code: def leiden(
      input_graph: Graph,
      max_iter: int = 100,
      resolution: int = 1.0,
      random_state: int = None,
      theta: int = 1.0,
  ) -> Tuple[dask_cudf.DataFrame, float]:
      """
      Compute the modularity optimizing partition of the input graph using the
      Leiden method
  
      Traag, V. A., Waltman, L., & van Eck, N. J. (2019). From Louvain to Leiden:
      guaranteeing well-connected communities. Scientific reports, 9(1), 5233.
      doi: 10.1038/s41598-019-41695-z
  
      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_iter : integer, optional (default=100)
          This controls the maximum number of levels/iterations of the Leiden
          algorithm. When specified the algorithm will terminate after no more
          than the specified number of iterations. No error occurs when the
          algorithm terminates early in this manner.
  
      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.
          Defaults to 1.
  
      random_state: int, optional(default=None)
          Random state to use when generating samples.  Optional argument,
          defaults to a hash of process id, time, and hostname.
  
      theta: float, optional (default=1.0)
          Called theta in the Leiden algorithm, this is used to scale
          modularity gain in Leiden refinement phase, to compute
          the probability of joining a random leiden community.
  
      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.leiden(dg)
  
      """
  
      if input_graph.is_directed():
          raise ValueError("input graph must be undirected")
  
      # Return a client if one has started
      client = default_client()
  
      do_expensive_check = False
  
      result = [
          client.submit(
              _call_plc_leiden,
              Comms.get_session_id(),
              input_graph._plc_graph[w],
              max_iter,
              resolution,
              random_state,
              theta,
              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/leiden.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
  from pylibcugraph import ResourceHandle
  from pylibcugraph import leiden
  import numpy
  import cupy
  from typing import Tuple, TYPE_CHECKING
label_desc: Use image node_id 3 for calling a global function with example usage: leiden(input_graph, max_iter, resolution, random_state, theta) and returns: ddf,  mod_score