Makes copy of input ddf to work around dropped column names (#3776)

When creating multiple graphs with the same dask_cudf dataframe, there is a metadata mismatch occurring when one or more partitions are empty. In fact, during the second graph creation with the dask_cudf dataframe that was used/modified earlier, the metadata are not conserved for partitions with empty empty dataframes. This is due to the fact a _reference_ to the input dataframe partly destroyed (modfied) during the first graph creation is reused in the second graph creation.

This PR makes a copy of the input dataframe right after the repartition call to avoid that alteration.

Authors:
   - jnke2016 ([email protected])

Approvers:
   - Vibhu Jawa (https://github.com/VibhuJawa)
   - Alex Barghi (https://github.com/alexbarghi-nv)
   - Rick Ratzel (https://github.com/rlratzel)

python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py

@@ -182,6 +182,8 @@ def __from_edgelist(
         workers = _client.scheduler_info()["workers"]
         # Repartition to 2 partitions per GPU for memory efficient process
         input_ddf = input_ddf.repartition(npartitions=len(workers) * 2)
+        # FIXME: Make a copy of the input ddf before implicitly altering it.
+        input_ddf = input_ddf.map_partitions(lambda df: df.copy())
         # The dataframe will be symmetrized iff the graph is undirected
         # otherwise, the inital dataframe will be returned
         if edge_attr is not None:
@@ -318,7 +320,6 @@ def __from_edgelist(
             is_symmetric=not self.properties.directed,
         )
         ddf = ddf.repartition(npartitions=len(workers) * 2)
-        ddf = ddf.map_partitions(lambda df: df.copy())
         ddf = persist_dask_df_equal_parts_per_worker(ddf, _client)
         num_edges = len(ddf)
         ddf = get_persisted_df_worker_map(ddf, _client)