We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Retraining Onsets and Frames Drums model with E-GMD dataset
Hello,

I am trying to retrain OaF model with the E-GMD dataset for drums transcription. I first downloaded the E-GMD dataset which has its corresponding csv file and a directoy for each drummer and subdirectories with the sessions.

I am trying to do the first step following the code in ```onsets_frames_transcription_create_tfrecords``` which I found that it is:

```
onsets_frames_transcription_create_tfrecords \
  --csv=".../e-gmd-v1.0.0/e-gmd-v1.0.0.csv" \
  --output_directory=".../e-gmd-v1.0.0" \
  --num_shards="0" \
  --wav_dir=".../e-gmd-v1.0.0" \
  --midi_dir=".../e-gmd-v1.0.0" \
  --expected_splits="train,validation,test"
```

But i got the following error which I don't know where does it come from:

```
2020-08-05 17:23:45.289023: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2020-08-05 17:23:45.289348: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
WARNING:tensorflow:From c:\users\carlos\anaconda3\lib\site-packages\tensorflow\python\compat\v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term
WARNING:tensorflow:From c:\users\carlos\anaconda3\lib\site-packages\tensorflow\python\compat\v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term
Traceback (most recent call last):
  File "c:\users\carlos\anaconda3\lib\runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "c:\users\carlos\anaconda3\lib\runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "C:\Users\Carlos\Anaconda3\Scripts\onsets_frames_transcription_create_tfrecords.exe\__main__.py", line 4, in <module>
ImportError: cannot import name 'console_entry_point'
```
I don't know if I have to change the paths of the wav and MIDI files in order to have the wav files in a directory and the MIDI files in other directory or the error comes from installation issues, versions, etc.

I am using Winows 10.

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `magenta/models/onsets_frames_transcription/onsets_frames_transcription_create_tfrecords.py`
Content:
```
1 # Copyright 2020 The Magenta Authors.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
4 # you may not use this file except in compliance with the License.
5 # You may obtain a copy of the License at
6 #
7 #     http://www.apache.org/licenses/LICENSE-2.0
8 #
9 # Unless required by applicable law or agreed to in writing, software
10 # distributed under the License is distributed on an "AS IS" BASIS,
11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
14 
15 # Lint as: python3
16 r"""Beam job for creating tfrecord files from datasets.
17 
18 Expects a CSV with the following fields: audio_filename, midi_filename, split
19 
20 Usage:
21 onsets_frames_transcription_create_tfrecords \
22   --csv="/path/to/dataset.csv" \
23   --output_directory="/path/to/output" \
24   --num_shards="0" \
25   --wav_dir="/path/to/dataset/audio" \
26   --midi_dir="/path/to/dataset/midi" \
27   --expected_splits="train,validation,test"
28 
29 """
30 
31 import collections
32 import copy
33 import csv
34 import os
35 
36 from absl import app
37 from absl import flags
38 from absl import logging
39 
40 import apache_beam as beam
41 from apache_beam.metrics import Metrics
42 from magenta.models.onsets_frames_transcription import audio_label_data_utils
43 from note_seq import midi_io
44 from note_seq.protobuf import music_pb2
45 import tensorflow.compat.v1 as tf
46 
47 tf.disable_v2_behavior()
48 
49 FLAGS = flags.FLAGS
50 
51 flags.DEFINE_string('csv', None, 'Path to dataset CSV')
52 flags.DEFINE_string('output_directory', None, 'Path to output_directory')
53 flags.DEFINE_string('wav_dir', None, 'Directory for wav files.')
54 flags.DEFINE_string('midi_dir', None, 'Directory for midi files.')
55 flags.DEFINE_integer('num_shards', 0, 'number of output shards')
56 flags.DEFINE_string('expected_splits', 'train,validation,test',
57                     'Comma separated list of expected splits.')
58 flags.DEFINE_boolean(
59     'add_wav_glob', False,
60     'If true, will add * to end of wav paths and use all matching files.')
61 flags.DEFINE_list(
62     'pipeline_options', '--runner=DirectRunner',
63     'A comma-separated list of command line arguments to be used as options '
64     'for the Beam Pipeline.')
65 
66 
67 class CreateExampleDoFn(beam.DoFn):
68   """Splits wav and midi files for the dataset."""
