prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  [tensor] fix some unittests
  
  [tensor] fix some unittests
  
  [tensor] fix some unittests
  
  
  </issue>
  <code>
  [start of applications/Chat/coati/models/gpt/gpt_actor.py]
  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
  [end of applications/Chat/coati/models/gpt/gpt_actor.py]
  [start of applications/Chat/coati/models/gpt/gpt_critic.py]
  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 of applications/Chat/coati/models/gpt/gpt_critic.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: 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)
  
verification_info: {"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"}]}