TypeErropr when running model_main_tf2.py

Question

when running model_main_tf2.py on visual studio code 2022, I am receiving a type error. i will leave the code i am running, and the error statement below. i am running this in Visual studio 2022, in an attempt to debug this code. I am using Tensorflow 2.8, on a conda virtual environment. I am using python 3.9, downloaded with anaconda. i get this error whether i use VS, or the command given in the setup code. Thanks in advance

Error statement:

     WARNING:tensorflow:There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce.
W0208 18:19:44.794903 31892 cross_device_ops.py:1387] There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce.
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0',)
I0208 18:19:44.806906 31892 mirrored_strategy.py:376] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0',)
Traceback (most recent call last):
  File "C:\Users\varis\anaconda3\envs\tensorflow\lib\runpy.py", line 197, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "C:\Users\varis\anaconda3\envs\tensorflow\lib\runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "c:\program files\microsoft visual studio\2022\community\common7\ide\extensions\microsoft\python\core\debugpy\__main__.py", line 45, in <module>
    cli.main()
  File "c:\program files\microsoft visual studio\2022\community\common7\ide\extensions\microsoft\python\core\debugpy/..\debugpy\server\cli.py", line 444, in main
    run()
  File "c:\program files\microsoft visual studio\2022\community\common7\ide\extensions\microsoft\python\core\debugpy/..\debugpy\server\cli.py", line 285, in run_file
    runpy.run_path(target_as_str, run_name=compat.force_str("__main__"))
  File "C:\Users\varis\anaconda3\envs\tensorflow\lib\runpy.py", line 268, in run_path
    return _run_module_code(code, init_globals, run_name,
  File "C:\Users\varis\anaconda3\envs\tensorflow\lib\runpy.py", line 97, in _run_module_code
    _run_code(code, mod_globals, init_globals,
  File "C:\Users\varis\anaconda3\envs\tensorflow\lib\runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "C:\Users\varis\Desktop\Deaf-hearing_project\RealTimeObjectDetection\Tflow\models\research\object_detection\model_main_tf2.py", line 116, in <module>
    tf.compat.v1.app.run(main=None, argv=None)
  File "C:\Users\varis\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\platform\app.py", line 40, in run
    _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
  File "C:\Users\varis\anaconda3\envs\tensorflow\lib\site-packages\absl\app.py", line 312, in run
    _run_main(main, args)
  File "C:\Users\varis\anaconda3\envs\tensorflow\lib\site-packages\absl\app.py", line 258, in _run_main
    sys.exit(main(argv))
  File "C:\Users\varis\Desktop\Deaf-hearing_project\RealTimeObjectDetection\Tflow\models\research\object_detection\model_main_tf2.py", line 107, in main
    model_lib_v2.train_loop(
  File "C:\Users\varis\AppData\Roaming\Python\Python39\site-packages\object_detection\model_lib_v2.py", line 504, in train_loop
    configs = get_configs_from_pipeline_file(
  File "C:\Users\varis\AppData\Roaming\Python\Python39\site-packages\object_detection\utils\config_util.py", line 138, in get_configs_from_pipeline_file
    proto_str = f.read()
  File "C:\Users\varis\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 118, in read
    self._preread_check()
  File "C:\Users\varis\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 80, in _preread_check
    self._read_buf = _pywrap_file_io.BufferedInputStream(
TypeError: __init__(): incompatible constructor arguments. The following argument types are supported:
    1. tensorflow.python.lib.io._pywrap_file_io.BufferedInputStream(filename: str, buffer_size: int, token: tensorflow.python.lib.io._pywrap_file_io.TransactionToken = None)

model_main_tf2.py:

