utils/scripts/gen_onnx_gru_model.py [296:327]:
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    )

    # Error node definition
    err_node_def = onnx.helper.make_node(
        "Sub", name="error", inputs=["Y", "Y_ref"], outputs=["Y_err"]
    )

    # --------------------------------------------- GRAPH DEFINITION  --------------------------------------------------
    graph_input = list()
    graph_init = list()
    graph_output = list()

    # GRU inputs
    graph_input.append(helper.make_tensor_value_info("X", TensorProto.FLOAT, X_shape))
    graph_input.append(helper.make_tensor_value_info("W", TensorProto.FLOAT, W_shape))
    graph_input.append(helper.make_tensor_value_info("R", TensorProto.FLOAT, R_shape))
    if has_bias:
        graph_input.append(
            helper.make_tensor_value_info("B", TensorProto.FLOAT, B_shape)
        )
    if has_sequence_lens:
        graph_input.append(
            helper.make_tensor_value_info(
                "sequence_lens", TensorProto.INT32, sequence_lens_shape
            )
        )
    if has_initial_h:
        graph_input.append(
            helper.make_tensor_value_info(
                "initial_h", TensorProto.FLOAT, initial_h_shape
            )
        )
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utils/scripts/gen_onnx_lstm_model.py [399:430]:
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    )

    # Error node definition
    err_node_def = onnx.helper.make_node(
        "Sub", name="error", inputs=["Y", "Y_ref"], outputs=["Y_err"]
    )

    # --------------------------------------------- GRAPH DEFINITION  --------------------------------------------------
    graph_input = list()
    graph_init = list()
    graph_output = list()

    # LSTM inputs
    graph_input.append(helper.make_tensor_value_info("X", TensorProto.FLOAT, X_shape))
    graph_input.append(helper.make_tensor_value_info("W", TensorProto.FLOAT, W_shape))
    graph_input.append(helper.make_tensor_value_info("R", TensorProto.FLOAT, R_shape))
    if has_bias:
        graph_input.append(
            helper.make_tensor_value_info("B", TensorProto.FLOAT, B_shape)
        )
    if has_sequence_lens:
        graph_input.append(
            helper.make_tensor_value_info(
                "sequence_lens", TensorProto.INT32, sequence_lens_shape
            )
        )
    if has_initial_h:
        graph_input.append(
            helper.make_tensor_value_info(
                "initial_h", TensorProto.FLOAT, initial_h_shape
            )
        )
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