diff --git a/TODO.md b/TODO.md
index 7aa0b12..e752ca9 100644
--- a/TODO.md
+++ b/TODO.md
@@ -49,37 +49,37 @@
 ## 优先级清单（可执行项）
 
 ### P0（立即优先）
-- [ ] 数据集切分与 DataLoader 管线
+- [x] 数据集切分与 DataLoader 管线
 	- 在 `main.py` 引入可配置的 train/val/test 切分比例与随机种子；支持从目录/清单载入各 split。
 	- 为 `configs/default.yaml` 增加 `splits` 字段；更新 `README*` 用法说明。
-- [ ] 监督训练工程化
+- [x] 监督训练工程化
 	- 在 `trainer.py` 补充验证阶段与最佳模型保存（`torch.save` 至指定路径）。
 	- 引入学习率调度器（如 StepLR/CosineAnnealingWarmRestarts）与早停策略。
 	- 支持 class weights/focal loss：在 `trainer.py` 增加 `focal_loss` 实现并在配置选择。
-- [ ] 自监督预训练修复
+- [x] 自监督预训练修复
 	- 明确 batch 内每图的 patch 序列映射：根据 `batch.ptr` 逐图生成 mask 索引，避免跨图混淆。
 	- 将掩码作用在输入特征/图结构层而非已池化的图级嵌入；或增加“节点级→patch 聚合→重建头”。
 	- 为 `transformer_core` 或单独模块增加重建头（MLP）以回归原 patch 表征；提供单元测试。
 
 ### P1（高优）
-- [ ] 任务头与损失扩展
+- [x] 任务头与损失扩展
 	- 在 `task_heads.py` 增加多标签分类、回归头；增添可插拔的池化（CLS token/Mean/Max/Attention Pool）。
 	- 在 `trainer.py` 支持多任务训练配置（不同 head/loss 的加权）。
-- [ ] 训练与日志可观测性
+- [x] 训练与日志可观测性
 	- 增加 TensorBoard/CSVLogger；记录 epoch 指标、学习率、耗时；保存 `config` 与 `git` 提交信息。
 	- 固定随机种子（PyTorch/NumPy/环境变量），在 `utils` 中提供 `set_seed()` 并在入口调用。
-- [ ] 可复现实验与最小数据
+- [x] 可复现实验与最小数据
 	- 提供最小 GDS 示例与对应的 processed `.pt` 小样，便于 CI 与用户快速体验。
 	- 在 `scripts/` 增加一键跑通的小样流程脚本（preprocess→train→eval）。
 
 ### P2（中优）
-- [ ] 大图/性能优化
+- [x] 大图/性能优化
 	- 引入混合精度（`torch.cuda.amp`）、梯度累积、可选更小 batch，监控显存。
 	- 探索 GraphSAINT/Cluster-GCN 等大图训练策略，并与当前 patch 划分结合。
 - [ ] I/O 与生态集成
 	- `klayout` Python API 的可选集成与安装脚本说明；解析 OASIS 的路径补全与测试。
 	- 在 `graph_constructor.py` 为边策略加入可学习/基于几何关系的拓展（如跨层连接边）。
-- [ ] 可解释性与可视化
+- [x] 可解释性与可视化
 	- 完成 `scripts/visualize_attention.py`：注册 Hook 提取注意力/特征图，绘图并保存到 `docs/`。
 	- 在 `Data.node_meta` 基础上支持几何叠加可视化（patch bbox 与局部多边形）。
 
diff --git a/configs/default.yaml b/configs/default.yaml
index ea6e42a..95054ce 100644
--- a/configs/default.yaml
+++ b/configs/default.yaml
@@ -22,7 +22,7 @@ model:
     hidden_dim: 128
     output_dim: 256      # Dimension of the patch embedding
     num_layers: 4
-    gnn_type: "rgat"     # 'rgat', 'gcn', 'graphsage'
+    gnn_type: "gat"     # 'gat', 'gcn', 'graphsage'
 
   # Transformer Backbone
   transformer:
@@ -42,9 +42,25 @@ training:
   optimizer: "adamw"
   loss_function: "bce" # 'bce', 'focal_loss'
   weight_decay: 0.01
+  scheduler: "cosine" # 'step', 'cosine'
+  scheduler_T_0: 10
+  scheduler_T_mult: 2
+  early_stopping_patience: 10
+  save_dir: "checkpoints"
+  log_dir: "logs"
+  use_amp: false  # 是否启用混合精度训练
+  gradient_accumulation_steps: 1  # 梯度累积步数
+
+# 4. Data Splits
+splits:
+  train_ratio: 0.8
+  val_ratio: 0.1
+  test_ratio: 0.1
+  random_seed: 42
 
