prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  dask.distributed.progress no longer callable in 2.3.0?
  We've used the progress() function from dask.distributed a bunch in the past to display a progress bar in JupyterLab, but it seems to have stopped working after upgrading to Dask 2.3.0:
  
  ```
  from dask.distributed import Client, progress
  import dask.dataframe as dd
  
  df = dd.demo.make_timeseries('2010', '2016',
                               {'value': float, 'name': str, 'id': int},
                               freq='10s', partition_freq='7d', seed=1)
  
  df = df.persist()
  progress(df)
  ```
  
  Executing this in a single cell in JupyterLab (with an existing Dask cluster already running) results in:
  
  ```
  ---------------------------------------------------------------------------
  TypeError                                 Traceback (most recent call last)
  <ipython-input-1-16af814d7204> in <module>
        7 
        8 df = df.persist()
  ----> 9 progress(df)
  
  TypeError: 'module' object is not callable
  ```
  
  Let me know if I can provide any more info. Thanks!
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `distributed/__init__.py`
  Content:
  ```
  1 from . import config
  2 from dask.config import config
  3 from .actor import Actor, ActorFuture
  4 from .core import connect, rpc
  5 from .deploy import LocalCluster, Adaptive, SpecCluster
  6 from .diagnostics import progress
  7 from .client import (
  8     Client,
  9     Executor,
  10     CompatibleExecutor,
  11     wait,
  12     as_completed,
  13     default_client,
  14     fire_and_forget,
  15     Future,
  16     futures_of,
  17     get_task_stream,
  18 )
  19 from .lock import Lock
  20 from .nanny import Nanny
  21 from .pubsub import Pub, Sub
  22 from .queues import Queue
  23 from .scheduler import Scheduler
  24 from .threadpoolexecutor import rejoin
  25 from .utils import sync
  26 from .variable import Variable
  27 from .worker import Worker, get_worker, get_client, secede, Reschedule
  28 from .worker_client import local_client, worker_client
  29 
  30 from tornado.gen import TimeoutError
  31 
  32 from ._version import get_versions
  33 
  34 versions = get_versions()
  35 __version__ = versions["version"]
  36 __git_revision__ = versions["full-revisionid"]
  37 del get_versions, versions
  38
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/distributed/__init__.py b/distributed/__init__.py
  --- a/distributed/__init__.py
  +++ b/distributed/__init__.py
  @@ -3,7 +3,7 @@
   from .actor import Actor, ActorFuture
   from .core import connect, rpc
   from .deploy import LocalCluster, Adaptive, SpecCluster
  -from .diagnostics import progress
  +from .diagnostics.progressbar import progress
   from .client import (
       Client,
       Executor,
  
verification_info: {"golden_diff": "diff --git a/distributed/__init__.py b/distributed/__init__.py\n--- a/distributed/__init__.py\n+++ b/distributed/__init__.py\n@@ -3,7 +3,7 @@\n from .actor import Actor, ActorFuture\n from .core import connect, rpc\n from .deploy import LocalCluster, Adaptive, SpecCluster\n-from .diagnostics import progress\n+from .diagnostics.progressbar import progress\n from .client import (\n     Client,\n     Executor,\n", "issue": "dask.distributed.progress no longer callable in 2.3.0?\nWe've used the progress() function from dask.distributed a bunch in the past to display a progress bar in JupyterLab, but it seems to have stopped working after upgrading to Dask 2.3.0:\r\n\r\n```\r\nfrom dask.distributed import Client, progress\r\nimport dask.dataframe as dd\r\n\r\ndf = dd.demo.make_timeseries('2010', '2016',\r\n                             {'value': float, 'name': str, 'id': int},\r\n                             freq='10s', partition_freq='7d', seed=1)\r\n\r\ndf = df.persist()\r\nprogress(df)\r\n```\r\n\r\nExecuting this in a single cell in JupyterLab (with an existing Dask cluster already running) results in:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-1-16af814d7204> in <module>\r\n      7 \r\n      8 df = df.persist()\r\n----> 9 progress(df)\r\n\r\nTypeError: 'module' object is not callable\r\n```\r\n\r\nLet me know if I can provide any more info. Thanks!\n", "before_files": [{"content": "from . import config\nfrom dask.config import config\nfrom .actor import Actor, ActorFuture\nfrom .core import connect, rpc\nfrom .deploy import LocalCluster, Adaptive, SpecCluster\nfrom .diagnostics import progress\nfrom .client import (\n    Client,\n    Executor,\n    CompatibleExecutor,\n    wait,\n    as_completed,\n    default_client,\n    fire_and_forget,\n    Future,\n    futures_of,\n    get_task_stream,\n)\nfrom .lock import Lock\nfrom .nanny import Nanny\nfrom .pubsub import Pub, Sub\nfrom .queues import Queue\nfrom .scheduler import Scheduler\nfrom .threadpoolexecutor import rejoin\nfrom .utils import sync\nfrom .variable import Variable\nfrom .worker import Worker, get_worker, get_client, secede, Reschedule\nfrom .worker_client import local_client, worker_client\n\nfrom tornado.gen import TimeoutError\n\nfrom ._version import get_versions\n\nversions = get_versions()\n__version__ = versions[\"version\"]\n__git_revision__ = versions[\"full-revisionid\"]\ndel get_versions, versions\n", "path": "distributed/__init__.py"}], "after_files": [{"content": "from . import config\nfrom dask.config import config\nfrom .actor import Actor, ActorFuture\nfrom .core import connect, rpc\nfrom .deploy import LocalCluster, Adaptive, SpecCluster\nfrom .diagnostics.progressbar import progress\nfrom .client import (\n    Client,\n    Executor,\n    CompatibleExecutor,\n    wait,\n    as_completed,\n    default_client,\n    fire_and_forget,\n    Future,\n    futures_of,\n    get_task_stream,\n)\nfrom .lock import Lock\nfrom .nanny import Nanny\nfrom .pubsub import Pub, Sub\nfrom .queues import Queue\nfrom .scheduler import Scheduler\nfrom .threadpoolexecutor import rejoin\nfrom .utils import sync\nfrom .variable import Variable\nfrom .worker import Worker, get_worker, get_client, secede, Reschedule\nfrom .worker_client import local_client, worker_client\n\nfrom tornado.gen import TimeoutError\n\nfrom ._version import get_versions\n\nversions = get_versions()\n__version__ = versions[\"version\"]\n__git_revision__ = versions[\"full-revisionid\"]\ndel get_versions, versions\n", "path": "distributed/__init__.py"}]}