We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[tensor] fix some unittests

[tensor] fix some unittests

[tensor] fix some unittests


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `applications/Chat/coati/models/gpt/gpt_actor.py`
Content:
```
1 from typing import Optional
2 
3 from transformers.models.gpt2.configuration_gpt2 import GPT2Config
4 from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
5 
6 from ..base import Actor
7 
8 
9 class GPTActor(Actor):
10     """
11     GPT Actor model.
12 
13     Args:
14         pretrained (str): Pretrained model name or path.
15         config (GPT2Config): Model config.
16         checkpoint (bool): Enable gradient checkpointing.
17         lora_rank (int): Rank of the LoRa layer.
18         lora_train_bias (str): Bias training strategy for the LoRa layer.
19     """
20 
21     def __init__(self,
22                  pretrained: Optional[str] = None,
23                  config: Optional[GPT2Config] = None,
24                  checkpoint: bool = False,
25                  lora_rank: int = 0,
26                  lora_train_bias: str = 'none') -> None:
27         if pretrained is not None:
28             model = GPT2LMHeadModel.from_pretrained(pretrained)
29         elif config is not None:
30             model = GPT2LMHeadModel(config)
31         else:
32             model = GPT2LMHeadModel(GPT2Config())
33         if checkpoint:
34             model.gradient_checkpointing_enable()
35         super().__init__(model, lora_rank, lora_train_bias)
36
```

Path: `applications/Chat/coati/models/gpt/gpt_critic.py`
Content:
```
1 from typing import Optional
2 
3 import torch.nn as nn
4 from transformers.models.gpt2.configuration_gpt2 import GPT2Config
5 from transformers.models.gpt2.modeling_gpt2 import GPT2Model
6 
7 from ..base import Critic
8 
9 
10 class GPTCritic(Critic):
11     """
12     GPT Critic model.
13 
14     Args:
15         pretrained (str): Pretrained model name or path.
16         config (GPT2Config): Model config.
17         checkpoint (bool): Enable gradient checkpointing.
18         lora_rank (int): Rank of the LO-RA decomposition.
19         lora_train_bias (str): LoRA bias training mode.
20     """
21 
22     def __init__(self,
23                  pretrained: Optional[str] = None,
24                  config: Optional[GPT2Config] = None,
25                  checkpoint: bool = False,
26                  lora_rank: int = 0,
27                  lora_train_bias: str = 'none') -> None:
28         if pretrained is not None:
29             model = GPT2Model.from_pretrained(pretrained)
30         elif config is not None:
31             model = GPT2Model(config)
32         else:
33             model = GPT2Model(GPT2Config())
34         if checkpoint:
35             model.gradient_checkpointing_enable()
36         value_head = nn.Linear(model.config.n_embd, 1)
37         super().__init__(model, value_head, lora_rank, lora_train_bias)
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()

```

diff --git a/applications/Chat/coati/models/gpt/gpt_actor.py b/applications/Chat/coati/models/gpt/gpt_actor.py
--- a/applications/Chat/coati/models/gpt/gpt_actor.py
+++ b/applications/Chat/coati/models/gpt/gpt_actor.py
@@ -23,7 +23,8 @@
                  config: Optional[GPT2Config] = None,
                  checkpoint: bool = False,
                  lora_rank: int = 0,
-                 lora_train_bias: str = 'none') -> None:
+                 lora_train_bias: str = 'none',
+                 **kwargs) -> None:
         if pretrained is not None:
             model = GPT2LMHeadModel.from_pretrained(pretrained)
         elif config is not None:
@@ -32,4 +33,4 @@
             model = GPT2LMHeadModel(GPT2Config())
         if checkpoint:
             model.gradient_checkpointing_enable()
-        super().__init__(model, lora_rank, lora_train_bias)
+        super().__init__(model, lora_rank, lora_train_bias, **kwargs)
diff --git a/applications/Chat/coati/models/gpt/gpt_critic.py b/applications/Chat/coati/models/gpt/gpt_critic.py
--- a/applications/Chat/coati/models/gpt/gpt_critic.py
+++ b/applications/Chat/coati/models/gpt/gpt_critic.py
@@ -24,7 +24,8 @@
                  config: Optional[GPT2Config] = None,
                  checkpoint: bool = False,
                  lora_rank: int = 0,
-                 lora_train_bias: str = 'none') -> None:
+                 lora_train_bias: str = 'none',
+                 **kwargs) -> None:
         if pretrained is not None:
             model = GPT2Model.from_pretrained(pretrained)
         elif config is not None:
@@ -34,4 +35,4 @@
         if checkpoint:
             model.gradient_checkpointing_enable()
         value_head = nn.Linear(model.config.n_embd, 1)
-        super().__init__(model, value_head, lora_rank, lora_train_bias)
+        super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs)