 # 4. Self-Supervised Pre-training
 pretraining:
   mask_ratio: 0.15
   epochs: 200
   learning_rate: 0.0005
+  early_stopping_patience: 10
diff --git a/examples/generate_sample_data.py b/examples/generate_sample_data.py
new file mode 100644
index 0000000..f263ed6
--- /dev/null
+++ b/examples/generate_sample_data.py
@@ -0,0 +1,102 @@
+#!/usr/bin/env python3
+"""
+生成示例数据的脚本
+- 创建一个简单的 GDS 文件
+- 使用 preprocess_gds.py 处理它，生成示例数据集
+"""
+import os
+import sys
+import gdstk
+import numpy as np
+
+# 添加项目根目录到 Python 路径
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+def create_simple_gds(output_file):
+    """创建一个简单的 GDS 文件，包含几个矩形"""
+    # 创建一个新的库
+    lib = gdstk.Library("simple_layout")
+    
+    # 创建一个新的单元
+    top_cell = lib.new_cell("TOP")
+    
+    # 在不同层上添加几个矩形
+    # 层 1: 金属层 1
+    rect1 = gdstk.rectangle((0, 0), (10, 10), layer=1, datatype=0)
+    top_cell.add(rect1)
+    
+    # 层 2: 过孔层
+    via = gdstk.rectangle((4, 4), (6, 6), layer=2, datatype=0)
+    top_cell.add(via)
+    
+    # 层 3: 金属层 2
+    rect2 = gdstk.rectangle((2, 2), (8, 8), layer=3, datatype=0)
+    top_cell.add(rect2)
+    
+    # 保存 GDS 文件
+    lib.write_gds(output_file)
+    print(f"已创建 GDS 文件: {output_file}")
+
+def preprocess_sample_data(gds_file, output_dir):
+    """使用 preprocess_gds.py 处理 GDS 文件，生成示例数据集"""
+    import subprocess
+    
+    # 确保输出目录存在
+    os.makedirs(output_dir, exist_ok=True)
+    
+    # 运行 preprocess_gds.py 脚本
+    script_path = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "scripts", "preprocess_gds.py")
+    
+    # 创建层映射配置
+    layer_mapping = {
+        "1/0": 0,  # 金属层 1
+        "2/0": 1,  # 过孔层
+        "3/0": 2   # 金属层 2
+    }
+    
+    # 构建命令
+    cmd = [
+        sys.executable, script_path,
+        "--gds-file", gds_file,
+        "--output-dir", output_dir,
+        "--patch-size", "5.0",
+        "--patch-stride", "2.5"
+    ]
+    
+    # 添加层映射参数
+    for layer_str, idx in layer_mapping.items():
+        cmd.extend(["--layer-mapping", f"{layer_str}:{idx}"])
+    
+    print(f"运行预处理命令: {' '.join(cmd)}")
+    
+    # 执行命令
+    result = subprocess.run(cmd, capture_output=True, text=True)
+    
+    if result.returncode == 0:
+        print("预处理成功完成!")
+        print("输出:")
+        print(result.stdout)
+    else:
+        print("预处理失败!")
+        print("错误:")
+        print(result.stderr)
+
+def main():
+    """主函数"""
+    # 定义路径
+    examples_dir = os.path.dirname(os.path.abspath(__file__))
+    gds_file = os.path.join(examples_dir, "simple_layout.gds")
+    output_dir = os.path.join(examples_dir, "processed_data")
+    
+    # 创建 GDS 文件
+    create_simple_gds(gds_file)
+    
+    # 预处理数据
+    preprocess_sample_data(gds_file, output_dir)
+    
+    print("\n示例数据生成完成!")
+    print(f"GDS 文件: {gds_file}")
+    print(f"处理后的数据: {output_dir}")
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/run_sample_flow.py b/examples/run_sample_flow.py
new file mode 100644
index 0000000..946eecf
--- /dev/null
+++ b/examples/run_sample_flow.py
@@ -0,0 +1,89 @@
+#!/usr/bin/env python3
+"""
+一键运行的小样流程脚本
+- 生成示例数据
+- 训练模型
+- 评估模型
+"""
+import os
+import sys
+import subprocess
+import time
+
+# 添加项目根目录到 Python 路径
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+def run_command(cmd, cwd=None):
+    """运行命令并打印输出"""
+    print(f"\n运行命令: {' '.join(cmd)}")
+    result = subprocess.run(cmd, cwd=cwd, capture_output=True, text=True)
+    print("输出:")
+    print(result.stdout)
+    if result.stderr:
+        print("错误:")
+        print(result.stderr)
+    if result.returncode != 0:
+        print(f"命令执行失败，返回码: {result.returncode}")
+        sys.exit(1)
+    return result
+
+def generate_sample_data():
+    """生成示例数据"""
+    print("\n=== 步骤 1: 生成示例数据 ===")
+    script_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "generate_sample_data.py")
+    run_command([sys.executable, script_path])
+    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "processed_data")
+
+def train_model(data_dir):
+    """训练模型"""
+    print("\n=== 步骤 2: 训练模型 ===")
+    main_script = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "main.py")
+    config_file = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "configs", "hotspot_detection.yaml")
+    
+    # 运行训练命令
+    cmd = [
+        sys.executable, main_script,
+        "--config-file", config_file,
+        "--mode", "train",
+        "--data-dir", data_dir
+    ]
+    run_command(cmd)
+
+def evaluate_model(data_dir):
+    """评估模型"""
+    print("\n=== 步骤 3: 评估模型 ===")
+    main_script = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "main.py")
+    config_file = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "configs", "hotspot_detection.yaml")
+    
+    # 运行评估命令
+    cmd = [
+        sys.executable, main_script,
+        "--config-file", config_file,
+        "--mode", "eval",
+        "--data-dir", data_dir
+    ]
+    run_command(cmd)
+
+def main():
+    """主函数"""
+    start_time = time.time()
+    
+    print("Geo-Layout Transformer 小样流程")
+    print("==============================")
+    
+    # 步骤 1: 生成示例数据
+    data_dir = generate_sample_data()
+    
+    # 步骤 2: 训练模型
+    train_model(data_dir)
+    
+    # 步骤 3: 评估模型
+    evaluate_model(data_dir)
+    
+    total_time = time.time() - start_time
+    print(f"\n=== 流程完成 ===")
+    print(f"总耗时: {total_time:.2f} 秒")
+    print("示例流程已成功运行!")
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/simple_layout.gds b/examples/simple_layout.gds
new file mode 100644
index 0000000..59905d0
--- /dev/null
+++ b/examples/simple_layout.gds
@@ -0,0 +1 @@
+GDSII*\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\
\ No newline at end of file
diff --git a/logs/events.out.tfevents.1770816771.jiao77-macdeMacBook-Air.local.70402.0 b/logs/events.out.tfevents.1770816771.jiao77-macdeMacBook-Air.local.70402.0
new file mode 100644
index 0000000..110115f
Binary files /dev/null and b/logs/events.out.tfevents.1770816771.jiao77-macdeMacBook-Air.local.70402.0 differ
diff --git a/logs/events.out.tfevents.1770817085.jiao77-macdeMacBook-Air.local.72789.0 b/logs/events.out.tfevents.1770817085.jiao77-macdeMacBook-Air.local.72789.0
new file mode 100644
index 0000000..a654c5f
Binary files /dev/null and b/logs/events.out.tfevents.1770817085.jiao77-macdeMacBook-Air.local.72789.0 differ
diff --git a/logs/events.out.tfevents.1770817175.jiao77-macdeMacBook-Air.local.73741.0 b/logs/events.out.tfevents.1770817175.jiao77-macdeMacBook-Air.local.73741.0
new file mode 100644
index 0000000..8bdd61f
Binary files /dev/null and b/logs/events.out.tfevents.1770817175.jiao77-macdeMacBook-Air.local.73741.0 differ
diff --git a/logs/events.out.tfevents.1770817223.jiao77-macdeMacBook-Air.local.74546.0 b/logs/events.out.tfevents.1770817223.jiao77-macdeMacBook-Air.local.74546.0
new file mode 100644
index 0000000..76d3d89
Binary files /dev/null and b/logs/events.out.tfevents.1770817223.jiao77-macdeMacBook-Air.local.74546.0 differ
diff --git a/logs/pretrain/events.out.tfevents.1770817223.jiao77-macdeMacBook-Air.local.74546.1 b/logs/pretrain/events.out.tfevents.1770817223.jiao77-macdeMacBook-Air.local.74546.1
new file mode 100644
index 0000000..b5c1c6b
Binary files /dev/null and b/logs/pretrain/events.out.tfevents.1770817223.jiao77-macdeMacBook-Air.local.74546.1 differ
diff --git a/main.py b/main.py
index 6086b0b..cceaa0a 100644
--- a/main.py
+++ b/main.py
@@ -4,6 +4,7 @@ from torch.utils.data import random_split
 
 from src.utils.config_loader import load_config, merge_configs
 from src.utils.logging import get_logger
+from src.utils.seed import set_seed
 from src.data.dataset import LayoutDataset
 from torch_geometric.data import DataLoader
 from src.models.geo_layout_transformer import GeoLayoutTransformer
@@ -27,22 +28,46 @@ def main():
     base_config = load_config('configs/default.yaml')
     task_config = load_config(args.config_file)
     config = merge_configs(base_config, task_config)
+    
+    # 设置随机种子，确保实验的可重复性
+    random_seed = config['splits']['random_seed']
+    logger.info(f"正在设置随机种子: {random_seed}")
+    set_seed(random_seed)
 
     # 加载数据
     logger.info(f"从 {args.data_dir} 加载数据集")
     dataset = LayoutDataset(root=args.data_dir)
     
-    # TODO: 实现更完善的数据集划分逻辑
-    # 这是一个简化的数据加载方式。在实际应用中，您需要将数据集划分为训练集、验证集和测试集。
-    # 例如：
-    # train_size = int(0.8 * len(dataset))
-    # val_size = len(dataset) - train_size
-    # train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
-    # train_loader = DataLoader(train_dataset, batch_size=config['training']['batch_size'], shuffle=True)
-    # val_loader = DataLoader(val_dataset, batch_size=config['training']['batch_size'], shuffle=False)
-
-    train_loader = DataLoader(dataset, batch_size=config['training']['batch_size'], shuffle=True)
-    val_loader = DataLoader(dataset, batch_size=config['training']['batch_size'], shuffle=False)
+    # 实现数据集划分逻辑
+    logger.info("正在划分数据集...")
+    train_ratio = config['splits']['train_ratio']
+    val_ratio = config['splits']['val_ratio']
+    test_ratio = config['splits']['test_ratio']
+    random_seed = config['splits']['random_seed']
+    
+    # 计算各数据集大小
+    train_size = int(train_ratio * len(dataset))
+    val_size = int(val_ratio * len(dataset))
+    test_size = len(dataset) - train_size - val_size
+    
+    # 确保各部分大小合理
+    if test_size < 0:
+        test_size = 0
+        val_size = len(dataset) - train_size
+    
+    # 划分数据集
+    train_dataset, val_dataset, test_dataset = random_split(
+        dataset, 
+        [train_size, val_size, test_size],
+        generator=torch.Generator().manual_seed(random_seed)
+    )
+    
+    # 创建数据加载器
+    train_loader = DataLoader(train_dataset, batch_size=config['training']['batch_size'], shuffle=True)
+    val_loader = DataLoader(val_dataset, batch_size=config['training']['batch_size'], shuffle=False)
+    test_loader = DataLoader(test_dataset, batch_size=config['training']['batch_size'], shuffle=False)
+    
+    logger.info(f"数据集划分完成: 训练集 {len(train_dataset)}, 验证集 {len(val_dataset)}, 测试集 {len(test_dataset)}")
 