69 
70   def __init__(self, wav_dir, midi_dir, add_wav_glob,
71                *unused_args, **unused_kwargs):
72     self._wav_dir = wav_dir
73     self._midi_dir = midi_dir
74     self._add_wav_glob = add_wav_glob
75     super(CreateExampleDoFn, self).__init__(*unused_args, **unused_kwargs)
76 
77   def process(self, paths):
78     midi_path, wav_path_base = paths
79 
80     if self._add_wav_glob:
81       wav_paths = tf.io.gfile.glob(wav_path_base + '*')
82     else:
83       wav_paths = [wav_path_base]
84 
85     if midi_path:
86       base_ns = midi_io.midi_file_to_note_sequence(midi_path)
87       base_ns.filename = midi_path
88     else:
89       base_ns = music_pb2.NoteSequence()
90 
91     for wav_path in wav_paths:
92       logging.info('Creating Example %s:%s', midi_path, wav_path)
93       wav_data = tf.io.gfile.GFile(wav_path, 'rb').read()
94 
95       ns = copy.deepcopy(base_ns)
96 
97       # Use base names.
98       ns.id = '%s:%s' % (wav_path.replace(self._wav_dir, ''),
99                          midi_path.replace(self._midi_dir, ''))
100 
101       Metrics.counter('create_example', 'read_midi_wav').inc()
102 
103       example = audio_label_data_utils.create_example(ns.id, ns, wav_data)
104 
105       Metrics.counter('create_example', 'created_example').inc()
106       yield example
107 
108 
109 def main(argv):
110   del argv
111 
112 
113   flags.mark_flags_as_required(['csv', 'output_directory'])
114 
115   tf.io.gfile.makedirs(FLAGS.output_directory)
116 
117   with tf.io.gfile.GFile(FLAGS.csv) as f:
118     reader = csv.DictReader(f)
119 
120     splits = collections.defaultdict(list)
121     for row in reader:
122       splits[row['split']].append(
123           (os.path.join(FLAGS.midi_dir, row['midi_filename']),
124            os.path.join(FLAGS.wav_dir, row['audio_filename'])))
125 
126   if sorted(splits.keys()) != sorted(FLAGS.expected_splits.split(',')):
127     raise ValueError('Got unexpected set of splits: %s' % list(splits.keys()))
128 
129   pipeline_options = beam.options.pipeline_options.PipelineOptions(
130       FLAGS.pipeline_options)
131   with beam.Pipeline(options=pipeline_options) as p:
132     for split in splits:
133       split_p = p | 'prepare_split_%s' % split >> beam.Create(splits[split])
134       split_p |= 'create_examples_%s' % split >> beam.ParDo(
135           CreateExampleDoFn(FLAGS.wav_dir, FLAGS.midi_dir, FLAGS.add_wav_glob))
136       split_p |= 'write_%s' % split >> beam.io.WriteToTFRecord(
137           os.path.join(FLAGS.output_directory, '%s.tfrecord' % split),
138           coder=beam.coders.ProtoCoder(tf.train.Example),
139           num_shards=FLAGS.num_shards)
140 
141 
142 if __name__ == '__main__':
143   app.run(main)
144
```
--- 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()

```

diff --git a/magenta/models/onsets_frames_transcription/onsets_frames_transcription_create_tfrecords.py b/magenta/models/onsets_frames_transcription/onsets_frames_transcription_create_tfrecords.py
--- a/magenta/models/onsets_frames_transcription/onsets_frames_transcription_create_tfrecords.py
+++ b/magenta/models/onsets_frames_transcription/onsets_frames_transcription_create_tfrecords.py
@@ -139,5 +139,10 @@
           num_shards=FLAGS.num_shards)
 
 
-if __name__ == '__main__':
+def console_entry_point():
+  tf.disable_v2_behavior()
   app.run(main)
+
+
+if __name__ == '__main__':
+  console_entry_point()