{"golden_diff": "diff --git a/applications/Chat/coati/models/gpt/gpt_actor.py b/applications/Chat/coati/models/gpt/gpt_actor.py\n--- a/applications/Chat/coati/models/gpt/gpt_actor.py\n+++ b/applications/Chat/coati/models/gpt/gpt_actor.py\n@@ -23,7 +23,8 @@\n                  config: Optional[GPT2Config] = None,\n                  checkpoint: bool = False,\n                  lora_rank: int = 0,\n-                 lora_train_bias: str = 'none') -> None:\n+                 lora_train_bias: str = 'none',\n+                 **kwargs) -> None:\n         if pretrained is not None:\n             model = GPT2LMHeadModel.from_pretrained(pretrained)\n         elif config is not None:\n@@ -32,4 +33,4 @@\n             model = GPT2LMHeadModel(GPT2Config())\n         if checkpoint:\n             model.gradient_checkpointing_enable()\n-        super().__init__(model, lora_rank, lora_train_bias)\n+        super().__init__(model, lora_rank, lora_train_bias, **kwargs)\ndiff --git a/applications/Chat/coati/models/gpt/gpt_critic.py b/applications/Chat/coati/models/gpt/gpt_critic.py\n--- a/applications/Chat/coati/models/gpt/gpt_critic.py\n+++ b/applications/Chat/coati/models/gpt/gpt_critic.py\n@@ -24,7 +24,8 @@\n                  config: Optional[GPT2Config] = None,\n                  checkpoint: bool = False,\n                  lora_rank: int = 0,\n-                 lora_train_bias: str = 'none') -> None:\n+                 lora_train_bias: str = 'none',\n+                 **kwargs) -> None:\n         if pretrained is not None:\n             model = GPT2Model.from_pretrained(pretrained)\n         elif config is not None:\n@@ -34,4 +35,4 @@\n         if checkpoint:\n             model.gradient_checkpointing_enable()\n         value_head = nn.Linear(model.config.n_embd, 1)\n-        super().__init__(model, value_head, lora_rank, lora_train_bias)\n+        super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs)\n", "issue": "[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n", "before_files": [{"content": "from typing import Optional\n\nfrom transformers.models.gpt2.configuration_gpt2 import GPT2Config\nfrom transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel\n\nfrom ..base import Actor\n\n\nclass GPTActor(Actor):\n    \"\"\"\n    GPT Actor model.\n\n    Args:\n        pretrained (str): Pretrained model name or path.\n        config (GPT2Config): Model config.\n        checkpoint (bool): Enable gradient checkpointing.\n        lora_rank (int): Rank of the LoRa layer.\n        lora_train_bias (str): Bias training strategy for the LoRa layer.\n    \"\"\"\n\n    def __init__(self,\n                 pretrained: Optional[str] = None,\n                 config: Optional[GPT2Config] = None,\n                 checkpoint: bool = False,\n                 lora_rank: int = 0,\n                 lora_train_bias: str = 'none') -> None:\n        if pretrained is not None:\n            model = GPT2LMHeadModel.from_pretrained(pretrained)\n        elif config is not None:\n            model = GPT2LMHeadModel(config)\n        else:\n            model = GPT2LMHeadModel(GPT2Config())\n        if checkpoint:\n            model.gradient_checkpointing_enable()\n        super().__init__(model, lora_rank, lora_train_bias)\n", "path": "applications/Chat/coati/models/gpt/gpt_actor.py"}, {"content": "from typing import Optional\n\nimport torch.nn as nn\nfrom transformers.models.gpt2.configuration_gpt2 import GPT2Config\nfrom transformers.models.gpt2.modeling_gpt2 import GPT2Model\n\nfrom ..base import Critic\n\n\nclass GPTCritic(Critic):\n    \"\"\"\n    GPT Critic model.\n\n    Args:\n        pretrained (str): Pretrained model name or path.\n        config (GPT2Config): Model config.