How to use the geopandas.GeoDataFrame.from_records function in geopandas

def manually_compute_intersections(sources, targets):
    records = []
    for i, source in sources.geometry.items():
        for j, target in targets.geometry.items():
            intersection = source.intersection(target)
            if not intersection.is_empty:
                records.append((i, j, intersection))

    expected = (
        geopandas.GeoDataFrame.from_records(
            records, columns=["source", "target", "geometry"]
        )
        .set_index(["source", "target"])
        .geometry
    )
    return expected

def manually_compute_intersections(sources, targets):
    records = []
    for i, source in sources.geometry.items():
        for j, target in targets.geometry.items():
            intersection = source.intersection(target)
            if not intersection.is_empty:
                records.append((i, j, intersection))

    expected = (
        geopandas.GeoDataFrame.from_records(
            records, columns=["source", "target", "geometry"]
        )
        .set_index(["source", "target"])
        .geometry
    )
    return expected

def intersections(sources, targets):
    reindexed_sources = get_geometries_with_range_index(sources)
    reindexed_targets = get_geometries_with_range_index(targets)
    spatially_indexed_sources = IndexedGeometries(reindexed_sources)

    records = [
        # Flip i, j to j, i so that the index is ["source", "target"]
        (sources.index[j], targets.index[i], geometry)
        for i, j, geometry in spatially_indexed_sources.enumerate_intersections(
            reindexed_targets
        )
    ]
    df = GeoDataFrame.from_records(records, columns=["source", "target", "geometry"])
    geometries = df.set_index(["source", "target"]).geometry
    geometries.sort_index(inplace=True)
    return geometries

:param area_cutoff: (optional) if provided, only return intersections with
        area greater than ``area_cutoff``
    :type area_cutoff: Number or None
    """
    reindexed_sources = get_geometries_with_range_index(sources)
    reindexed_targets = get_geometries_with_range_index(targets)
    spatially_indexed_sources = IndexedGeometries(reindexed_sources)

    records = [
        # Flip i, j to j, i so that the index is ["source", "target"]
        (sources.index[j], targets.index[i], geometry)
        for i, j, geometry in spatially_indexed_sources.enumerate_intersections(
            reindexed_targets
        )
    ]
    df = GeoDataFrame.from_records(records, columns=["source", "target", "geometry"])
    geometries = df.set_index(["source", "target"]).geometry
    geometries.sort_index(inplace=True)
    geometries.crs = sources.crs

    if area_cutoff is not None:
        geometries = geometries[geometries.area > area_cutoff]

    return geometries

nominal intensity category based on the central pressure value.
    
    :param str trackFile: Path to a TCRM-format track file
    
    :returns: `GeoPandas.GeoDataFrame` of tracks
    """

    LOGGER.debug(f"Loading track data from {trackFile}")
    tracks = track.ncReadTrackData(trackFile)
    trackgdf = []
    for t in tracks:
        segments = []
        for n in range(len(t.data) - 1):
            segment = LineString([[t.Longitude[n], t.Latitude[n]],[t.Longitude[n+1], t.Latitude[n+1]]])
            segments.append(segment)
        gdf = gpd.GeoDataFrame.from_records(t.data[:-1])
        gdf['geometry'] = segments
        gdf['category'] = pd.cut(gdf['CentralPressure'], 
                                bins=[0, 930, 955, 970, 985, 990, 1020], 
                                labels=[5,4,3,2,1,0])
        trackgdf.append(gdf)
    trackgdf = pd.concat(trackgdf)
    return trackgdf