We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
defect_enhancement_task Taskcluster task is missing the artifact
From the task () logs:
```
[taskcluster 2019-05-21 08:37:51.436Z] === Task Finished ===
[taskcluster 2019-05-21 08:37:51.519Z] Artifact "public/defectenhancementtaskmodel.xz" not found at "/defectenhancementtaskmodel.xz"
[taskcluster 2019-05-21 08:37:51.927Z] Successful task run with exit code: 0 completed in 471.275 seconds
```

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `scripts/trainer.py`
Content:
```
1 # -*- coding: utf-8 -*-
2 
3 import argparse
4 import lzma
5 import os
6 import shutil
7 from logging import INFO, basicConfig, getLogger
8 from urllib.request import urlretrieve
9 
10 from bugbug.models import get_model_class
11 
12 basicConfig(level=INFO)
13 logger = getLogger(__name__)
14 
15 BASE_URL = "https://index.taskcluster.net/v1/task/project.relman.bugbug.data_{}.latest/artifacts/public"
16 
17 
18 class Trainer(object):
19     def decompress_file(self, path):
20         with lzma.open(f"{path}.xz", "rb") as input_f:
21             with open(path, "wb") as output_f:
22                 shutil.copyfileobj(input_f, output_f)
23 
24     def compress_file(self, path):
25         with open(path, "rb") as input_f:
26             with lzma.open(f"{path}.xz", "wb") as output_f:
27                 shutil.copyfileobj(input_f, output_f)
28 
29     def go(self, model_name):
30         # Download datasets that were built by bugbug_data.
31         os.makedirs("data", exist_ok=True)
32 
33         # Bugs.json
34         logger.info("Downloading bugs database")
35         bugs_url = BASE_URL.format("bugs")
36         urlretrieve(f"{bugs_url}/bugs.json.xz", "data/bugs.json.xz")
37         logger.info("Decompressing bugs database")
38         self.decompress_file("data/bugs.json")
39 
40         logger.info(f"Training *{model_name}* model")
41 
42         model_class = get_model_class(model_name)
43         model = model_class()
44         model.train()
45 
46         model_file_name = f"{model_name}model"
47         self.compress_file(model_file_name)
48 
49 
50 def main():
51     description = "Train the models"
52     parser = argparse.ArgumentParser(description=description)
53 
54     parser.add_argument("model", help="Which model to train.")
55 
56     args = parser.parse_args()
57 
58     retriever = Trainer()
59     retriever.go(args.model)
60
```
--- 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/scripts/trainer.py b/scripts/trainer.py
--- a/scripts/trainer.py
+++ b/scripts/trainer.py
@@ -43,9 +43,14 @@
         model = model_class()
         model.train()
 
+        logger.info(f"Training done")
+
         model_file_name = f"{model_name}model"
+        assert os.path.exists(model_file_name)
         self.compress_file(model_file_name)
 
+        logger.info(f"Model compressed")
+
 
 def main():
     description = "Train the models"

