How to use the pdpipe.exceptions.PipelineApplicationError function in pdpipe

def test_scale_app_exception():
    df1 = _some_df1()
    scale_stage = Scale(
        "StandardScaler", exclude_columns=[], exclude_object_columns=False
    )
    with pytest.raises(PipelineApplicationError):
        scale_stage(df1)

    df2 = _some_df2()
    res_df = scale_stage(df2)
    assert "ph" in res_df.columns
    assert "gt" in res_df.columns

    # test transform exception
    with pytest.raises(PipelineApplicationError):
        scale_stage(df1)

def test_colbyframefunc_error():
    df = _some_df()
    cbf_stage = ColByFrameFunc('A==B', _are_a_c_equal)
    with pytest.raises(PipelineApplicationError):
        cbf_stage(df)

def test_scale_app_exception():
    df1 = _some_df1()
    scale_stage = Scale(
        "StandardScaler", exclude_columns=[], exclude_object_columns=False
    )
    with pytest.raises(PipelineApplicationError):
        scale_stage(df1)

    df2 = _some_df2()
    res_df = scale_stage(df2)
    assert "ph" in res_df.columns
    assert "gt" in res_df.columns

    # test transform exception
    with pytest.raises(PipelineApplicationError):
        scale_stage(df1)

if cols_to_exclude:
            excluded = df[cols_to_exclude]
            apply_to = df[
                [col for col in df.columns if col not in cols_to_exclude]
            ]
        else:
            apply_to = df
        self._scaler = scaler_by_params(self.scaler, **self._kwargs)
        try:
            res = pd.DataFrame(
                data=self._scaler.fit_transform(apply_to),
                index=apply_to.index,
                columns=apply_to.columns,
            )
        except Exception:
            raise PipelineApplicationError(
                "Exception raised when Scale applied to columns {}".format(
                    apply_to.columns
                )
            )
        if cols_to_exclude:
            res = pd.concat([res, excluded], axis=1)
            res = res[self._col_order]
        self.is_fitted = True
        return res

def _transform(self, df, verbose):
        inter_df = df
        try:
            new_col = self._func(df)
        except Exception:
            raise PipelineApplicationError(
                "Exception raised applying function{} to dataframe.".format(
                    self._func_desc
                )
            )
        if self._follow_column:
            loc = df.columns.get_loc(self._follow_column) + 1
        else:
            loc = len(df.columns)
        inter_df = out_of_place_col_insert(
            df=inter_df, series=new_col, loc=loc, column_name=self._column
        )
        return inter_df

self._col_order = list(df.columns)
        if cols_to_exclude:
            excluded = df[cols_to_exclude]
            apply_to = df[
                [col for col in df.columns if col not in cols_to_exclude]
            ]
        else:
            apply_to = df
        try:
            res = pd.DataFrame(
                data=self._scaler.transform(apply_to),
                index=apply_to.index,
                columns=apply_to.columns,
            )
        except Exception:
            raise PipelineApplicationError(
                "Exception raised when Scale applied to columns {}".format(
                    apply_to.columns
                )
            )
        if cols_to_exclude:
            res = pd.concat([res, excluded], axis=1)
            res = res[self._col_order]
        return res