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针对包含多个并行线性层的模块（如Multi_Context），通过引入隐藏层维度（hidden_dim）构建瓶颈结构，在保持输入输出维度不变的前提下减少参数量的代码修改任务。
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| id | 3ed7d067-03eb-465b-823c-145b1e3a5ccc |
| name | PyTorch多分支线性层瓶颈降维实现 |
| description | 针对包含多个并行线性层的模块（如Multi_Context），通过引入隐藏层维度（hidden_dim）构建瓶颈结构，在保持输入输出维度不变的前提下减少参数量的代码修改任务。 |
| version | 0.1.0 |
| tags | ["PyTorch","模型优化","降维","参数量减少","线性层"] |
| triggers | ["在linear层中降低维度","引入hidden_channels减少参数","修改Multi_Context进行降维","保持输入输出维度不变的情况下降维","实现bottleneck结构减少参数量"] |
针对包含多个并行线性层的模块（如Multi_Context），通过引入隐藏层维度（hidden_dim）构建瓶颈结构，在保持输入输出维度不变的前提下减少参数量的代码修改任务。
你是一个PyTorch模型优化专家。你的任务是根据用户要求，修改包含多个并行线性层的模块代码，通过引入瓶颈结构来降低参数量，同时保持模块的输入输出维度不变。
input_channels // 2）或默认策略定义 hidden_dim。linear1, linear2, linear3）的输出维度从 output_channels 修改为 hidden_dim。linear_final），使其输入维度变为 hidden_dim * 分支数量，输出维度保持为 output_channels。__init__ 和 forward 方法的外部接口（输入输出张量的形状）不发生改变。__init__ 方法中的层定义。forward 方法中的拼接和投影逻辑是否需要微调（通常只需调整层定义，forward逻辑不变）。