Fix bug about criteo download notebook (#1453)

* API Overhaul

First draft of the API overhauls changes. Adds most core functionality, including
defining workflow graphs with a ColumnGroup class, the workflow and dataset changes
, most operators converted to use the new api, etc.

* remove debug print statement

* Fix test_io unittest

Also partially fix some tests inside test_workflow

* Handle multi-column joint/combo categorify

* Update JoinGroupby

* Fix differencelag

* add dependencies method (#498)

* Convert TargetEncoding op

* Update nvtabular/workflow.py

Co-authored-by: Richard (Rick) Zamora <rzamora217@gmail.com>

* Update nvtabular/workflow.py

Co-authored-by: Richard (Rick) Zamora <rzamora217@gmail.com>

* Remove workflow code from dataloaders

We should be doing online transforms like
```KerasSequenceLoader(workflow.transform(dataset), ...```  instead of
```KerasSequenceLoader(dataset, workflows=[workflow], ...``` now

* Unittest ops + bugfix in Bucketize (#496)

* test_minmix

* updates test

* unittest ops

* First draft get_embedding_sizes support

Re-add get_embedding_sizes . Note that this changes how we support multi-hot columns here
(sizes are returned same as single hot, and we don't use this method to distinguish between
multi and singlehot columns)

* isort

* Remove groupbystatistics

* implement serialization of statistics

add save_stats/load_stats/clear_stats methods to the workflow, with each statoperator getting
called as appropiate

* Fix TF dataloader unittests

* test_torch_dataloader fixes

* doc strings

* update download

Co-authored-by: Ben Frederickson <github@benfrederickson.com>
Co-authored-by: rnyak <ronayak@hotmail.com>
Co-authored-by: Richard (Rick) Zamora <rzamora217@gmail.com>
Co-authored-by: root <root@dgx06.aselab.nvidia.com>
Co-authored-by: Julio Perez <37191411+jperez999@users.noreply.github.com>
Co-authored-by: Karl Higley <kmhigley@gmail.com>

examples/scaling-criteo/01-Download-Convert.ipynb

@@ -232,14 +232,15 @@
                 "    dtypes[x] = \"hex\"\n",
                 "\n",
                 "# Create an NVTabular Dataset from a CSV-file glob\n",
-                "file_list = glob.glob(os.path.join(INPUT_PATH, \"day_*\"))\n",
+                "file_list = glob.glob(os.path.join(INPUT_PATH, \"day_*[!.gz]\"))\n",
                 "dataset = nvt.Dataset(\n",
                 "    file_list,\n",
                 "    engine=\"csv\",\n",
                 "    names=cols,\n",
                 "    part_mem_fraction=frac_size,\n",
                 "    sep=\"\\t\",\n",
                 "    dtypes=dtypes,\n",
+                "    client=client,\n",
                 ")"
             ]
         },
@@ -300,4 +301,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 4
-}
+}