code: def convert_multinomial(node, **kwargs):
      
      name, input_nodes, attrs = get_inputs(node, kwargs)
      dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(attrs.get("dtype", 'int32'))]
      sample_size = convert_string_to_list(attrs.get("shape", '1'))
      if len(sample_size) < 2:
          sample_size = sample_size[-1]
      else:
          raise AttributeError("ONNX currently supports integer sample_size only")
      node = onnx.helper.make_node(
          "Multinomial",
          input_nodes,
          [name],
          dtype=dtype,
          sample_size=sample_size,
          name=name,
      )
      return [node]
docs: Map MXNet's multinomial operator attributes to onnx's
      Multinomial operator and return the created node.