Get error when running cog build for resnet example

[xiaoyu-work]

I made this change locally to bypass paramspec error, but got another protobuf error:

Building Docker image from environment in cog.yaml as cog-resnet...
[+] Building 3.0s (14/14) FINISHED
 => [internal] load build definition from Dockerfile                                                               0.0s
 => => transferring dockerfile: 622B                                                                               0.0s
 => [internal] load .dockerignore                                                                                  0.0s
 => => transferring context: 2B                                                                                    0.0s
 => resolve image config for docker.io/docker/dockerfile:1.2                                                       0.1s
 => CACHED docker-image://docker.io/docker/dockerfile:1.2@sha256:e2a8561e419ab1ba6b2fe6cbdf49fd92b95912df1cf7d313  0.0s
 => [internal] load metadata for docker.io/library/python:3.8                                                      0.1s
 => [internal] load build context                                                                                  0.0s
 => => transferring context: 29.32kB                                                                               0.0s
 => [stage-0 1/6] FROM docker.io/library/python:3.8@sha256:f8dd6cc493bb667f693293f69927ae7c5ebf430a88b9d384c0c3ee  0.0s
 => CACHED [stage-0 2/6] COPY .cog/tmp/build3752096533/cog-0.0.1.dev-py3-none-any.whl /tmp/cog-0.0.1.dev-py3-none  0.0s
 => CACHED [stage-0 3/6] RUN --mount=type=cache,target=/root/.cache/pip pip install /tmp/cog-0.0.1.dev-py3-none-a  0.0s
 => CACHED [stage-0 4/6] RUN --mount=type=cache,target=/root/.cache/pip pip install   pillow==9.1.0 tensorflow==2  0.0s
 => CACHED [stage-0 5/6] WORKDIR /src                                                                              0.0s
 => [stage-0 6/6] COPY . /src                                                                                      0.1s
 => exporting to image                                                                                             0.0s
 => => exporting layers                                                                                            0.0s
 => => writing image sha256:034e64239bd4618f4272e0b719fd4263366a34a9a37eacdaf77139887c179680                       0.0s
 => => naming to docker.io/library/cog-resnet                                                                      0.0s
 => exporting cache                                                                                                0.0s
 => => preparing build cache for export                                                                            0.0s
Adding labels to image...

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/usr/local/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/usr/local/lib/python3.8/site-packages/cog/command/openapi_schema.py", line 18, in <module>
    predictor = load_predictor()
  File "/usr/local/lib/python3.8/site-packages/cog/predictor.py", line 76, in load_predictor
    spec.loader.exec_module(module)
  File "<frozen importlib._bootstrap_external>", line 843, in exec_module
  File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
  File "predict.py", line 5, in <module>
    from tensorflow.keras.applications.resnet50 import (
  File "/usr/local/lib/python3.8/site-packages/tensorflow/__init__.py", line 37, in <module>
    from tensorflow.python.tools import module_util as _module_util
  File "/usr/local/lib/python3.8/site-packages/tensorflow/python/__init__.py", line 37, in <module>
    from tensorflow.python.eager import context
  File "/usr/local/lib/python3.8/site-packages/tensorflow/python/eager/context.py", line 29, in <module>
    from tensorflow.core.framework import function_pb2
  File "/usr/local/lib/python3.8/site-packages/tensorflow/core/framework/function_pb2.py", line 16, in <module>
    from tensorflow.core.framework import attr_value_pb2 as tensorflow_dot_core_dot_framework_dot_attr__value__pb2
  File "/usr/local/lib/python3.8/site-packages/tensorflow/core/framework/attr_value_pb2.py", line 16, in <module>
    from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
  File "/usr/local/lib/python3.8/site-packages/tensorflow/core/framework/tensor_pb2.py", line 16, in <module>
    from tensorflow.core.framework import resource_handle_pb2 as tensorflow_dot_core_dot_framework_dot_resource__handle__pb2
  File "/usr/local/lib/python3.8/site-packages/tensorflow/core/framework/resource_handle_pb2.py", line 16, in <module>
    from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
  File "/usr/local/lib/python3.8/site-packages/tensorflow/core/framework/tensor_shape_pb2.py", line 36, in <module>
    _descriptor.FieldDescriptor(
  File "/usr/local/lib/python3.8/site-packages/google/protobuf/descriptor.py", line 560, in __new__
    _message.Message._CheckCalledFromGeneratedFile()
TypeError: Descriptors cannot not be created directly.
If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.
If you cannot immediately regenerate your protos, some other possible workarounds are:
 1. Downgrade the protobuf package to 3.20.x or lower.
 2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).

More information: https://developers.google.com/protocol-buffers/docs/news/2022-05-06#python-updates

ⅹ Failed to get type signature: exit status 1

I tried different versions of protobuf like 3.17, 3.20 or 4.21, and none of them worked. I also tried to set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python, still didn't work.