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/base/actor.py]
  1 from typing import Optional
  2 
  3 import torch
  4 import torch.nn as nn
  5 
  6 from ..lora import LoRAModule
  7 
  8 
  9 class Actor(LoRAModule):
  10     """
  11     Actor model base class.
  12 
  13     Args:
  14         model (nn.Module): Actor Model.
  15         lora_rank (int): LoRA rank.
  16         lora_train_bias (str): LoRA bias training mode.
  17     """
  18 
  19     def __init__(self, model: nn.Module, lora_rank: int = 0, lora_train_bias: str = 'none') -> None:
  20         super().__init__(lora_rank=lora_rank, lora_train_bias=lora_train_bias)
  21         self.model = model
  22         self.convert_to_lora()
  23 
  24     def forward(self,
  25                 input_ids: torch.LongTensor,
  26                 attention_mask: Optional[torch.Tensor] = None,
  27                 **model_kwargs,  # HACK: `generate` method may pass more kwargs
  28                 ) -> torch.Tensor:
  29         """Returns model output.
  30         """
  31         output = self.model(
  32             input_ids,
  33             attention_mask=attention_mask,
  34             **model_kwargs
  35         )
  36         return output
  37
  [end of applications/Chat/coati/models/base/actor.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/base/actor.py b/applications/Chat/coati/models/base/actor.py
  --- a/applications/Chat/coati/models/base/actor.py
  +++ b/applications/Chat/coati/models/base/actor.py
  @@ -21,16 +21,13 @@
           self.model = model
           self.convert_to_lora()
   
  -    def forward(self,
  -                input_ids: torch.LongTensor,
  -                attention_mask: Optional[torch.Tensor] = None,
  -                **model_kwargs,  # HACK: `generate` method may pass more kwargs
  -                ) -> torch.Tensor:
  +    def forward(
  +            self,
  +            input_ids: torch.LongTensor,
  +            attention_mask: Optional[torch.Tensor] = None,
  +            **model_kwargs,    # HACK: `generate` method may pass more kwargs
  +    ) -> torch.Tensor:
           """Returns model output.
           """
  -        output = self.model(
  -            input_ids,
  -            attention_mask=attention_mask,
  -            **model_kwargs
  -        )
  +        output = self.model(input_ids, attention_mask=attention_mask, **model_kwargs)
           return output
  
verification_info: {"golden_diff": "diff --git a/applications/Chat/coati/models/base/actor.py b/applications/Chat/coati/models/base/actor.py\n--- a/applications/Chat/coati/models/base/actor.py\n+++ b/applications/Chat/coati/models/base/actor.py\n@@ -21,16 +21,13 @@\n         self.model = model\n         self.convert_to_lora()\n \n-    def forward(self,\n-                input_ids: torch.LongTensor,\n-                attention_mask: Optional[torch.Tensor] = None,\n-                **model_kwargs,  # HACK: `generate` method may pass more kwargs\n-                ) -> torch.Tensor:\n+    def forward(\n+            self,\n+            input_ids: torch.LongTensor,\n+            attention_mask: Optional[torch.Tensor] = None,\n+            **model_kwargs,    # HACK: `generate` method may pass more kwargs\n+    ) -> torch.Tensor:\n         \"\"\"Returns model output.\n         \"\"\"\n-        output = self.model(\n-            input_ids,\n-            attention_mask=attention_mask,\n-            **model_kwargs\n-        )\n+        output = self.model(input_ids, attention_mask=attention_mask, **model_kwargs)\n         return output\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\nimport torch\nimport torch.nn as nn\n\nfrom ..lora import LoRAModule\n\n\nclass Actor(LoRAModule):\n    \"\"\"\n    Actor model base class.\n\n    Args:\n        model (nn.Module): Actor Model.\n        lora_rank (int): LoRA rank.\n        lora_train_bias (str): LoRA bias training mode.\n    \"\"\"\n\n    def __init__(self, model: nn.Module, lora_rank: int = 0, lora_train_bias: str = 'none') -> None:\n        super().__init__(lora_rank=lora_rank, lora_train_bias=lora_train_bias)\n        self.model = model\n        self.convert_to_lora()\n\n    def forward(self,\n                input_ids: torch.LongTensor,\n                attention_mask: Optional[torch.Tensor] = None,\n                **model_kwargs,  # HACK: `generate` method may pass more kwargs\n                ) -> torch.Tensor:\n        \"\"\"Returns model output.\n        \"\"\"\n        output = self.model(\n            input_ids,\n            attention_mask=attention_mask,\n            **model_kwargs\n        )\n        return output\n", "path": "applications/Chat/coati/models/base/actor.py"}]}