def METHOD_NAME(self, imported_module: torch_mlir.ir.Module, pipeline=None,
            verbose=False, segment_offset=None, segment_size=None):
    """Compiles an imported module, with a flat list of functions.
    The module is expected to be in linalg-on-tensors + scalar code form.
    Args:
      imported_module: The MLIR module consisting of funcs in the torch
        dialect.
      pipeline: The custom lowering pipeline to use for lowering. First
        `air.compiler.util.LINALG_TENSOR_TO_MEMREF_PIPELINE` is applied,
        then `pipeline`.
        The default is `air.backend.linalg_on_tensors.LINALG_MEMREF_TO_AIR_PIPELINE`
      segment_offset: default location for generated segments as [colOffset, rowOffset]
      segment_size: default size for generated segments as [numCols, numRows]
    Returns:
      An opaque, backend specific compiled artifact object that can be
      passed to `load`.
    """
    if segment_offset is None:
        segment_offset = [7, 2]
    if segment_size is None:
        segment_size = [10, 6]
    if pipeline is None:
        pipeline = LINALG_MEMREF_TO_AIR_PIPELINE
    if type(imported_module) is torch_mlir.ir.Module:
        with imported_module.context:
            pm = torch_mlir.passmanager.PassManager.parse('builtin.module(refback-mlprogram-bufferize)')
            pm.run(imported_module)
    with air.mlir.ir.Context():
        air_module = air.mlir.ir.Module.parse(str(imported_module))
        pm = air.mlir.passmanager.PassManager.parse(
            air.compiler.util.LINALG_TENSOR_TO_MEMREF_PIPELINE)
        if verbose:
            print("Running MLIR pass pipeline: ",
                  air.compiler.util.LINALG_TENSOR_TO_MEMREF_PIPELINE)
        pm.run(air_module)
        if verbose:
            print("Running MLIR pass pipeline: ", pipeline)
        pm = air.mlir.passmanager.PassManager.parse(pipeline)
        pm.run(air_module)
        if verbose:
            print("AIR Module:")
            print(air_module)
        aircc_options = ['torch.mlir', '--shared', '-o', 'torch.mlir.so']
        aircc_options = aircc_options + \
                         [f"-row-offset={segment_offset[1]}",
                          f"-col-offset={segment_offset[0]}"]
        aircc_options = aircc_options + \
                         [f"-num-rows={segment_size[1]}",
                          f"-num-cols={segment_size[0]}"]
        if verbose:
            aircc_options = aircc_options + ['-v']
        aircc.run(air_module,aircc_options)
        with open("air_project/refback.torch.mlir") as f:
            imported_module = torch_mlir.ir.Module.parse(f.read(),imported_module.context)
    return self.refbackend.METHOD_NAME(imported_module)