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 colossalai/booster/mixed_precision/mixed_precision_base.py]
  1 from abc import ABC, abstractmethod
  2 from typing import Callable, Optional, Tuple
  3 
  4 import torch.nn as nn
  5 from torch.optim import Optimizer
  6 
  7 from colossalai.interface import OptimizerWrapper
  8 
  9 
  10 class MixedPrecision(ABC):
  11     """
  12     An abstract class for mixed precision training.
  13     """
  14 
  15     @abstractmethod
  16     def configure(self,
  17                   model: nn.Module,
  18                   optimizer: Optional[Optimizer] = None,
  19                   criterion: Optional[Callable] = None,
  20                   ) -> Tuple[nn.Module, OptimizerWrapper, Callable]:
  21         # TODO: implement this method
  22         pass
  23
  [end of colossalai/booster/mixed_precision/mixed_precision_base.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/colossalai/booster/mixed_precision/mixed_precision_base.py b/colossalai/booster/mixed_precision/mixed_precision_base.py
  --- a/colossalai/booster/mixed_precision/mixed_precision_base.py
  +++ b/colossalai/booster/mixed_precision/mixed_precision_base.py
  @@ -13,10 +13,11 @@
       """
   
       @abstractmethod
  -    def configure(self,
  -                  model: nn.Module,
  -                  optimizer: Optional[Optimizer] = None,
  -                  criterion: Optional[Callable] = None,
  -                  ) -> Tuple[nn.Module, OptimizerWrapper, Callable]:
  +    def configure(
  +        self,
  +        model: nn.Module,
  +        optimizer: Optional[Optimizer] = None,
  +        criterion: Optional[Callable] = None,
  +    ) -> Tuple[nn.Module, OptimizerWrapper, Callable]:
           # TODO: implement this method
           pass
  
verification_info: {"golden_diff": "diff --git a/colossalai/booster/mixed_precision/mixed_precision_base.py b/colossalai/booster/mixed_precision/mixed_precision_base.py\n--- a/colossalai/booster/mixed_precision/mixed_precision_base.py\n+++ b/colossalai/booster/mixed_precision/mixed_precision_base.py\n@@ -13,10 +13,11 @@\n     \"\"\"\n \n     @abstractmethod\n-    def configure(self,\n-                  model: nn.Module,\n-                  optimizer: Optional[Optimizer] = None,\n-                  criterion: Optional[Callable] = None,\n-                  ) -> Tuple[nn.Module, OptimizerWrapper, Callable]:\n+    def configure(\n+        self,\n+        model: nn.Module,\n+        optimizer: Optional[Optimizer] = None,\n+        criterion: Optional[Callable] = None,\n+    ) -> Tuple[nn.Module, OptimizerWrapper, Callable]:\n         # TODO: implement this method\n         pass\n", "issue": "[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n", "before_files": [{"content": "from abc import ABC, abstractmethod\nfrom typing import Callable, Optional, Tuple\n\nimport torch.nn as nn\nfrom torch.optim import Optimizer\n\nfrom colossalai.interface import OptimizerWrapper\n\n\nclass MixedPrecision(ABC):\n    \"\"\"\n    An abstract class for mixed precision training.\n    \"\"\"\n\n    @abstractmethod\n    def configure(self,\n                  model: nn.Module,\n                  optimizer: Optional[Optimizer] = None,\n                  criterion: Optional[Callable] = None,\n                  ) -> Tuple[nn.Module, OptimizerWrapper, Callable]:\n        # TODO: implement this method\n        pass\n", "path": "colossalai/booster/mixed_precision/mixed_precision_base.py"}]}