If you just take the simplest example like this one,
@tf.function(
    input_signature=(
        tf.TensorSpec(shape=(M, K), dtype=tf.float32),
        tf.TensorSpec(shape=(K, N), dtype=tf.float32),
    )
)
def matmul(lhs, rhs):
    return tf.matmul(lhs, rhs)

and use tensorflow.python.pywrap_mlir to do a:
 import_function(
        func.get_concrete_function(), pass_pipeline="", show_debug_info=False)

the MLIR you get won’t have a “main” but just something like matmul in its name. Most of the tensorflow MLIR transforms would just end up being no-ops on them.