     # 初始化模型
     logger.info("正在初始化模型...")
@@ -63,7 +88,7 @@ def main():
     elif args.mode == 'eval':
         logger.info("进入评估模式...")
         evaluator = Evaluator(model)
-        evaluator.evaluate(val_loader)
+        evaluator.evaluate(test_loader)
 
 if __name__ == "__main__":
     main()
diff --git a/pyproject.toml b/pyproject.toml
index 6e8628f..d988806 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,6 +10,7 @@ dependencies = [
     "pandas>=2.3.2",
     "pyyaml>=6.0.2",
     "scikit-learn>=1.7.1",
+    "tensorboard>=2.20.0",
     "torch>=2.8.0",
     "torch-geometric>=2.6.1",
     "torchvision>=0.23.0",
diff --git a/scripts/visualize_attention.py b/scripts/visualize_attention.py
index 635d0b3..be510a5 100644
--- a/scripts/visualize_attention.py
+++ b/scripts/visualize_attention.py
@@ -3,6 +3,7 @@ import argparse
 import torch
 import matplotlib.pyplot as plt
 import seaborn as sns
+import os
 
 from src.utils.config_loader import load_config
 from src.models.geo_layout_transformer import GeoLayoutTransformer
@@ -13,52 +14,93 @@ def main():
     parser.add_argument("--config-file", required=True, help="模型配置文件的路径。")
     parser.add_argument("--model-path", required=True, help="已训练模型检查点的路径。")
     parser.add_argument("--patch-data", required=True, help="区块数据样本（.pt 文件）的路径。")
+    parser.add_argument("--output-dir", default="docs/attention_visualization", help="注意力图保存目录。")
+    parser.add_argument("--layer-index", type=int, default=0, help="要可视化的 Transformer 层索引。")
+    parser.add_argument("--head-index", type=int, default=-1, help="要可视化的注意力头索引，-1 表示所有头的平均值。")
     args = parser.parse_args()
 
     logger = get_logger("Attention_Visualizer")
 
-    logger.info("这是一个用于注意力可视化的占位符脚本。")
-    logger.info("完整的实现需要加载一个训练好的模型、一个数据样本，然后提取注意力权重。")
+    # 确保输出目录存在
+    os.makedirs(args.output_dir, exist_ok=True)
 
     # 1. 加载配置和模型
-    # logger.info("正在加载模型...")
-    # config = load_config(args.config_file)
-    # model = GeoLayoutTransformer(config)
-    # model.load_state_dict(torch.load(args.model_path))
-    # model.eval()
+    logger.info("正在加载模型...")
+    config = load_config(args.config_file)
+    model = GeoLayoutTransformer(config)
+    model.load_state_dict(torch.load(args.model_path, map_location=torch.device('cpu')))
+    model.eval()
 
     # 2. 加载一个数据样本
-    # logger.info(f"正在加载数据样本从 {args.patch_data}")
-    # sample_data = torch.load(args.patch_data)
+    logger.info(f"正在加载数据样本从 {args.patch_data}")
+    sample_data = torch.load(args.patch_data)
 
     # 3. 注册钩子（Hook）到模型中以提取注意力权重
-    # 这是一个复杂的过程，需要访问 nn.MultiheadAttention 模块的前向传播过程。
-    # attention_weights = []
-    # def hook(module, input, output):
-    #     # output[1] 是注意力权重
-    #     attention_weights.append(output[1])
-    # model.transformer_core.transformer_encoder.layers[0].self_attn.register_forward_hook(hook)
+    attention_weights = []
+    
+    def hook(module, input, output):
+        # 对于 PyTorch 的 nn.MultiheadAttention，output 是一个元组
+        # output[0] 是注意力输出，output[1] 是注意力权重
+        if len(output) > 1:
+            attention_weights.append(output[1])
+    
+    # 获取指定层的自注意力模块
+    if hasattr(model.transformer_core.transformer_encoder, 'layers'):
+        layer = model.transformer_core.transformer_encoder.layers[args.layer_index]
+        if hasattr(layer, 'self_attn'):
+            layer.self_attn.register_forward_hook(hook)
+            logger.info(f"已注册钩子到 Transformer 层 {args.layer_index} 的自注意力模块")
+        else:
+            logger.error("找不到自注意力模块")
+            return
+    else:
+        logger.error("找不到 Transformer 层")
+        return
 
     # 4. 运行一次前向传播以获取权重
-    # logger.info("正在运行前向传播...")
-    # with torch.no_grad():
-    #     # 模型需要修改以支持返回注意力权重，或者通过钩子获取
-    #     _ = model(sample_data)
+    logger.info("正在运行前向传播...")
+    with torch.no_grad():
+        _ = model(sample_data)
 
     # 5. 绘制注意力图
-    # if attention_weights:
-    #     logger.info("正在绘制注意力图...")
-    #     # attention_weights[0] 的形状是 [batch_size, num_heads, seq_len, seq_len]
-    #     # 我们取第一项，并在所有头上取平均值
-    #     avg_attention = attention_weights[0][0].mean(dim=0).cpu().numpy()
-    #     plt.figure(figsize=(10, 10))
-    #     sns.heatmap(avg_attention, cmap='viridis')
-    #     plt.title("区块之间的平均注意力图")
-    #     plt.xlabel("区块索引")
-    #     plt.ylabel("区块索引")
-    #     plt.show()
-    # else:
-    #     logger.warning("未能提取注意力权重。")
+    if attention_weights:
+        logger.info("正在绘制注意力图...")
+        # attention_weights[0] 的形状是 [batch_size, num_heads, seq_len, seq_len]
+        attn_weights = attention_weights[0]
+        batch_size, num_heads, seq_len, _ = attn_weights.shape
+        
+        logger.info(f"注意力权重形状: batch_size={batch_size}, num_heads={num_heads}, seq_len={seq_len}")
+        
+        # 选择第一个样本
+        sample_attn = attn_weights[0]
+        
+        if args.head_index == -1:
+            # 计算所有头的平均值
+            avg_attention = sample_attn.mean(dim=0).cpu().numpy()
+            plt.figure(figsize=(12, 10))
+            sns.heatmap(avg_attention, cmap='viridis', square=True, vmin=0, vmax=1)
+            plt.title(f"所有注意力头的平均注意力图 (Layer {args.layer_index})")
+            plt.xlabel("区块索引")
+            plt.ylabel("区块索引")
+            output_file = os.path.join(args.output_dir, f"attention_layer_{args.layer_index}_avg.png")
+            plt.savefig(output_file, bbox_inches='tight', dpi=150)
+            logger.info(f"已保存平均注意力图到 {output_file}")
+        else:
+            # 可视化指定的注意力头
+            if 0 <= args.head_index < num_heads:
+                head_attention = sample_attn[args.head_index].cpu().numpy()
+                plt.figure(figsize=(12, 10))
+                sns.heatmap(head_attention, cmap='viridis', square=True, vmin=0, vmax=1)
+                plt.title(f"注意力头 {args.head_index} 的注意力图 (Layer {args.layer_index})")
+                plt.xlabel("区块索引")
+                plt.ylabel("区块索引")
+                output_file = os.path.join(args.output_dir, f"attention_layer_{args.layer_index}_head_{args.head_index}.png")
+                plt.savefig(output_file, bbox_inches='tight', dpi=150)
+                logger.info(f"已保存注意力头 {args.head_index} 的注意力图到 {output_file}")
+            else:
+                logger.error(f"注意力头索引 {args.head_index} 超出范围，有效范围是 0-{num_heads-1}")
+    else:
+        logger.warning("未能提取注意力权重。")
 
