How to use the feast.feature.Feature function in feast

entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(gcs_fs1)

    proc_time_fs = FeatureSet(
            "processing_time",
            features=[Feature("feature_value3", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(proc_time_fs)

    add_cols_fs = FeatureSet(
            "additional_columns",
            features=[Feature("feature_value4", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(add_cols_fs)

    historical_fs = FeatureSet(
            "historical",
            features=[Feature("feature_value5", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(historical_fs)

    fs1 = FeatureSet(
            "feature_set_1",
            features=[Feature("feature_value6", ValueType.STRING)],

features=[Feature("feature_value5", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(historical_fs)

    fs1 = FeatureSet(
            "feature_set_1",
            features=[Feature("feature_value6", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )

    fs2 = FeatureSet(
        "feature_set_2",
        features=[Feature("other_feature_value7", ValueType.INT64)],
        entities=[Entity("other_entity_id", ValueType.INT64)],
        max_age=Duration(seconds=100),
    )
    client.apply(fs1)
    client.apply(fs2)

entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(file_fs1)

    gcs_fs1 = FeatureSet(
            "gcs_feature_set",
            features=[Feature("feature_value2", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(gcs_fs1)

    proc_time_fs = FeatureSet(
            "processing_time",
            features=[Feature("feature_value3", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(proc_time_fs)

    add_cols_fs = FeatureSet(
            "additional_columns",
            features=[Feature("feature_value4", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(add_cols_fs)

    historical_fs = FeatureSet(
            "historical",
            features=[Feature("feature_value5", ValueType.STRING)],

entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(add_cols_fs)

    historical_fs = FeatureSet(
            "historical",
            features=[Feature("feature_value5", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(historical_fs)

    fs1 = FeatureSet(
            "feature_set_1",
            features=[Feature("feature_value6", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )

    fs2 = FeatureSet(
        "feature_set_2",
        features=[Feature("other_feature_value7", ValueType.INT64)],
        entities=[Entity("other_entity_id", ValueType.INT64)],
        max_age=Duration(seconds=100),
    )
    client.apply(fs1)
    client.apply(fs2)

def test_apply_all_featuresets(client):
    client.set_project(PROJECT_NAME)

    file_fs1 = FeatureSet(
            "file_feature_set",
            features=[Feature("feature_value1", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(file_fs1)

    gcs_fs1 = FeatureSet(
            "gcs_feature_set",
            features=[Feature("feature_value2", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(gcs_fs1)

    proc_time_fs = FeatureSet(
            "processing_time",
            features=[Feature("feature_value3", ValueType.STRING)],

def test_apply_all_featuresets(client):
    client.set_project(PROJECT_NAME)

    file_fs1 = FeatureSet(
            "file_feature_set",
            features=[Feature("feature_value1", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(file_fs1)

    gcs_fs1 = FeatureSet(
            "gcs_feature_set",
            features=[Feature("feature_value2", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(gcs_fs1)

    proc_time_fs = FeatureSet(
            "processing_time",
            features=[Feature("feature_value3", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(proc_time_fs)

    add_cols_fs = FeatureSet(
            "additional_columns",
            features=[Feature("feature_value4", ValueType.STRING)],

entities=[Entity(name="user_id", dtype=ValueType.INT64)],
        features=[
            Feature(name="float_feature", dtype=ValueType.FLOAT),
            Feature(name="int64_feature", dtype=ValueType.INT64),
            Feature(name="int32_feature", dtype=ValueType.INT32),
            Feature(name="string_feature", dtype=ValueType.STRING),
            Feature(name="bytes_feature", dtype=ValueType.BYTES),
            Feature(name="bool_feature", dtype=ValueType.BOOL),
            Feature(name="double_feature", dtype=ValueType.DOUBLE),
            Feature(name="double_list_feature", dtype=ValueType.DOUBLE_LIST),
            Feature(name="float_list_feature", dtype=ValueType.FLOAT_LIST),
            Feature(name="int64_list_feature", dtype=ValueType.INT64_LIST),
            Feature(name="int32_list_feature", dtype=ValueType.INT32_LIST),
            Feature(name="string_list_feature",
                    dtype=ValueType.STRING_LIST),
            Feature(name="bytes_list_feature", dtype=ValueType.BYTES_LIST),
        ],
        max_age=Duration(seconds=3600),
    )

    # Register feature set
    client.apply(all_types_fs_expected)

    # Feast Core needs some time to fully commit the FeatureSet applied
    # when there is no existing job yet for the Featureset
    time.sleep(15)

    all_types_fs_actual = client.get_feature_set(name="all_types")

    assert all_types_fs_actual == all_types_fs_expected

    if all_types_fs_actual is None:

def features(self) -> List[Feature]:
        """
        Returns a list of features from this feature set
        """
        return [field for field in self._fields.values() if isinstance(field, Feature)]

def features(self, features: List[Feature]):
        """
        Sets the active features within this feature set

        Args:
            features: List of feature objects
        """
        for feature in features:
            if not isinstance(feature, Feature):
                raise Exception("object type is not a Feature: " + str(type(feature)))

        for key in list(self._fields.keys()):
            if isinstance(self._fields[key], Feature):
                del self._fields[key]

        if features is not None:
            self._add_fields(features)

# Only overwrite conflicting fields if replacement is allowed
            if column in new_fields:
                if (
                    isinstance(self._fields[column], Feature)
                    and not replace_existing_features
                ):
                    continue

                if (
                    isinstance(self._fields[column], Entity)
                    and not replace_existing_entities
                ):
                    continue

            # Store this field as a feature
            new_fields[column] = Feature(
                name=column,
                dtype=_infer_pd_column_type(column, df[column], rows_to_sample),
            )

            output_log += f"{type(new_fields[column]).__name__} {new_fields[column].name} ({new_fields[column].dtype}) added from dataframe.\n"

        # Discard unused fields from feature set
        if discard_unused_fields:
            keys_to_remove = []
            for key in new_fields.keys():
                if not (key in df.columns or key in provided_fields.keys()):
                    output_log += f"{type(new_fields[key]).__name__} {new_fields[key].name} ({new_fields[key].dtype}) removed because it is unused.\n"
                    keys_to_remove.append(key)
            for key in keys_to_remove:
                del new_fields[key]