{"golden_diff": "diff --git a/magenta/models/onsets_frames_transcription/onsets_frames_transcription_create_tfrecords.py b/magenta/models/onsets_frames_transcription/onsets_frames_transcription_create_tfrecords.py\n--- a/magenta/models/onsets_frames_transcription/onsets_frames_transcription_create_tfrecords.py\n+++ b/magenta/models/onsets_frames_transcription/onsets_frames_transcription_create_tfrecords.py\n@@ -139,5 +139,10 @@\n           num_shards=FLAGS.num_shards)\n \n \n-if __name__ == '__main__':\n+def console_entry_point():\n+  tf.disable_v2_behavior()\n   app.run(main)\n+\n+\n+if __name__ == '__main__':\n+  console_entry_point()\n", "issue": "Retraining Onsets and Frames Drums model with E-GMD dataset\nHello,\r\n\r\nI am trying to retrain OaF model with the E-GMD dataset for drums transcription. I first downloaded the E-GMD dataset which has its corresponding csv file and a directoy for each drummer and subdirectories with the sessions.\r\n\r\nI am trying to do the first step following the code in ```onsets_frames_transcription_create_tfrecords``` which I found that it is:\r\n\r\n```\r\nonsets_frames_transcription_create_tfrecords \\\r\n  --csv=\".../e-gmd-v1.0.0/e-gmd-v1.0.0.csv\" \\\r\n  --output_directory=\".../e-gmd-v1.0.0\" \\\r\n  --num_shards=\"0\" \\\r\n  --wav_dir=\".../e-gmd-v1.0.0\" \\\r\n  --midi_dir=\".../e-gmd-v1.0.0\" \\\r\n  --expected_splits=\"train,validation,test\"\r\n```\r\n\r\nBut i got the following error which I don't know where does it come from:\r\n\r\n```\r\n2020-08-05 17:23:45.289023: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found\r\n2020-08-05 17:23:45.289348: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\nWARNING:tensorflow:From c:\\users\\carlos\\anaconda3\\lib\\site-packages\\tensorflow\\python\\compat\\v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\r\nInstructions for updating:\r\nnon-resource variables are not supported in the long term\r\nWARNING:tensorflow:From c:\\users\\carlos\\anaconda3\\lib\\site-packages\\tensorflow\\python\\compat\\v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\r\nInstructions for updating:\r\nnon-resource variables are not supported in the long term\r\nTraceback (most recent call last):\r\n  File \"c:\\users\\carlos\\anaconda3\\lib\\runpy.py\", line 193, in _run_module_as_main\r\n    \"__main__\", mod_spec)\r\n  File \"c:\\users\\carlos\\anaconda3\\lib\\runpy.py\", line 85, in _run_code\r\n    exec(code, run_globals)\r\n  File \"C:\\Users\\Carlos\\Anaconda3\\Scripts\\onsets_frames_transcription_create_tfrecords.exe\\__main__.py\", line 4, in <module>\r\nImportError: cannot import name 'console_entry_point'\r\n```\r\nI don't know if I have to change the paths of the wav and MIDI files in order to have the wav files in a directory and the MIDI files in other directory or the error comes from installation issues, versions, etc.\r\n\r\nI am using Winows 10.\n", "before_files": [{"content": "# Copyright 2020 The Magenta Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# Lint as: python3\nr\"\"\"Beam job for creating tfrecord files from datasets.\n\nExpects a CSV with the following fields: audio_filename, midi_filename, split\n\nUsage:\nonsets_frames_transcription_create_tfrecords \\\n  --csv=\"/path/to/dataset.csv\" \\\n  --output_directory=\"/path/to/output\" \\\n  --num_shards=\"0\" \\\n  --wav_dir=\"/path/to/dataset/audio\" \\\n  --midi_dir=\"/path/to/dataset/midi\" \\\n  --expected_splits=\"train,validation,test\"\n\n\"\"\"\n\nimport collections\nimport copy\nimport csv\nimport os\n\nfrom absl import app\nfrom absl import flags\nfrom absl import logging\n\nimport apache_beam as beam\nfrom apache_beam.metrics import Metrics\nfrom magenta.models.onsets_frames_transcription import audio_label_data_utils\nfrom note_seq import midi_io\nfrom note_seq.protobuf import music_pb2\nimport tensorflow.compat.v1 as tf\n\ntf.disable_v2_behavior()\n\nFLAGS = flags.FLAGS\n\nflags.DEFINE_string('csv', None, 'Path to dataset CSV')\nflags.DEFINE_string('output_directory', None, 'Path to output_directory')\nflags.DEFINE_string('wav_dir', None, 'Directory for wav files.')\nflags.DEFINE_string('midi_dir', None, 'Directory for midi files.')\nflags.DEFINE_integer('num_shards', 0, 'number of output shards')\nflags.DEFINE_string('expected_splits', 'train,validation,test',\n                    'Comma separated list of expected splits.')