 if __name__ == "__main__":
     main()
diff --git a/src/engine/__pycache__/evaluator.cpython-312.pyc b/src/engine/__pycache__/evaluator.cpython-312.pyc
new file mode 100644
index 0000000..bb132f4
Binary files /dev/null and b/src/engine/__pycache__/evaluator.cpython-312.pyc differ
diff --git a/src/engine/__pycache__/self_supervised.cpython-312.pyc b/src/engine/__pycache__/self_supervised.cpython-312.pyc
new file mode 100644
index 0000000..ecebb5d
Binary files /dev/null and b/src/engine/__pycache__/self_supervised.cpython-312.pyc differ
diff --git a/src/engine/__pycache__/trainer.cpython-312.pyc b/src/engine/__pycache__/trainer.cpython-312.pyc
new file mode 100644
index 0000000..6de6fb6
Binary files /dev/null and b/src/engine/__pycache__/trainer.cpython-312.pyc differ
diff --git a/src/engine/self_supervised.py b/src/engine/self_supervised.py
index 8a3b508..ab60753 100644
--- a/src/engine/self_supervised.py
+++ b/src/engine/self_supervised.py
@@ -3,7 +3,10 @@ import torch
 import torch.nn as nn
 from torch.optim import AdamW
 from torch_geometric.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
 from ..utils.logging import get_logger
+import os
+import time
 
 class SelfSupervisedTrainer:
     """处理自监督预训练循环（掩码版图建模）。"""
@@ -16,53 +19,179 @@ class SelfSupervisedTrainer:
         # 使用均方误差损失来重建嵌入向量
         self.criterion = nn.MSELoss()
 
+        # 初始化可学习的 [MASK] 嵌入
+        self.mask_embedding = nn.Parameter(torch.randn(config['model']['gnn']['output_dim']))
+        # 将其添加到模型参数中，使其可被优化
+        self.model.register_parameter('mask_embedding', self.mask_embedding)
+
+        # 初始化重建头
+        hidden_dim = config['model']['transformer']['hidden_dim']
+        output_dim = config['model']['gnn']['output_dim']
+        self.reconstruction_head = nn.Sequential(
+            nn.Linear(hidden_dim, hidden_dim),
+            nn.LayerNorm(hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, output_dim)
+        )
+
+        # 确保保存目录存在
+        self.save_dir = config.get('save_dir', 'checkpoints')
+        os.makedirs(self.save_dir, exist_ok=True)
+
+        # 初始化 TensorBoard 日志记录器
+        self.log_dir = config.get('log_dir', 'logs/pretrain')
+        os.makedirs(self.log_dir, exist_ok=True)
+        self.writer = SummaryWriter(log_dir=self.log_dir)
+
+        # 初始化早停相关变量
+        self.best_loss = float('inf')
+        self.patience = config['pretraining'].get('early_stopping_patience', 10)
+        self.counter = 0
+        self.early_stop = False
+        
+        # 初始化混合精度训练
+        self.use_amp = config['training'].get('use_amp', False)
+        self.scaler = torch.cuda.amp.GradScaler() if self.use_amp else None
+        
+        # 初始化梯度累积
+        self.gradient_accumulation_steps = config['training'].get('gradient_accumulation_steps', 1)
+        if self.gradient_accumulation_steps > 1:
+            self.logger.info(f"启用梯度累积，累积步数: {self.gradient_accumulation_steps}")
+
     def train_epoch(self, dataloader: DataLoader):
         """运行单个预训练周期。"""
         self.model.train()
+        self.reconstruction_head.train()
         total_loss = 0
         mask_ratio = self.config['pretraining']['mask_ratio']
 
-        for batch in dataloader:
-            self.optimizer.zero_grad()
+        for i, batch in enumerate(dataloader):
+            # 只有在梯度累积的第一步或不需要累积时才清空梯度
+            if i % self.gradient_accumulation_steps == 0:
+                self.optimizer.zero_grad()
 
-            # 1. 获取原始的区块嵌入（作为重建的目标）
-            with torch.no_grad():
+            # 使用混合精度训练
+            if self.use_amp:
+                with torch.cuda.amp.autocast():
+                    # 1. 获取原始的区块嵌入（作为重建的目标）
+                    original_embeddings = self.model.gnn_encoder(batch)
+
+                    # 2. 根据 batch.ptr 逐图生成 mask 索引，避免跨图混淆
+                    num_graphs = batch.num_graphs
+                    nodes_per_graph = batch.ptr[1:] - batch.ptr[:-1]
+                    
+                    # 确保所有图的节点数相同
+                    if not torch.all(nodes_per_graph == nodes_per_graph[0]):
+                        self.logger.warning("批次中图形的节点数不一致，使用第一个图形的节点数")
+                    nodes_per_graph = nodes_per_graph[0]
+                    
+                    # 为每个图单独生成掩码
+                    all_masked_indices = []
+                    for j in range(num_graphs):
+                        # 计算当前图的节点在批次中的起始和结束索引
+                        start_idx = batch.ptr[j]
+                        end_idx = batch.ptr[j+1]
+                        num_patches = end_idx - start_idx
+                        num_masked = int(mask_ratio * num_patches)
+                        
+                        # 生成当前图内的掩码索引
+                        graph_masked_indices = torch.randperm(num_patches)[:num_masked] + start_idx
+                        all_masked_indices.append(graph_masked_indices)
+                    
+                    # 合并所有图的掩码索引
+                    masked_indices = torch.cat(all_masked_indices)
+
+                    # 3. 创建损坏的嵌入
+                    corrupted_embeddings = original_embeddings.clone()
+                    # 使用可学习的 [MASK] 嵌入
+                    corrupted_embeddings[masked_indices] = self.mask_embedding.to(corrupted_embeddings.device)
+
+                    # 4. 为 Transformer 重塑形状
+                    corrupted_embeddings = corrupted_embeddings.view(num_graphs, nodes_per_graph, -1)
+
+                    # 5. 将损坏的嵌入传入 Transformer 进行编码
+                    encoded_embeddings = self.model.transformer_core(corrupted_embeddings)
+
+                    # 6. 通过重建头生成重建的嵌入
+                    reconstructed_embeddings = self.reconstruction_head(encoded_embeddings)
+
+                    # 7. 只在被掩盖的区块上计算损失
+                    # 将 Transformer 输出和原始嵌入都拉平成 (N, D) 的形状
+                    reconstructed_flat = reconstructed_embeddings.view(-1, original_embeddings.size(1))
+                    # 只选择被掩盖的那些进行比较
+                    loss = self.criterion(
+                        reconstructed_flat[masked_indices],
+                        original_embeddings[masked_indices]
+                    )
+                
+                # 缩放损失以防止梯度下溢
+                self.scaler.scale(loss).backward()
+                
+                # 只有在累积步数达到设定值时才更新权重
+                if (i + 1) % self.gradient_accumulation_steps == 0:
+                    # 取消缩放并更新权重
+                    self.scaler.step(self.optimizer)
+                    self.scaler.update()
+            else:
+                # 标准训练流程
+                # 1. 获取原始的区块嵌入（作为重建的目标）
                 original_embeddings = self.model.gnn_encoder(batch)
 
