How to use the pandera.dtypes function in pandera

for dtype in [
            dtypes.Int,
            dtypes.Int8,
            dtypes.Int16,
            dtypes.Int32,
            dtypes.Int64]:
        schema = DataFrameSchema({"col": Column(dtype, nullable=False)})
        validated_df = schema.validate(
            pd.DataFrame(
                {"col": [-712, -4, -321, 0, 1, 777, 5, 123, 9000]},
                dtype=dtype.value))
        assert isinstance(validated_df, pd.DataFrame)

    for dtype in [
            dtypes.UInt8,
            dtypes.UInt16,
            dtypes.UInt32,
            dtypes.UInt64]:
        schema = DataFrameSchema({"col": Column(dtype, nullable=False)})
        validated_df = schema.validate(
            pd.DataFrame(
                {"col": [1, 777, 5, 123, 9000]}, dtype=dtype.value))
        assert isinstance(validated_df, pd.DataFrame)

def test_numeric_dtypes():
    for dtype in [
            dtypes.Float,
            dtypes.Float16,
            dtypes.Float32,
            dtypes.Float64]:
        schema = DataFrameSchema({"col": Column(dtype, nullable=False)})
        validated_df = schema.validate(
            pd.DataFrame(
                {"col": [-123.1, -7654.321, 1.0, 1.1, 1199.51, 5.1, 4.6]},
                dtype=dtype.value))
        assert isinstance(validated_df, pd.DataFrame)

    for dtype in [
            dtypes.Int,
            dtypes.Int8,
            dtypes.Int16,
            dtypes.Int32,
            dtypes.Int64]:

def test_dtypes():
    for dtype in [
            dtypes.Float,
            dtypes.Float16,
            dtypes.Float32,
            dtypes.Float64]:
        schema = DataFrameSchema({"col": Column(dtype, nullable=False)})
        validated_df = schema.validate(
            pd.DataFrame(
                {"col": [-123.1, -7654.321, 1.0, 1.1, 1199.51, 5.1, 4.6]},
                dtype=dtype.value))
        assert isinstance(validated_df, pd.DataFrame)

    for dtype in [
            dtypes.Int,
            dtypes.Int8,
            dtypes.Int16,
            dtypes.Int32,
            dtypes.Int64]:
        schema = DataFrameSchema({"col": Column(dtype, nullable=False)})
        validated_df = schema.validate(
            pd.DataFrame(

def test_dtypes():
    for dtype in [
            dtypes.Float,
            dtypes.Float16,
            dtypes.Float32,
            dtypes.Float64]:
        schema = DataFrameSchema({"col": Column(dtype, nullable=False)})
        validated_df = schema.validate(
            pd.DataFrame(
                {"col": [-123.1, -7654.321, 1.0, 1.1, 1199.51, 5.1, 4.6]},
                dtype=dtype.value))
        assert isinstance(validated_df, pd.DataFrame)

    for dtype in [
            dtypes.Int,
            dtypes.Int8,
            dtypes.Int16,
            dtypes.Int32,
            dtypes.Int64]:

def coerce_dtype(self, series: pd.Series) -> pd.Series:
        """Coerce the type of a pd.Series by the type specified in the Column
            object's self._pandas_dtype

        :param pd.Series series: One-dimensional ndarray with axis labels
            (including time series).
        :returns: ``Series`` with coerced data type

        """
        _dtype = str if self._pandas_dtype is dtypes.PandasDtype.String \
            else self._pandas_dtype.value
        if _dtype is str:
            # only coerce non-null elements to string
            _series = series.copy()
            _series[series.notna()] = _series[series.notna()].astype(str)
            return _series
        return series.astype(_dtype)