\n        checkpoint (bool): Enable gradient checkpointing.\n        lora_rank (int): Rank of the LO-RA decomposition.\n        lora_train_bias (str): LoRA bias training mode.\n    \"\"\"\n\n    def __init__(self,\n                 pretrained: Optional[str] = None,\n                 config: Optional[GPT2Config] = None,\n                 checkpoint: bool = False,\n                 lora_rank: int = 0,\n                 lora_train_bias: str = 'none') -> None:\n        if pretrained is not None:\n            model = GPT2Model.from_pretrained(pretrained)\n        elif config is not None:\n            model = GPT2Model(config)\n        else:\n            model = GPT2Model(GPT2Config())\n        if checkpoint:\n            model.gradient_checkpointing_enable()\n        value_head = nn.Linear(model.config.n_embd, 1)\n        super().__init__(model, value_head, lora_rank, lora_train_bias)\n", "path": "applications/Chat/coati/models/gpt/gpt_critic.py"}], "after_files": [{"content": "from typing import Optional\n\nfrom transformers.models.gpt2.configuration_gpt2 import GPT2Config\nfrom transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel\n\nfrom ..base import Actor\n\n\nclass GPTActor(Actor):\n    \"\"\"\n    GPT Actor model.\n\n    Args:\n        pretrained (str): Pretrained model name or path.\n        config (GPT2Config): Model config.\n        checkpoint (bool): Enable gradient checkpointing.\n        lora_rank (int): Rank of the LoRa layer.\n        lora_train_bias (str): Bias training strategy for the LoRa layer.\n    \"\"\"\n\n    def __init__(self,\n                 pretrained: Optional[str] = None,\n                 config: Optional[GPT2Config] = None,\n                 checkpoint: bool = False,\n                 lora_rank: int = 0,\n                 lora_train_bias: str = 'none',\n                 **kwargs) -> None:\n        if pretrained is not None:\n            model = GPT2LMHeadModel.from_pretrained(pretrained)\n        elif config is not None:\n            model = GPT2LMHeadModel(config)\n        else:\n            model = GPT2LMHeadModel(GPT2Config())\n        if checkpoint:\n            model.gradient_checkpointing_enable()\n        super().__init__(model, lora_rank, lora_train_bias, **kwargs)\n", "path": "applications/Chat/coati/models/gpt/gpt_actor.py"}, {"content": "from typing import Optional\n\nimport torch.nn as nn\nfrom transformers.models.gpt2.configuration_gpt2 import GPT2Config\nfrom transformers.models.gpt2.modeling_gpt2 import GPT2Model\n\nfrom ..base import Critic\n\n\nclass GPTCritic(Critic):\n    \"\"\"\n    GPT Critic model.\n\n    Args:\n        pretrained (str): Pretrained model name or path.\n        config (GPT2Config): Model config.\n        checkpoint (bool): Enable gradient checkpointing.\n        lora_rank (int): Rank of the LO-RA decomposition.\n        lora_train_bias (str): LoRA bias training mode.\n    \"\"\"\n\n    def __init__(self,\n                 pretrained: Optional[str] = None,\n                 config: Optional[GPT2Config] = None,\n                 checkpoint: bool = False,\n                 lora_rank: int = 0,\n                 lora_train_bias: str = 'none',\n                 **kwargs) -> None:\n        if pretrained is not None:\n            model = GPT2Model.from_pretrained(pretrained)\n        elif config is not None:\n            model = GPT2Model(config)\n        else:\n            model = GPT2Model(GPT2Config())\n        if checkpoint:\n            model.gradient_checkpointing_enable()\n        value_head = nn.Linear(model.config.n_embd, 1)\n        super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs)\n", "path": "applications/Chat/coati/models/gpt/gpt_critic.py"}]}