onnx's RandomNormal
      operator and return the created node.