code: def louvain(
      input_graph: Graph,
      max_level: int = None,
      max_iter: int = None,
      resolution: float = 1.0,
      threshold: float = 1e-7,
  ) -> Tuple[dask_cudf.DataFrame, float]:
      """
      Compute the modularity optimizing partition of the input graph using the
      Louvain method
  
      It uses the Louvain method described in:
  
      VD Blondel, J-L Guillaume, R Lambiotte and E Lefebvre: Fast unfolding of
      community hierarchies in large networks, J Stat Mech P10008 (2008),
      http://arxiv.org/abs/0803.0476
  
      Parameters
      ----------
      G : cugraph.Graph
          The graph descriptor should contain the connectivity information
          and weights. The adjacency list will be computed if not already
          present.
          The current implementation only supports undirected graphs.
  
      max_level : integer, optional (default=100)
          This controls the maximum number of levels of the Louvain
          algorithm. When specified the algorithm will terminate after no more
          than the specified number of levels. No error occurs when the
          algorithm terminates early in this manner.
  
      max_iter : integer, optional (default=None)
          This parameter is deprecated in favor of max_level.  Previously
          it was used to control the maximum number of levels of the Louvain
          algorithm.
  
      resolution: float, optional (default=1.0)
          Called gamma in the modularity formula, this changes the size
          of the communities.  Higher resolutions lead to more smaller
          communities, lower resolutions lead to fewer larger communities.
  
      threshold: float, optional (default=1e-7)
          Modularity gain threshold for each level. If the gain of
          modularity between 2 levels of the algorithm is less than the
          given threshold then the algorithm stops and returns the
          resulting communities.
  
      Returns
      -------
      parts : dask_cudf.DataFrame
          GPU data frame of size V containing two columns the vertex id and the
          partition id it is assigned to.
  
          ddf['vertex'] : cudf.Series
              Contains the vertex identifiers
          ddf['partition'] : cudf.Series
              Contains the partition assigned to the vertices
  
      modularity_score : float
          a floating point number containing the global modularity score of the
          partitioning.
  
      Examples
      --------
      >>> import cugraph.dask as dcg
      >>> import dask_cudf
      >>> # ... Init a DASK Cluster
      >>> #    see https://docs.rapids.ai/api/cugraph/stable/dask-cugraph.html
      >>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/..
      >>> chunksize = dcg.get_chunksize(datasets_path / "karate.csv")
      >>> ddf = dask_cudf.read_csv(datasets_path / "karate.csv",
      ...                          chunksize=chunksize, delimiter=" ",
      ...                          names=["src", "dst", "value"],
      ...                          dtype=["int32", "int32", "float32"])
      >>> dg = cugraph.Graph()
      >>> dg.from_dask_cudf_edgelist(ddf, source='src', destination='dst')
      >>> parts, modularity_score = dcg.louvain(dg)
  
      """
  
      if input_graph.is_directed():
          raise ValueError("input graph must be undirected")
  
      # FIXME: This max_iter logic and the max_level defaulting can be deleted
      #        in favor of defaulting max_level in call once max_iter is deleted
      if max_iter:
          if max_level:
              raise ValueError(
                  "max_iter is deprecated.  Cannot specify both max_iter and max_level"
              )
  
          warning_msg = (
              "max_iter has been renamed max_level.  Use of max_iter is "
              "deprecated and will no longer be supported in the next releases. "
          )
          warnings.warn(warning_msg, FutureWarning)
          max_level = max_iter
  
      if max_level is None:
          max_level = 100
  
      # Initialize dask client
      client = default_client()
  
      do_expensive_check = False
  
      result = [
          client.submit(
              _call_plc_louvain,
              Comms.get_session_id(),
              input_graph._plc_graph[w],
              max_level,
              threshold,
              resolution,
              do_expensive_check,
              workers=[w],
              allow_other_workers=False,
          )
          for w in Comms.get_workers()
      ]
  
      wait(result)
  
      part_mod_score = [
          client.submit(convert_to_cudf, r) for r in result
      ]
      wait(part_mod_score)
  
      vertex_dtype = input_graph.edgelist.edgelist_df.dtypes[0]
      empty_df = cudf.DataFrame(
          {
              "vertex": numpy.empty(shape=0, dtype=vertex_dtype),
              "partition": numpy.empty(shape=0, dtype="int32"),
          }
      )
  
      part_mod_score = [
          delayed(lambda x: x, nout=2)(r) for r in part_mod_score
      ]
  
      ddf = dask_cudf.from_delayed(
          [r[0] for r in part_mod_score],
          meta=empty_df,
          verify_meta=False,
      ).persist()
  
      mod_score = dask.array.from_delayed(
          part_mod_score[0][1], shape=(1,), dtype=float
      ).compute()
  
      wait(ddf)
      wait(mod_score)
  
      wait([r.release() for r in part_mod_score])
  
      if input_graph.renumbered:
          ddf = input_graph.unrenumber(ddf, "vertex")
  
      return ddf, mod_score
  
file_path: cugraph/python/cugraph/cugraph/dask/community/louvain.py
imports: from __future__ import annotations
  from dask.distributed import wait, default_client
  import cugraph.dask.comms.comms
  import dask_cudf
  import dask
  from dask import delayed
  import cudf
  import cupy
  import numpy
  from pylibcugraph import ResourceHandle
  from pylibcugraph import louvain
  from typing import Tuple, TYPE_CHECKING
  import warnings
label_desc: Use image node_id 3 for calling a global function with example usage: louvain(input_graph, max_level, max_iter, resolution, threshold) and returns: ddf,  mod_score