\nflags.DEFINE_boolean(\n    'add_wav_glob', False,\n    'If true, will add * to end of wav paths and use all matching files.')\nflags.DEFINE_list(\n    'pipeline_options', '--runner=DirectRunner',\n    'A comma-separated list of command line arguments to be used as options '\n    'for the Beam Pipeline.')\n\n\nclass CreateExampleDoFn(beam.DoFn):\n  \"\"\"Splits wav and midi files for the dataset.\"\"\"\n\n  def __init__(self, wav_dir, midi_dir, add_wav_glob,\n               *unused_args, **unused_kwargs):\n    self._wav_dir = wav_dir\n    self._midi_dir = midi_dir\n    self._add_wav_glob = add_wav_glob\n    super(CreateExampleDoFn, self).__init__(*unused_args, **unused_kwargs)\n\n  def process(self, paths):\n    midi_path, wav_path_base = paths\n\n    if self._add_wav_glob:\n      wav_paths = tf.io.gfile.glob(wav_path_base + '*')\n    else:\n      wav_paths = [wav_path_base]\n\n    if midi_path:\n      base_ns = midi_io.midi_file_to_note_sequence(midi_path)\n      base_ns.filename = midi_path\n    else:\n      base_ns = music_pb2.NoteSequence()\n\n    for wav_path in wav_paths:\n      logging.info('Creating Example %s:%s', midi_path, wav_path)\n      wav_data = tf.io.gfile.GFile(wav_path, 'rb').read()\n\n      ns = copy.deepcopy(base_ns)\n\n      # Use base names.\n      ns.id = '%s:%s' % (wav_path.replace(self._wav_dir, ''),\n                         midi_path.replace(self._midi_dir, ''))\n\n      Metrics.counter('create_example', 'read_midi_wav').inc()\n\n      example = audio_label_data_utils.create_example(ns.id, ns, wav_data)\n\n      Metrics.counter('create_example', 'created_example').inc()\n      yield example\n\n\ndef main(argv):\n  del argv\n\n\n  flags.mark_flags_as_required(['csv', 'output_directory'])\n\n  tf.io.gfile.makedirs(FLAGS.output_directory)\n\n  with tf.io.gfile.GFile(FLAGS.csv) as f:\n    reader = csv.DictReader(f)\n\n    splits = collections.defaultdict(list)\n    for row in reader:\n      splits[row['split']].append(\n          (os.path.join(FLAGS.midi_dir, row['midi_filename']),\n           os.path.join(FLAGS.wav_dir, row['audio_filename'])))\n\n  if sorted(splits.keys()) != sorted(FLAGS.expected_splits.split(',')):\n    raise ValueError('Got unexpected set of splits: %s' % list(splits.keys()))\n\n  pipeline_options = beam.options.pipeline_options.PipelineOptions(\n      FLAGS.pipeline_options)\n  with beam.Pipeline(options=pipeline_options) as p:\n    for split in splits:\n      split_p = p | 'prepare_split_%s' % split >> beam.Create(splits[split])\n      split_p |= 'create_examples_%s' % split >> beam.ParDo(\n          CreateExampleDoFn(FLAGS.wav_dir, FLAGS.midi_dir, FLAGS.add_wav_glob))\n      split_p |= 'write_%s' % split >> beam.io.WriteToTFRecord(\n          os.path.join(FLAGS.output_directory, '%s.tfrecord' % split),\n          coder=beam.coders.ProtoCoder(tf.train.Example),\n          num_shards=FLAGS.num_shards)\n\n\nif __name__ == '__main__':\n  app.run(main)\n", "path": "magenta/models/onsets_frames_transcription/onsets_frames_transcription_create_tfrecords.py"}], "after_files": [{"content": "# Copyright 2020 The Magenta Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# Lint as: python3\nr\"\"\"Beam job for creating tfrecord files from datasets.\n\nExpects a CSV with the following fields: audio_filename, midi_filename, split\n\nUsage:\nonsets_frames_transcription_create_tfrecords \\\n  --csv=\"/path/to/dataset.csv\" \\\n  --output_directory=\"/path/to/output\" \\\n  --num_shards=\"0\" \\\n  --wav_dir=\"/path/to/dataset/audio\" \\\n  --midi_dir=\"/path/to/dataset/midi\" \\\n  --expected_splits=\"train,validation,test\"\n\n\"\"\"\n\nimport collections\nimport copy\nimport csv\nimport os\n\nfrom absl import app\nfrom absl import flags\nfrom absl import logging\n\nimport apache_beam as beam\nfrom apache_beam.metrics import Metrics\nfrom magenta.models.onsets_frames_transcription import audio_label_data_utils\nfrom note_seq import midi_io\nfrom note_seq.protobuf import music_pb2\nimport tensorflow.compat.v1 as tf\n\ntf.disable_v2_behavior()\n\nFLAGS = flags.FLAGS\n\nflags.DEFINE_string('csv', None, 'Path to dataset CSV')\nflags.DEFINE_string('output_directory', None, 'Path to output_directory')\nflags.DEFINE_string('wav_dir', None, 'Directory for wav files.')