How to use the arcgis.geometry.Geometry function in arcgis

#data_fl = FeatureLayer(url=indicator_url, gis=self.gis_conn)

        # Enumerate Used to Leverage the Merge Method on the Data Frame.
        # Set the First and Merge the Remainder to the First.
        for idx, row in enumerate(self.grid_sdf.iterrows()):

            # Negative Buffer Used to Avoid Selecting More Than 1 Cell
            sp_filter = filters.intersects(
                Geometry(row[1].SHAPE).buffer(-.1),
                self.grid_wkid
            )

            if not indicator_url:
                df_current = SpatialDataFrame(
                    columns=field_schema.get(indicator),
                    geometry=[Geometry(json.loads(row[1].SHAPE.JSON))]
                )
                created = True

            else:
                # Negative Buffer to Select a Single Grid Cell
                sp_filter = filters.intersects(
                    Geometry(row[1].SHAPE).buffer(-.1),
                    self.grid_wkid
                )
                df_current = data_fl.query(geometry_filter=sp_filter,
                                           return_all_records=self.return_all_records).df

            # Set The First Instance
            if idx == 0:

                # Check If Cell Found in Target Indicator Layer

m1, n1, m2, n2 = box
        x, y = p
        if x >= m1 and x <= m2 and y >= n1 and y <= n2:
            return True
        return False
    return (point_in((x1, y1), b)) or (point_in((x2, y1), b)) or (point_in((x2, y2), b)) or (point_in((x1, y2), b))

# iterate through image tiles on naip_image_layer starting from bottom row 
for y_idx in range(y_num_tile):
    for x_idx in range(x_num_tile):
        image_name = str(y_idx*x_num_tile + x_idx)
        x_start = min_x + x_idx * tile_size
        y_start = min_y + y_idx * tile_size

        # export annotations for buildings in the image 
        tile_image_geometry = Geometry({
            "rings" : [[[x_start,y_start],[x_start+tile_size,y_start],[x_start+tile_size,y_start+tile_size],[x_start,y_start+tile_size]]],
            "spatialReference" : {"wkid" : crs_id}
        })
        annotations = []
        for idx, bbox in enumerate(building_data.geometry):
            # bbox is polygon
            bbox_extent = bbox.extent
            tile_extent = tile_image_geometry.extent
            try:
                # if bbox overlaps with tile image extent, record the building bbox
                if overlap(bbox_extent, tile_extent):
                    # bbox contains normalized [xywh]
                    x1_r, y1_r, x2_r, y2_r = bbox_extent
                    # clipping
                    x1 = max(x_start, x1_r)
                    y1 = max(y_start, y1_r)

def validate_img_gis(geo_gis, img_url):

    try:
        img_lyr = ImageryLayer(img_url, gis=geo_gis)
        img_lyr.get_samples(Geometry({"x":-122.435,"y":37.785}), geometry_type='esriGeometryPoint')
        print('Thematic Service GIS: {}'.format(geo_gis))

    except RuntimeError:
        raise Exception('{} Does Not Support getSamples on Service: {}'.format(geo_gis, img_url))

look_back_days = config.look_back_days

    dates = csl.get_dates_in_range(look_back_days)
    where_clause = csl.form_query_string(dates)

    grid_fl = FeatureLayer(url=config.grid_url)
    grid_sdf = grid_fl.query(return_all_records=return_all_records, where=where_clause).df

    geometry = grid_sdf.geometry
    sr = {'wkid':4326}
    sp_rel = "esriSpatialRelIntersects"

    for idx, row in enumerate(grid_sdf.iterrows()):
        geom = row[1].SHAPE

        new_geom = Geometry({
            "rings" : [[[geom.extent.upperRight.X-.1, geom.extent.lowerLeft.Y+.1], [geom.extent.lowerLeft.X+.1, geom.extent.lowerLeft.Y+.1], [geom.extent.lowerLeft.X+.1, geom.extent.upperRight.Y-.1], [geom.extent.upperRight.X-.1, geom.extent.upperRight.Y-.1], [geom.extent.upperRight.X-.1, geom.extent.lowerLeft.Y+.1]]],
            "spatialReference" : {"wkid" : 4326}
        })

        grid_filter = filters._filter(new_geom, sr, sp_rel)
        sp_filter = filters._filter(geom, sr, sp_rel)

        data_fl = FeatureLayer(url=config.features_url)
        #out_fields=in_fields,
        data_sdf = data_fl.query(geometry_filter=sp_filter,return_geometry=True,
            return_all_records=return_all_records).df

        print('Processing Completeness')
        #bounding_box = '(37.708132, -122.513617, 37.832132, -122.349607)'
        bounding_box = '(' + \
                    str(geom.extent.lowerLeft.Y) + ',' + \

created = False
        out_sdf = None

        # Indicator Feature Layer
        indicator_url = self.__getattribute__(indicator + '_url')
        print(indicator_url)
        #data_fl = FeatureLayer(url=indicator_url, gis=self.gis_conn)

        # Enumerate Used to Leverage the Merge Method on the Data Frame.
        # Set the First and Merge the Remainder to the First.
        for idx, row in enumerate(self.grid_sdf.iterrows()):

            # Negative Buffer Used to Avoid Selecting More Than 1 Cell
            sp_filter = filters.intersects(
                Geometry(row[1].SHAPE).buffer(-.1),
                self.grid_wkid
            )

            if not indicator_url:
                df_current = SpatialDataFrame(
                    columns=field_schema.get(indicator),
                    geometry=[Geometry(json.loads(row[1].SHAPE.JSON))]
                )
                created = True

            else:
                # Negative Buffer to Select a Single Grid Cell
                sp_filter = filters.intersects(
                    Geometry(row[1].SHAPE).buffer(-.1),
                    self.grid_wkid
                )

# # Push Significant Values Into List for Averaging
        # if enriched_pop or sample_total < 100:
        #     pass
        # else:
        #     diff = abs(enriched_pop - sample_total)
        #     if diff > 100:
        #         pop_diff.append(diff)
        #
        # tot_pop = enriched_pop if enriched_pop > 0 else sample_total
        # tot_pop = tot_pop if tot_pop > 0 else -1

        ##-----------------------------------------------------------------------------

        them_lyr = FeatureLayer(url=them_url, gis=them_gis)

        geom = Geometry(row[1].SHAPE).buffer(-.01)

        sp_filter = filters.intersects(geom, 4326)

        them_sdf = them_lyr.query(geometry_filter=sp_filter, return_all_records=True).df
        #print(them_sdf)


        if len(df_current) > 0:
            count = len(df_current)
            max_val = df_current[f_thm_acc].max()
            max_scale = 100 * (len(df_current[df_current[f_thm_acc] == max_val])/count)
            min_val = df_current[f_thm_acc].min()
            min_scale = 100 * (len(df_current[df_current[f_thm_acc] == min_val])/count)
            vc = df_current[f_thm_acc].value_counts()
            common = df_current[f_thm_acc].mode() # Used in MSP
            mean = df_current[f_thm_acc].mean()