{"golden_diff": "diff --git a/scripts/trainer.py b/scripts/trainer.py\n--- a/scripts/trainer.py\n+++ b/scripts/trainer.py\n@@ -43,9 +43,14 @@\n         model = model_class()\n         model.train()\n \n+        logger.info(f\"Training done\")\n+\n         model_file_name = f\"{model_name}model\"\n+        assert os.path.exists(model_file_name)\n         self.compress_file(model_file_name)\n \n+        logger.info(f\"Model compressed\")\n+\n \n def main():\n     description = \"Train the models\"\n", "issue": "defect_enhancement_task Taskcluster task is missing the artifact\nFrom the task () logs:\r\n```\r\n[taskcluster 2019-05-21 08:37:51.436Z] === Task Finished ===\r\n[taskcluster 2019-05-21 08:37:51.519Z] Artifact \"public/defectenhancementtaskmodel.xz\" not found at \"/defectenhancementtaskmodel.xz\"\r\n[taskcluster 2019-05-21 08:37:51.927Z] Successful task run with exit code: 0 completed in 471.275 seconds\r\n```\n", "before_files": [{"content": "# -*- coding: utf-8 -*-\n\nimport argparse\nimport lzma\nimport os\nimport shutil\nfrom logging import INFO, basicConfig, getLogger\nfrom urllib.request import urlretrieve\n\nfrom bugbug.models import get_model_class\n\nbasicConfig(level=INFO)\nlogger = getLogger(__name__)\n\nBASE_URL = \"https://index.taskcluster.net/v1/task/project.relman.bugbug.data_{}.latest/artifacts/public\"\n\n\nclass Trainer(object):\n    def decompress_file(self, path):\n        with lzma.open(f\"{path}.xz\", \"rb\") as input_f:\n            with open(path, \"wb\") as output_f:\n                shutil.copyfileobj(input_f, output_f)\n\n    def compress_file(self, path):\n        with open(path, \"rb\") as input_f:\n            with lzma.open(f\"{path}.xz\", \"wb\") as output_f:\n                shutil.copyfileobj(input_f, output_f)\n\n    def go(self, model_name):\n        # Download datasets that were built by bugbug_data.\n        os.makedirs(\"data\", exist_ok=True)\n\n        # Bugs.json\n        logger.info(\"Downloading bugs database\")\n        bugs_url = BASE_URL.format(\"bugs\")\n        urlretrieve(f\"{bugs_url}/bugs.json.xz\", \"data/bugs.json.xz\")\n        logger.info(\"Decompressing bugs database\")\n        self.decompress_file(\"data/bugs.json\")\n\n        logger.info(f\"Training *{model_name}* model\")\n\n        model_class = get_model_class(model_name)\n        model = model_class()\n        model.train()\n\n        model_file_name = f\"{model_name}model\"\n        self.compress_file(model_file_name)\n\n\ndef main():\n    description = \"Train the models\"\n    parser = argparse.ArgumentParser(description=description)\n\n    parser.add_argument(\"model\", help=\"Which model to train.\")\n\n    args = parser.parse_args()\n\n    retriever = Trainer()\n    retriever.go(args.model)\n", "path": "scripts/trainer.py"}], "after_files": [{"content": "# -*- coding: utf-8 -*-\n\nimport argparse\nimport lzma\nimport os\nimport shutil\nfrom logging import INFO, basicConfig, getLogger\nfrom urllib.request import urlretrieve\n\nfrom bugbug.models import get_model_class\n\nbasicConfig(level=INFO)\nlogger = getLogger(__name__)\n\nBASE_URL = \"https://index.taskcluster.net/v1/task/project.relman.bugbug.data_{}.latest/artifacts/public\"\n\n\nclass Trainer(object):\n    def decompress_file(self, path):\n        with lzma.open(f\"{path}.xz\", \"rb\") as input_f:\n            with open(path, \"wb\") as output_f:\n                shutil.copyfileobj(input_f, output_f)\n\n    def compress_file(self, path):\n        with open(path, \"rb\") as input_f:\n            with lzma.open(f\"{path}.xz\", \"wb\") as output_f:\n                shutil.copyfileobj(input_f, output_f)\n\n    def go(self, model_name):\n        # Download datasets that were built by bugbug_data.\n        os.makedirs(\"data\", exist_ok=True)\n\n        # Bugs.json\n        logger.info(\"Downloading bugs database\")\n        bugs_url = BASE_URL.format(\"bugs\")\n        urlretrieve(f\"{bugs_url}/bugs.json.xz\", \"data/bugs.json.xz\")\n        logger.info(\"Decompressing bugs database\")\n        self.decompress_file(\"data/bugs.json\")\n\n        logger.info(f\"Training *{model_name}* model\")\n\n        model_class = get_model_class(model_name)\n        model = model_class()\n        model.train()\n\n        logger.info(f\"Training done\")\n\n        model_file_name = f\"{model_name}model\"\n        assert os.path.exists(model_file_name)\n        self.compress_file(model_file_name)\n\n        logger.info(f\"Model compressed\")\n\n\ndef main():\n    description = \"Train the models\"\n    parser = argparse.ArgumentParser(description=description)\n\n    parser.add_argument(\"model\", help=\"Which model to train.\")\n\n    args = parser.parse_args()\n\n    retriever = Trainer()\n    retriever.go(args.model)\n", "path": "scripts/trainer.py"}]}