def METHOD_NAME(self, draw):
    alpha = draw(st.floats(min_value=1, max_value=1))  #required in pass
    x_num_col_dims = draw(st.floats(min_value=0, max_value=1))
    y_num_col_dims = draw(st.floats(min_value=0, max_value=1))
    int32_values_1 = draw(st.integers(min_value=1, max_value=40))
    int32_values_2 = draw(st.integers(min_value=1, max_value=40))
    int32_values_3 = draw(st.integers(min_value=1, max_value=40))
    squeeze2_input_shape = [int32_values_1, int32_values_2, 1, 1]
    matmul_input_shape = [squeeze2_input_shape[1], int32_values_3]
    scale_x = draw(st.sampled_from([0.1, 1.1]))
    scale_y = draw(st.sampled_from([0.1, 1.1]))
    scale_out = draw(st.sampled_from([0.1, 1.1]))
    force_fp32_output = draw(st.booleans())
    squeeze2_op = OpConfig(
        type="squeeze2",
        inputs={"X": ["squeeze2_input_x"]},
        outputs={
            "Out": ["squeeze2_output"],
            "XShape": ["squeeze2_output_XShape"]
        },
        attrs={
            "axes": [2, 3]  #required in pass
        })
    matmul_op = OpConfig(
        type="matmul",
        inputs={"X": ["squeeze2_output"],
                "Y": ["matmul_input"]},
        outputs={"Out": ["output_data"]},
        attrs={
            "transpose_X": False,  #required in pass
            "transpose_Y": False,  #required in pass
            "x_num_col_dims": x_num_col_dims,
            "y_num_col_dims": y_num_col_dims,
            "Scale_x": scale_x,
            "Scale_y": scale_y,
            "Scale_out": scale_out,
            "force_fp32_output": force_fp32_output,
            "alpha": alpha,
            "fused_reshape_X": [],
            "fused_transpose_X": [],
            "fused_reshape_Y": [],
            "fused_transpose_Y": [],
            "fused_reshape_Out": [],
            "fused_transpose_Out": [],
            "head_number": int(1)
        })
    ops = [squeeze2_op, matmul_op]
    program_config = ProgramConfig(
        ops=ops,
        weights={},
        inputs={
            "squeeze2_input_x": TensorConfig(shape=squeeze2_input_shape),
            "matmul_input": TensorConfig(shape=matmul_input_shape)
        },
        outputs=["output_data"])
    return program_config