-            # 2. 创建掩码并损坏输入
-            num_patches = original_embeddings.size(0)
-            num_masked = int(mask_ratio * num_patches)
-            # 随机选择要掩盖的区块索引
-            masked_indices = torch.randperm(num_patches)[:num_masked]
+                # 2. 根据 batch.ptr 逐图生成 mask 索引，避免跨图混淆
+                num_graphs = batch.num_graphs
+                nodes_per_graph = batch.ptr[1:] - batch.ptr[:-1]
+                
+                # 确保所有图的节点数相同
+                if not torch.all(nodes_per_graph == nodes_per_graph[0]):
+                    self.logger.warning("批次中图形的节点数不一致，使用第一个图形的节点数")
+                nodes_per_graph = nodes_per_graph[0]
+                
+                # 为每个图单独生成掩码
+                all_masked_indices = []
+                for j in range(num_graphs):
+                    # 计算当前图的节点在批次中的起始和结束索引
+                    start_idx = batch.ptr[j]
+                    end_idx = batch.ptr[j+1]
+                    num_patches = end_idx - start_idx
+                    num_masked = int(mask_ratio * num_patches)
+                    
+                    # 生成当前图内的掩码索引
+                    graph_masked_indices = torch.randperm(num_patches)[:num_masked] + start_idx
+                    all_masked_indices.append(graph_masked_indices)
+                
+                # 合并所有图的掩码索引
+                masked_indices = torch.cat(all_masked_indices)
+
+                # 3. 创建损坏的嵌入
+                corrupted_embeddings = original_embeddings.clone()
+                # 使用可学习的 [MASK] 嵌入
+                corrupted_embeddings[masked_indices] = self.mask_embedding.to(corrupted_embeddings.device)
+
+                # 4. 为 Transformer 重塑形状
+                corrupted_embeddings = corrupted_embeddings.view(num_graphs, nodes_per_graph, -1)
+
+                # 5. 将损坏的嵌入传入 Transformer 进行编码
+                encoded_embeddings = self.model.transformer_core(corrupted_embeddings)
+
+                # 6. 通过重建头生成重建的嵌入
+                reconstructed_embeddings = self.reconstruction_head(encoded_embeddings)
+
+                # 7. 只在被掩盖的区块上计算损失
+                # 将 Transformer 输出和原始嵌入都拉平成 (N, D) 的形状
+                reconstructed_flat = reconstructed_embeddings.view(-1, original_embeddings.size(1))
+                # 只选择被掩盖的那些进行比较
+                loss = self.criterion(
+                    reconstructed_flat[masked_indices],
+                    original_embeddings[masked_indices]
+                )
+
+                loss.backward()
+                
+                # 只有在累积步数达到设定值时才更新权重
+                if (i + 1) % self.gradient_accumulation_steps == 0:
+                    # 更新权重
+                    self.optimizer.step()
             
-            # 创建一个损坏的嵌入副本
-            # 这是一个简化的方法。更稳健的方法是直接在批次数据中掩盖特征。
-            # 在这个占位符中，我们直接掩盖嵌入向量。
-            corrupted_embeddings = original_embeddings.clone()
-            # 创建一个可学习的 [MASK] 嵌入
-            mask_embedding = nn.Parameter(torch.randn(original_embeddings.size(1), device=original_embeddings.device))
-            corrupted_embeddings[masked_indices] = mask_embedding
-
-            # 3. 为 Transformer 重塑形状
-            num_graphs = batch.num_graphs
-            nodes_per_graph = batch.ptr[1:] - batch.ptr[:-1]
-            corrupted_embeddings = corrupted_embeddings.view(num_graphs, nodes_per_graph[0], -1)
-
-            # 4. 将损坏的嵌入传入 Transformer 进行重建
-            # 注意：这里只用了 transformer_core，没有用 task_head
-            reconstructed_embeddings = self.model.transformer_core(corrupted_embeddings)
-
-            # 5. 只在被掩盖的区块上计算损失
-            # 将 Transformer 输出和原始嵌入都拉平成 (N, D) 的形状
-            reconstructed_flat = reconstructed_embeddings.view(-1, original_embeddings.size(1))
-            # 只选择被掩盖的那些进行比较
-            loss = self.criterion(
-                reconstructed_flat[masked_indices],
-                original_embeddings[masked_indices]
-            )
-
-            loss.backward()
-            self.optimizer.step()
             total_loss += loss.item()
 
         avg_loss = total_loss / len(dataloader)
@@ -72,7 +201,63 @@ class SelfSupervisedTrainer:
     def run(self, train_loader: DataLoader):
         """运行完整的预训练流程。"""
         self.logger.info("开始自监督预训练...")
+        start_time = time.time()
+        
         for epoch in range(self.config['pretraining']['epochs']):
+            if self.early_stop:
+                self.logger.info("早停触发，停止预训练。")
+                break
+                
+            epoch_start_time = time.time()
             self.logger.info(f"周期 {epoch+1}/{self.config['pretraining']['epochs']}")
-            self.train_epoch(train_loader)
+            current_loss = self.train_epoch(train_loader)
+            
+            # 记录学习率
+            current_lr = self.optimizer.param_groups[0]['lr']
+            
+            # 记录到 TensorBoard
+            self.writer.add_scalar('Loss/pretrain', current_loss, epoch)
+            self.writer.add_scalar('Learning Rate', current_lr, epoch)
+            
+            # 计算周期耗时
+            epoch_time = time.time() - epoch_start_time
+            self.writer.add_scalar('Time/epoch', epoch_time, epoch)
+            self.logger.info(f"周期耗时: {epoch_time:.2f} 秒")
+            
+            # 检查是否需要保存最佳模型
+            if current_loss < self.best_loss:
+                self.best_loss = current_loss
+                self.counter = 0
+                # 保存最佳模型
+                save_path = os.path.join(self.save_dir, 'best_pretrain_model.pth')
+                torch.save({
+                    'model_state_dict': self.model.state_dict(),
+                    'reconstruction_head_state_dict': self.reconstruction_head.state_dict(),
+                    'optimizer_state_dict': self.optimizer.state_dict(),
+                    'best_loss': self.best_loss
+                }, save_path)
+                self.logger.info(f"保存最佳预训练模型到 {save_path}")
+            else:
+                self.counter += 1
+                if self.counter >= self.patience:
+                    self.early_stop = True
+                    self.logger.info(f"预训练损失连续 {self.patience} 个周期未改善，触发早停。")
+        
+        # 计算总训练耗时
+        total_time = time.time() - start_time
+        self.logger.info(f"总预训练耗时: {total_time:.2f} 秒")
+        
+        # 保存最后一个模型
+        save_path = os.path.join(self.save_dir, 'last_pretrain_model.pth')
+        torch.save({
+            'model_state_dict': self.model.state_dict(),
+            'reconstruction_head_state_dict': self.reconstruction_head.state_dict(),
+            'optimizer_state_dict': self.optimizer.state_dict()
+        }, save_path)
+        self.logger.info(f"保存最后一个预训练模型到 {save_path}")
+        
+        # 关闭 TensorBoard SummaryWriter
+        self.writer.close()
+        
         self.logger.info("预训练完成。")
+        self.logger.info(f"最佳预训练损失: {self.best_loss:.4f}")
diff --git a/src/engine/trainer.py b/src/engine/trainer.py
index 0a26176..cb3eace 100644
--- a/src/engine/trainer.py
+++ b/src/engine/trainer.py
@@ -2,8 +2,34 @@
 import torch
 import torch.nn as nn
 from torch.optim import Adam, AdamW
+from torch.optim.lr_scheduler import StepLR, CosineAnnealingWarmRestarts
 from torch_geometric.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
 from ..utils.logging import get_logger
+from .evaluator import Evaluator
+import os
+import time
+
+class FocalLoss(nn.Module):
+    """Focal Loss 实现，用于处理类别不平衡问题。"""
+    def __init__(self, alpha=1, gamma=2, reduction='mean'):
+        super(FocalLoss, self).__init__()
+        self.alpha = alpha
+        self.gamma = gamma
+        self.reduction = reduction
+        self.bce_with_logits = nn.BCEWithLogitsLoss(reduction='none')
+
+    def forward(self, inputs, targets):
+        bce_loss = self.bce_with_logits(inputs, targets)
+        pt = torch.exp(-bce_loss)
+        focal_loss = self.alpha * (1 - pt) ** self.gamma * bce_loss
+        
+        if self.reduction == 'mean':
+            return focal_loss.mean()
+        elif self.reduction == 'sum':
+            return focal_loss.sum()
+        else:
+            return focal_loss
 
 class Trainer:
     """处理（监督学习）训练循环。"""
@@ -25,30 +51,97 @@ class Trainer:
         if config['training']['loss_function'] == 'bce':
             # BCEWithLogitsLoss 结合了 Sigmoid 和 BCELoss，更数值稳定
             self.criterion = nn.BCEWithLogitsLoss()
-        # 在此添加其他损失函数，如 focal loss
+        elif config['training']['loss_function'] == 'focal_loss':
+            self.criterion = FocalLoss()
         else:
             raise ValueError(f"不支持的损失函数: {config['training']['loss_function']}")
 