# Lint as: python3
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

r"""Creates and runs TF2 object detection models.
ASasASs
For local training/evaluation run:
PIPELINE_CONFIG_PATH=path/to/pipeline.config
MODEL_DIR=/tmp/model_outputs
NUM_TRAIN_STEPS=10000
SAMPLE_1_OF_N_EVAL_EXAMPLES=1
python model_main_tf2.py -- \
  --model_dir=$MODEL_DIR --num_train_steps=$NUM_TRAIN_STEPS \
  --sample_1_of_n_eval_examples=$SAMPLE_1_OF_N_EVAL_EXAMPLES \
  --pipeline_config_path=$PIPELINE_CONFIG_PATH \
  --alsologtostderr
"""
import absl
from absl import flags
import tensorflow.compat.v2 as tf
from object_detection import model_lib_v2

flags.DEFINE_string('pipeline_config_path', None, 'Path to pipeline config '
                    'file.')
flags.DEFINE_integer('num_train_steps', None, 'Number of train steps.')
flags.DEFINE_bool('eval_on_train_data', False, 'Enable evaluating on train '
                  'data (only supported in distributed training).')
flags.DEFINE_integer('sample_1_of_n_eval_examples', None, 'Will sample one of '
                     'every n eval input examples, where n is provided.')
flags.DEFINE_integer('sample_1_of_n_eval_on_train_examples', 5, 'Will sample '
                     'one of every n train input examples for evaluation, '
                     'where n is provided. This is only used if '
                     '`eval_training_data` is True.')
flags.DEFINE_string(
    'model_dir', None, 'Path to output model directory '
                       'where event and checkpoint files will be written.')
flags.DEFINE_string(
    'checkpoint_dir', None, 'Path to directory holding a checkpoint.  If '
    '`checkpoint_dir` is provided, this binary operates in eval-only mode, '
    'writing resulting metrics to `model_dir`.')

flags.DEFINE_integer('eval_timeout', 3600, 'Number of seconds to wait for an'
                     'evaluation checkpoint before exiting.')

flags.DEFINE_bool('use_tpu', False, 'Whether the job is executing on a TPU.')
flags.DEFINE_string(
    'tpu_name',
    default=None,
    help='Name of the Cloud TPU for Cluster Resolvers.')
flags.DEFINE_integer(
    'num_workers', 1, 'When num_workers > 1, training uses '
    'MultiWorkerMirroredStrategy. When num_workers = 1 it uses '
    'MirroredStrategy.')
flags.DEFINE_integer(
    'checkpoint_every_n', 1000, 'Integer defining how often we checkpoint.')
flags.DEFINE_boolean('record_summaries', True,
                     ('Whether or not to record summaries defined by the model'
                      ' or the training pipeline. This does not impact the'
                      ' summaries of the loss values which are always'
                      ' recorded.'))

FLAGS = flags.FLAGS


def main(unused_argv):
  flags.mark_flag_as_required('model_dir')
  flags.mark_flag_as_required('pipeline_config_path')
  tf.config.set_soft_device_placement(True)

  if FLAGS.checkpoint_dir:
    model_lib_v2.eval_continuously(
        pipeline_config_path=FLAGS.pipeline_config_path,
        model_dir=FLAGS.model_dir,
        train_steps=FLAGS.num_train_steps,
        sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples,
        sample_1_of_n_eval_on_train_examples=(
            FLAGS.sample_1_of_n_eval_on_train_examples),
        checkpoint_dir=FLAGS.checkpoint_dir,
        wait_interval=300, timeout=FLAGS.eval_timeout)
  else:
    if FLAGS.use_tpu:
      # TPU is automatically inferred if tpu_name is None and
      # we are running under cloud ai-platform.
      resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
          FLAGS.tpu_name)
      tf.config.experimental_connect_to_cluster(resolver)
      tf.tpu.experimental.initialize_tpu_system(resolver)
      strategy = tf.distribute.experimental.TPUStrategy(resolver)
    elif FLAGS.num_workers > 1:
      strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
    else:
      strategy = tf.compat.v2.distribute.MirroredStrategy()

    with strategy.scope():
      model_lib_v2.train_loop(
          pipeline_config_path=FLAGS.pipeline_config_path,
          model_dir=FLAGS.model_dir,
          train_steps=FLAGS.num_train_steps,
          use_tpu=FLAGS.use_tpu,
          checkpoint_every_n=FLAGS.checkpoint_every_n,
          record_summaries=FLAGS.record_summaries)

if __name__ == '__main__':
  tf.compat.v1.app.run(main=None, argv=None)