\nflags.DEFINE_string('midi_dir', None, 'Directory for midi files.')\nflags.DEFINE_integer('num_shards', 0, 'number of output shards')\nflags.DEFINE_string('expected_splits', 'train,validation,test',\n                    'Comma separated list of expected splits.')\nflags.DEFINE_boolean(\n    'add_wav_glob', False,\n    'If true, will add * to end of wav paths and use all matching files.')\nflags.DEFINE_list(\n    'pipeline_options', '--runner=DirectRunner',\n    'A comma-separated list of command line arguments to be used as options '\n    'for the Beam Pipeline.')\n\n\nclass CreateExampleDoFn(beam.DoFn):\n  \"\"\"Splits wav and midi files for the dataset.\"\"\"\n\n  def __init__(self, wav_dir, midi_dir, add_wav_glob,\n               *unused_args, **unused_kwargs):\n    self._wav_dir = wav_dir\n    self._midi_dir = midi_dir\n    self._add_wav_glob = add_wav_glob\n    super(CreateExampleDoFn, self).__init__(*unused_args, **unused_kwargs)\n\n  def process(self, paths):\n    midi_path, wav_path_base = paths\n\n    if self._add_wav_glob:\n      wav_paths = tf.io.gfile.glob(wav_path_base + '*')\n    else:\n      wav_paths = [wav_path_base]\n\n    if midi_path:\n      base_ns = midi_io.midi_file_to_note_sequence(midi_path)\n      base_ns.filename = midi_path\n    else:\n      base_ns = music_pb2.NoteSequence()\n\n    for wav_path in wav_paths:\n      logging.info('Creating Example %s:%s', midi_path, wav_path)\n      wav_data = tf.io.gfile.GFile(wav_path, 'rb').read()\n\n      ns = copy.deepcopy(base_ns)\n\n      # Use base names.\n      ns.id = '%s:%s' % (wav_path.replace(self._wav_dir, ''),\n                         midi_path.replace(self._midi_dir, ''))\n\n      Metrics.counter('create_example', 'read_midi_wav').inc()\n\n      example = audio_label_data_utils.create_example(ns.id, ns, wav_data)\n\n      Metrics.counter('create_example', 'created_example').inc()\n      yield example\n\n\ndef main(argv):\n  del argv\n\n\n  flags.mark_flags_as_required(['csv', 'output_directory'])\n\n  tf.io.gfile.makedirs(FLAGS.output_directory)\n\n  with tf.io.gfile.GFile(FLAGS.csv) as f:\n    reader = csv.DictReader(f)\n\n    splits = collections.defaultdict(list)\n    for row in reader:\n      splits[row['split']].append(\n          (os.path.join(FLAGS.midi_dir, row['midi_filename']),\n           os.path.join(FLAGS.wav_dir, row['audio_filename'])))\n\n  if sorted(splits.keys()) != sorted(FLAGS.expected_splits.split(',')):\n    raise ValueError('Got unexpected set of splits: %s' % list(splits.keys()))\n\n  pipeline_options = beam.options.pipeline_options.PipelineOptions(\n      FLAGS.pipeline_options)\n  with beam.Pipeline(options=pipeline_options) as p:\n    for split in splits:\n      split_p = p | 'prepare_split_%s' % split >> beam.Create(splits[split])\n      split_p |= 'create_examples_%s' % split >> beam.ParDo(\n          CreateExampleDoFn(FLAGS.wav_dir, FLAGS.midi_dir, FLAGS.add_wav_glob))\n      split_p |= 'write_%s' % split >> beam.io.WriteToTFRecord(\n          os.path.join(FLAGS.output_directory, '%s.tfrecord' % split),\n          coder=beam.coders.ProtoCoder(tf.train.Example),\n          num_shards=FLAGS.num_shards)\n\n\ndef console_entry_point():\n  tf.disable_v2_behavior()\n  app.run(main)\n\n\nif __name__ == '__main__':\n  console_entry_point()\n", "path": "magenta/models/onsets_frames_transcription/onsets_frames_transcription_create_tfrecords.py"}]}