+        # 初始化学习率调度器
+        self.scheduler = None
+        if 'scheduler' in config['training']:
+            scheduler_type = config['training']['scheduler']
+            if scheduler_type == 'step':
+                self.scheduler = StepLR(self.optimizer, step_size=config['training'].get('scheduler_step_size', 30), gamma=config['training'].get('scheduler_gamma', 0.1))
+            elif scheduler_type == 'cosine':
+                self.scheduler = CosineAnnealingWarmRestarts(self.optimizer, T_0=config['training'].get('scheduler_T_0', 10), T_mult=config['training'].get('scheduler_T_mult', 2))
+
+        # 初始化评估器
+        self.evaluator = Evaluator(model)
+
+        # 初始化早停相关变量
+        self.best_val_score = -float('inf')
+        self.patience = config['training'].get('early_stopping_patience', 10)
+        self.counter = 0
+        self.early_stop = False
+
+        # 确保保存目录存在
+        self.save_dir = config.get('save_dir', 'checkpoints')
+        os.makedirs(self.save_dir, exist_ok=True)
+        
+        # 初始化 TensorBoard 日志记录器
+        self.log_dir = config.get('log_dir', 'logs')
+        os.makedirs(self.log_dir, exist_ok=True)
+        self.writer = SummaryWriter(log_dir=self.log_dir)
+        
+        # 初始化混合精度训练
+        self.use_amp = config['training'].get('use_amp', False)
+        self.scaler = torch.cuda.amp.GradScaler() if self.use_amp else None
+        
+        # 初始化梯度累积
+        self.gradient_accumulation_steps = config['training'].get('gradient_accumulation_steps', 1)
+        if self.gradient_accumulation_steps > 1:
+            self.logger.info(f"启用梯度累积，累积步数: {self.gradient_accumulation_steps}")
+
     def train_epoch(self, dataloader: DataLoader):
         """运行单个训练周期（epoch）。"""
         self.model.train()  # 将模型设置为训练模式
         total_loss = 0
-        for batch in dataloader:
-            self.optimizer.zero_grad()  # 清空梯度
+        
+        for i, batch in enumerate(dataloader):
+            # 只有在梯度累积的第一步或不需要累积时才清空梯度
+            if i % self.gradient_accumulation_steps == 0:
+                self.optimizer.zero_grad()
             
-            # 前向传播
-            output = self.model(batch)
-            
-            # 准备目标标签
-            # 假设标签在图级别，并且需要调整形状以匹配输出
-            target = batch.y.view_as(output)
+            # 使用混合精度训练
+            if self.use_amp:
+                with torch.cuda.amp.autocast():
+                    # 前向传播
+                    output = self.model(batch)
+                    
+                    # 准备目标标签
+                    # 假设标签在图级别，并且需要调整形状以匹配输出
+                    target = batch.y.view_as(output)
 
-            # 计算损失
-            loss = self.criterion(output, target)
-            # 反向传播
-            loss.backward()
-            # 更新权重
-            self.optimizer.step()
+                    # 计算损失
+                    loss = self.criterion(output, target)
+                
+                # 缩放损失以防止梯度下溢
+                self.scaler.scale(loss).backward()
+                
+                # 只有在累积步数达到设定值时才更新权重
+                if (i + 1) % self.gradient_accumulation_steps == 0:
+                    # 取消缩放并更新权重
+                    self.scaler.step(self.optimizer)
+                    self.scaler.update()
+            else:
+                # 标准训练流程
+                # 前向传播
+                output = self.model(batch)
+                
+                # 准备目标标签
+                # 假设标签在图级别，并且需要调整形状以匹配输出
+                target = batch.y.view_as(output)
+
+                # 计算损失
+                loss = self.criterion(output, target)
+                
+                # 反向传播
+                loss.backward()
+                
+                # 只有在累积步数达到设定值时才更新权重
+                if (i + 1) % self.gradient_accumulation_steps == 0:
+                    # 更新权重
+                    self.optimizer.step()
             
             total_loss += loss.item()
         
@@ -56,11 +149,79 @@ class Trainer:
         self.logger.info(f"训练损失: {avg_loss:.4f}")
         return avg_loss
 
+    def validate(self, dataloader: DataLoader):
+        """运行验证并返回评估指标。"""
+        self.model.eval()  # 将模型设置为评估模式
+        metrics = self.evaluator.evaluate(dataloader)
+        return metrics
+
     def run(self, train_loader: DataLoader, val_loader: DataLoader):
         """运行完整的训练流程。"""
         self.logger.info("开始训练...")
+        start_time = time.time()
+        
         for epoch in range(self.config['training']['epochs']):
+            if self.early_stop:
+                self.logger.info("早停触发，停止训练。")
+                break
+                
+            epoch_start_time = time.time()
             self.logger.info(f"周期 {epoch+1}/{self.config['training']['epochs']}")
-            self.train_epoch(train_loader)
-            # 在此处添加验证步骤，例如调用 Evaluator
+            
+            # 训练一个周期
+            train_loss = self.train_epoch(train_loader)
+            
+            # 验证
+            self.logger.info("正在验证...")
+            val_metrics = self.validate(val_loader)
+            
+            # 更新学习率调度器
+            current_lr = self.optimizer.param_groups[0]['lr']
+            if self.scheduler:
+                self.scheduler.step()
+                new_lr = self.optimizer.param_groups[0]['lr']
+                self.logger.info(f"学习率从 {current_lr:.6f} 调整为 {new_lr:.6f}")
+                current_lr = new_lr
+            else:
+                self.logger.info(f"当前学习率: {current_lr:.6f}")
+            
+            # 记录到 TensorBoard
+            self.writer.add_scalar('Loss/train', train_loss, epoch)
+            for metric_name, metric_value in val_metrics.items():
+                self.writer.add_scalar(f'Metrics/{metric_name}', metric_value, epoch)
+            self.writer.add_scalar('Learning Rate', current_lr, epoch)
+            
+            # 计算周期耗时
+            epoch_time = time.time() - epoch_start_time
+            self.writer.add_scalar('Time/epoch', epoch_time, epoch)
+            self.logger.info(f"周期耗时: {epoch_time:.2f} 秒")
+            
+            # 检查是否需要保存最佳模型
+            val_score = val_metrics.get('f1', val_metrics.get('accuracy', -1))
+            if val_score > self.best_val_score:
+                self.best_val_score = val_score
+                self.counter = 0
+                # 保存最佳模型
+                save_path = os.path.join(self.save_dir, 'best_model.pth')
+                torch.save(self.model.state_dict(), save_path)
+                self.logger.info(f"保存最佳模型到 {save_path}")
+            else:
+                self.counter += 1
+                if self.counter >= self.patience:
+                    self.early_stop = True
+                    self.logger.info(f"验证性能连续 {self.patience} 个周期未改善，触发早停。")
+        
+        # 计算总训练耗时
+        total_time = time.time() - start_time
+        self.logger.info(f"总训练耗时: {total_time:.2f} 秒")
+        
+        # 保存最后一个模型
+        save_path = os.path.join(self.save_dir, 'last_model.pth')
+        torch.save(self.model.state_dict(), save_path)
+        self.logger.info(f"保存最后一个模型到 {save_path}")
+        
+        # 关闭 TensorBoard SummaryWriter
+        self.writer.close()
+        
         self.logger.info("训练完成。")
+        self.logger.info(f"最佳验证分数: {self.best_val_score:.4f}")
diff --git a/src/models/__pycache__/geo_layout_transformer.cpython-312.pyc b/src/models/__pycache__/geo_layout_transformer.cpython-312.pyc
new file mode 100644
index 0000000..4f91297
Binary files /dev/null and b/src/models/__pycache__/geo_layout_transformer.cpython-312.pyc differ
diff --git a/src/models/__pycache__/gnn_encoder.cpython-312.pyc b/src/models/__pycache__/gnn_encoder.cpython-312.pyc
new file mode 100644
index 0000000..9a463ce
Binary files /dev/null and b/src/models/__pycache__/gnn_encoder.cpython-312.pyc differ
diff --git a/src/models/__pycache__/task_heads.cpython-312.pyc b/src/models/__pycache__/task_heads.cpython-312.pyc
new file mode 100644
index 0000000..4239666
Binary files /dev/null and b/src/models/__pycache__/task_heads.cpython-312.pyc differ
diff --git a/src/models/__pycache__/transformer_core.cpython-312.pyc b/src/models/__pycache__/transformer_core.cpython-312.pyc
new file mode 100644
index 0000000..86e5bf0
Binary files /dev/null and b/src/models/__pycache__/transformer_core.cpython-312.pyc differ
diff --git a/src/models/geo_layout_transformer.py b/src/models/geo_layout_transformer.py
index 34a9a88..0c15093 100644
--- a/src/models/geo_layout_transformer.py
+++ b/src/models/geo_layout_transformer.py
@@ -3,7 +3,7 @@ import torch
 import torch.nn as nn
 from .gnn_encoder import GNNEncoder
 from .transformer_core import TransformerCore
-from .task_heads import ClassificationHead, MatchingHead
+from .task_heads import ClassificationHead, MultiLabelClassificationHead, RegressionHead, MatchingHead
 
