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/auto_parallel/tensor_shard/deprecated/op_handler/strategy_generator.py]
  1 from dataclasses import dataclass
  2 from abc import ABC, abstractmethod
  3 from typing import List, Dict
  4 from colossalai.device.device_mesh import DeviceMesh
  5 
  6 __all__ = ['IntermediateStrategy', 'StrategyGenerator']
  7 
  8 
  9 @dataclass
  10 class IntermediateStrategy:
  11     """
  12     IntermediateStrategy contains the subset of meta information for ShardingStrategy. It is 
  13     to store the essential information regarding the tensor sharding and leave other meta information to OperatorHandler.
  14 
  15     Args:
  16         name (str): name of the sharding strategy.
  17         dim_partition_dict (Dict[Dict]): stores the tensor to dim partition dict mapping.
  18         all_reduce_dims (List[int]): stores the dimensions which require an all-reduce operation.
  19     """
  20     name: str
  21     dim_partition_dict: Dict[str, Dict[int, List[int]]]
  22     all_reduce_axis: List[int] = None
  23 
  24 
  25 class StrategyGenerator(ABC):
  26     """
  27     StrategyGenerator is used to generate the same group of sharding strategies. 
  28     """
  29 
  30     def __init__(self, device_mesh: DeviceMesh):
  31         self.device_mesh = device_mesh
  32 
  33     @abstractmethod
  34     def generate(self) -> List[IntermediateStrategy]:
  35         """
  36         """
  37         pass
  38 
  39     @abstractmethod
  40     def validate(self, *args, **kwargs) -> bool:
  41         """
  42         Validate if the operands are of desired shape. 
  43         If True, means this generator can be used for the current operation.
  44         """
  45         pass
  46
  [end of colossalai/auto_parallel/tensor_shard/deprecated/op_handler/strategy_generator.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/auto_parallel/tensor_shard/deprecated/op_handler/strategy_generator.py b/colossalai/auto_parallel/tensor_shard/deprecated/op_handler/strategy_generator.py
  --- a/colossalai/auto_parallel/tensor_shard/deprecated/op_handler/strategy_generator.py
  +++ b/colossalai/auto_parallel/tensor_shard/deprecated/op_handler/strategy_generator.py
  @@ -1,6 +1,7 @@
  -from dataclasses import dataclass
   from abc import ABC, abstractmethod
  -from typing import List, Dict
  +from dataclasses import dataclass
  +from typing import Dict, List
  +
   from colossalai.device.device_mesh import DeviceMesh
   
   __all__ = ['IntermediateStrategy', 'StrategyGenerator']
  @@ -9,7 +10,7 @@
   @dataclass
   class IntermediateStrategy:
       """
  -    IntermediateStrategy contains the subset of meta information for ShardingStrategy. It is 
  +    IntermediateStrategy contains the subset of meta information for ShardingStrategy. It is
       to store the essential information regarding the tensor sharding and leave other meta information to OperatorHandler.
   
       Args:
  @@ -24,7 +25,7 @@
   
   class StrategyGenerator(ABC):
       """
  -    StrategyGenerator is used to generate the same group of sharding strategies. 
  +    StrategyGenerator is used to generate the same group of sharding strategies.
       """
   
       def __init__(self, device_mesh: DeviceMesh):
  @@ -39,7 +40,7 @@
       @abstractmethod
       def validate(self, *args, **kwargs) -> bool:
           """
  -        Validate if the operands are of desired shape. 
  +        Validate if the operands are of desired shape.
           If True, means this generator can be used for the current operation.
           """
           pass
  
verification_info: {"golden_diff": "diff --git a/colossalai/auto_parallel/tensor_shard/deprecated/op_handler/strategy_generator.py b/colossalai/auto_parallel/tensor_shard/deprecated/op_handler/strategy_generator.py\n--- a/colossalai/auto_parallel/tensor_shard/deprecated/op_handler/strategy_generator.py\n+++ b/colossalai/auto_parallel/tensor_shard/deprecated/op_handler/strategy_generator.py\n@@ -1,6 +1,7 @@\n-from dataclasses import dataclass\n from abc import ABC, abstractmethod\n-from typing import List, Dict\n+from dataclasses import dataclass\n+from typing import Dict, List\n+\n from colossalai.device.device_mesh import DeviceMesh\n \n __all__ = ['IntermediateStrategy', 'StrategyGenerator']\n@@ -9,7 +10,7 @@\n @dataclass\n class IntermediateStrategy:\n     \"\"\"\n-    IntermediateStrategy contains the subset of meta information for ShardingStrategy. It is \n+    IntermediateStrategy contains the subset of meta information for ShardingStrategy. It is\n     to store the essential information regarding the tensor sharding and leave other meta information to OperatorHandler.\n \n     Args:\n@@ -24,7 +25,7 @@\n \n class StrategyGenerator(ABC):\n     \"\"\"\n-    StrategyGenerator is used to generate the same group of sharding strategies. \n+    StrategyGenerator is used to generate the same group of sharding strategies.\n     \"\"\"\n \n     def __init__(self, device_mesh: DeviceMesh):\n@@ -39,7 +40,7 @@\n     @abstractmethod\n     def validate(self, *args, **kwargs) -> bool:\n         \"\"\"\n-        Validate if the operands are of desired shape. \n+        Validate if the operands are of desired shape.\n         If True, means this generator can be used for the current operation.\n         \"\"\"\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 dataclasses import dataclass\nfrom abc import ABC, abstractmethod\nfrom typing import List, Dict\nfrom colossalai.device.device_mesh import DeviceMesh\n\n__all__ = ['IntermediateStrategy', 'StrategyGenerator']\n\n\n@dataclass\nclass IntermediateStrategy:\n    \"\"\"\n    IntermediateStrategy contains the subset of meta information for ShardingStrategy. It is \n    to store the essential information regarding the tensor sharding and leave other meta information to OperatorHandler.\n\n    Args:\n        name (str): name of the sharding strategy.\n        dim_partition_dict (Dict[Dict]): stores the tensor to dim partition dict mapping.\n        all_reduce_dims (List[int]): stores the dimensions which require an all-reduce operation.\n    \"\"\"\n    name: str\n    dim_partition_dict: Dict[str, Dict[int, List[int]]]\n    all_reduce_axis: List[int] = None\n\n\nclass StrategyGenerator(ABC):\n    \"\"\"\n    StrategyGenerator is used to generate the same group of sharding strategies. \n    \"\"\"\n\n    def __init__(self, device_mesh: DeviceMesh):\n        self.device_mesh = device_mesh\n\n    @abstractmethod\n    def generate(self) -> List[IntermediateStrategy]:\n        \"\"\"\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def validate(self, *args, **kwargs) -> bool:\n        \"\"\"\n        Validate if the operands are of desired shape. \n        If True, means this generator can be used for the current operation.\n        \"\"\"\n        pass\n", "path": "colossalai/auto_parallel/tensor_shard/deprecated/op_handler/strategy_generator.py"}]}