def METHOD_NAME():
    steps_ref = 100
    prune_target_ref = 0.1
    train_optimizer = partial(optim.Adam)
    model = PruningTestModel()
    config = create_default_legr_config()
    config["compression"]["params"]["legr_params"] = {}
    config["compression"]["params"]["legr_params"]["train_steps"] = steps_ref
    config["compression"]["params"]["legr_params"]["max_pruning"] = prune_target_ref
    train_loader = create_ones_mock_dataloader(config)
    val_loader = create_ones_mock_dataloader(config)
    train_steps_fn = lambda *x: None
    validate_fn = lambda *x: (0, 0)
    nncf_config = register_default_init_args(
        config,
        train_loader=train_loader,
        train_steps_fn=train_steps_fn,
        val_loader=val_loader,
        validate_fn=validate_fn,
        legr_train_optimizer=train_optimizer,
    )
    _, compression_ctrl = create_compressed_model_and_algo_for_test(model, nncf_config)
    evolution_env = compression_ctrl.legr.env
    assert evolution_env.prune_target == prune_target_ref
    assert evolution_env.steps == steps_ref
    assert evolution_env.train_optimizer == train_optimizer