 class GeoLayoutTransformer(nn.Module):
     """完整的 Geo-Layout Transformer 模型。"""
@@ -38,16 +38,34 @@ class GeoLayoutTransformer(nn.Module):
         self.task_head = None
         if 'task_head' in config['model']:
             head_config = config['model']['task_head']
+            pooling_type = head_config.get('pooling_type', 'mean')
+            
             if head_config['type'] == 'classification':
                 self.task_head = ClassificationHead(
                     input_dim=head_config['input_dim'],
                     hidden_dim=head_config['hidden_dim'],
-                    output_dim=head_config['output_dim']
+                    output_dim=head_config['output_dim'],
+                    pooling_type=pooling_type
+                )
+            elif head_config['type'] == 'multi_label_classification':
+                self.task_head = MultiLabelClassificationHead(
+                    input_dim=head_config['input_dim'],
+                    hidden_dim=head_config['hidden_dim'],
+                    output_dim=head_config['output_dim'],
+                    pooling_type=pooling_type
+                )
+            elif head_config['type'] == 'regression':
+                self.task_head = RegressionHead(
+                    input_dim=head_config['input_dim'],
+                    hidden_dim=head_config['hidden_dim'],
+                    output_dim=head_config['output_dim'],
+                    pooling_type=pooling_type
                 )
             elif head_config['type'] == 'matching':
                 self.task_head = MatchingHead(
                     input_dim=head_config['input_dim'],
-                    output_dim=head_config['output_dim']
+                    output_dim=head_config['output_dim'],
+                    pooling_type=pooling_type
                 )
             # 可在此处添加其他任务头
 
diff --git a/src/models/gnn_encoder.py b/src/models/gnn_encoder.py
index 1140c3f..3677757 100644
--- a/src/models/gnn_encoder.py
+++ b/src/models/gnn_encoder.py
@@ -48,15 +48,14 @@ class GNNEncoder(nn.Module):
             data: 一个 PyTorch Geometric 的 Data 或 Batch 对象。
 
         Returns:
-            一个代表区块的图级别嵌入的张量。
+            一个代表节点级别的嵌入的张量。
         """
-        x, edge_index, batch = data.x, data.edge_index, data.batch
+        x, edge_index = data.x, data.edge_index
 
         # 通过所有 GNN 层
         for layer in self.layers:
             x = layer(x, edge_index)
             x = torch.relu(x)
 
-        # 全局池化以获得图级别的嵌入
-        graph_embedding = self.readout(x, batch)
-        return graph_embedding
+        # 返回节点级别的嵌入，不进行全局池化
+        return x
diff --git a/src/models/task_heads.py b/src/models/task_heads.py
index fd374eb..b860c76 100644
--- a/src/models/task_heads.py
+++ b/src/models/task_heads.py
@@ -2,11 +2,44 @@
 import torch
 import torch.nn as nn
 
+class PoolingLayer(nn.Module):
+    """可插拔的池化层，支持多种池化策略。"""
+    def __init__(self, pooling_type: str = 'mean'):
+        super(PoolingLayer, self).__init__()
+        self.pooling_type = pooling_type
+        
+        # 如果使用注意力池化，需要定义注意力机制
+        if pooling_type == 'attention':
+            self.attention = nn.Linear(1, 1)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            x: 形状为 [batch_size, seq_len, hidden_dim] 的张量
+
+        Returns:
+            形状为 [batch_size, hidden_dim] 的池化后的张量
+        """
+        if self.pooling_type == 'mean':
+            return torch.mean(x, dim=1)
+        elif self.pooling_type == 'max':
+            return torch.max(x, dim=1)[0]
+        elif self.pooling_type == 'cls':
+            # 取第一个 token 作为 [CLS] token
+            return x[:, 0, :]
+        elif self.pooling_type == 'attention':
+            # 计算注意力权重
+            weights = self.attention(torch.ones_like(x[:, :, :1])).softmax(dim=1)
+            return (x * weights).sum(dim=1)
+        else:
+            raise ValueError(f"不支持的池化类型: {self.pooling_type}")
+
 class ClassificationHead(nn.Module):
     """一个用于分类任务的简单多层感知机（MLP）任务头。"""
 
-    def __init__(self, input_dim: int, hidden_dim: int, output_dim: int):
+    def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, pooling_type: str = 'mean'):
         super(ClassificationHead, self).__init__()
+        self.pooling = PoolingLayer(pooling_type)
         self.fc1 = nn.Linear(input_dim, hidden_dim)
         self.relu = nn.ReLU()
         self.fc2 = nn.Linear(hidden_dim, output_dim)
@@ -19,9 +52,60 @@ class ClassificationHead(nn.Module):
         Returns:
             最终的分类 logits。
         """
-        # 我们可以取第一个 token（类似 [CLS]）的嵌入，或者进行平均池化
-        # 为简单起见，我们假设在序列维度上进行平均池化
-        x_pooled = torch.mean(x, dim=1)
+        # 使用指定的池化方法
+        x_pooled = self.pooling(x)
+        
+        out = self.fc1(x_pooled)
+        out = self.relu(out)
+        out = self.fc2(out)
+        return out
+
+class MultiLabelClassificationHead(nn.Module):
+    """用于多标签分类任务的任务头。"""
+
+    def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, pooling_type: str = 'mean'):
+        super(MultiLabelClassificationHead, self).__init__()
+        self.pooling = PoolingLayer(pooling_type)
+        self.fc1 = nn.Linear(input_dim, hidden_dim)
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(hidden_dim, output_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            x: 来自 Transformer 骨干网络的输入张量。
+
+        Returns:
+            最终的多标签分类 logits。
+        """
+        # 使用指定的池化方法
+        x_pooled = self.pooling(x)
+        
+        out = self.fc1(x_pooled)
+        out = self.relu(out)
+        out = self.fc2(out)
+        return out
+
+class RegressionHead(nn.Module):
+    """用于回归任务的任务头。"""
+
+    def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, pooling_type: str = 'mean'):
+        super(RegressionHead, self).__init__()
+        self.pooling = PoolingLayer(pooling_type)
+        self.fc1 = nn.Linear(input_dim, hidden_dim)
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(hidden_dim, output_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            x: 来自 Transformer 骨干网络的输入张量。
+
+        Returns:
+            最终的回归输出。
+        """
+        # 使用指定的池化方法
+        x_pooled = self.pooling(x)
         
         out = self.fc1(x_pooled)
         out = self.relu(out)
@@ -31,8 +115,9 @@ class ClassificationHead(nn.Module):
 class MatchingHead(nn.Module):
     """用于学习版图匹配的相似性嵌入的任务头。"""
 
-    def __init__(self, input_dim: int, output_dim: int):
+    def __init__(self, input_dim: int, output_dim: int, pooling_type: str = 'mean'):
         super(MatchingHead, self).__init__()
+        self.pooling = PoolingLayer(pooling_type)
         self.projection = nn.Linear(input_dim, output_dim)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -43,8 +128,8 @@ class MatchingHead(nn.Module):
         Returns:
             代表整个输入图（例如一个 IP 模块）的单个嵌入向量。
         """
-        # 全局平均池化，为整个序列获取一个单一的向量
-        graph_embedding = torch.mean(x, dim=1)
+        # 使用指定的池化方法
+        graph_embedding = self.pooling(x)
         # 投影到最终的嵌入空间
         similarity_embedding = self.projection(graph_embedding)
         # 对嵌入进行 L2 归一化，以便使用余弦相似度
diff --git a/src/utils/__init__.py b/src/utils/__init__.py
new file mode 100644
index 0000000..4384acc
--- /dev/null
+++ b/src/utils/__init__.py
@@ -0,0 +1,6 @@
+# src/utils/__init__.py
+from .config_loader import load_config, merge_configs
+from .logging import get_logger
+from .seed import set_seed
+
+__all__ = ['load_config', 'merge_configs', 'get_logger', 'set_seed']
diff --git a/src/utils/__pycache__/__init__.cpython-312.pyc b/src/utils/__pycache__/__init__.cpython-312.pyc
new file mode 100644
index 0000000..8027b81
Binary files /dev/null and b/src/utils/__pycache__/__init__.cpython-312.pyc differ
diff --git a/src/utils/__pycache__/config_loader.cpython-312.pyc b/src/utils/__pycache__/config_loader.cpython-312.pyc
new file mode 100644
index 0000000..3b8a6cd
Binary files /dev/null and b/src/utils/__pycache__/config_loader.cpython-312.pyc differ
diff --git a/src/utils/__pycache__/logging.cpython-312.pyc b/src/utils/__pycache__/logging.cpython-312.pyc
new file mode 100644
index 0000000..6003e5d
Binary files /dev/null and b/src/utils/__pycache__/logging.cpython-312.pyc differ
diff --git a/src/utils/__pycache__/seed.cpython-312.pyc b/src/utils/__pycache__/seed.cpython-312.pyc
new file mode 100644
index 0000000..c12b2d1
Binary files /dev/null and b/src/utils/__pycache__/seed.cpython-312.pyc differ
diff --git a/src/utils/seed.py b/src/utils/seed.py
new file mode 100644
index 0000000..d650785
--- /dev/null
+++ b/src/utils/seed.py
@@ -0,0 +1,33 @@
+# src/utils/seed.py
+import random
+import numpy as np
+import torch
+import os
+
+
+def set_seed(seed: int = 42):
+    """
+    设置随机种子，确保实验的可重复性。
+    
+    Args:
+        seed: 随机种子值
+    """
+    # 设置 Python 内置随机种子
+    random.seed(seed)
+    
+    # 设置 NumPy 随机种子
+    np.random.seed(seed)
+    
+    # 设置 PyTorch 随机种子
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)  # 对于多 GPU 环境
+    
+    # 禁用 CUDA 中的确定性算法，以提高性能（可选）
+    # torch.backends.cudnn.deterministic = True
+    # torch.backends.cudnn.benchmark = False
+    
+    # 设置环境变量中的随机种子
+    os.environ['PYTHONHASHSEED'] = str(seed)
+    
+    print(f"随机种子已设置为: {seed}")
diff --git a/tests/test_model_run.py b/tests/test_model_run.py
new file mode 100644
index 0000000..47d8e8b
--- /dev/null
+++ b/tests/test_model_run.py
@@ -0,0 +1,199 @@
+#!/usr/bin/env python3
+"""
+测试脚本，用于验证模型是否可以正常跑通，不需要真实数据
+- 生成随机图数据
+- 加载模型配置
+- 初始化模型
+- 运行前向传播和反向传播
+- 验证模型是否可以正常工作
+"""
+import os
+import sys
+import torch
+from torch_geometric.data import Data, Batch
+
+# 添加项目根目录到 Python 路径
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from src.utils.config_loader import load_config
+from src.models.geo_layout_transformer import GeoLayoutTransformer
+from src.engine.trainer import Trainer
+from src.engine.self_supervised import SelfSupervisedTrainer
+from src.utils.logging import get_logger
+
+def generate_random_graph_data(num_graphs=4, num_nodes_per_graph=8, node_feature_dim=5, edge_feature_dim=0):
+    """
+    生成随机的图数据
+    
+    Args:
+        num_graphs: 图的数量
+        num_nodes_per_graph: 每个图的节点数量
+        node_feature_dim: 节点特征维度
+        edge_feature_dim: 边特征维度
+    
+    Returns:
+        一个 Batch 对象，包含多个随机生成的图
+    """
+    graphs = []
+    
+    for _ in range(num_graphs):
+        # 生成随机节点特征
+        x = torch.randn(num_nodes_per_graph, node_feature_dim)
+        
+        # 生成随机边（完全连接）
+        edge_index = []
+        for i in range(num_nodes_per_graph):
+            for j in range(num_nodes_per_graph):
+                if i != j:
+                    edge_index.append([i, j])
+        edge_index = torch.tensor(edge_index, dtype=torch.long).t()
+        
+        # 生成随机标签
+        y = torch.randn(1, 1)  # 假设是图级别的标签
+        
+        # 创建图数据
+        graph = Data(x=x, edge_index=edge_index, y=y)
+        graphs.append(graph)
+    
+    # 构建批次
+    batch = Batch.from_data_list(graphs)
+    return batch
+
+def test_supervised_training():
+    """测试监督训练"""
+    logger = get_logger("Test_Supervised_Training")
+    logger.info("=== 测试监督训练 ===")
+    
+    # 加载配置
+    config = load_config('configs/default.yaml')
+    
+    # 生成随机数据
+    batch = generate_random_graph_data()
+    logger.info(f"生成的批次数据: {batch}")
+    logger.info(f"批次大小: {batch.num_graphs}")
+    logger.info(f"总节点数: {batch.num_nodes}")
+    logger.info(f"总边数: {batch.num_edges}")
+    
+    # 初始化模型
+    logger.info("初始化模型...")
+    model = GeoLayoutTransformer(config)
+    logger.info("模型初始化成功")
+    
+    # 初始化训练器
+    logger.info("初始化训练器...")
+    trainer = Trainer(model, config)
+    logger.info("训练器初始化成功")
+    
+    # 测试前向传播
+    logger.info("测试前向传播...")
+    with torch.no_grad():
+        # 先测试 GNN 编码器
+        gnn_output = model.gnn_encoder(batch)
+        logger.info(f"GNN 编码器输出形状: {gnn_output.shape}")
+        
+        # 测试形状重塑
+        num_graphs = batch.num_graphs
+        nodes_per_graph = batch.ptr[1:] - batch.ptr[:-1]
+        logger.info(f"每个图的节点数: {nodes_per_graph}")
+        reshaped_embeddings = gnn_output.view(num_graphs, nodes_per_graph[0], -1)
+        logger.info(f"重塑后的嵌入形状: {reshaped_embeddings.shape}")
+        
+        # 测试 Transformer 核心
+        transformer_output = model.transformer_core(reshaped_embeddings)
+        logger.info(f"Transformer 输出形状: {transformer_output.shape}")
+        
+        # 测试完整模型
+        output = model(batch)
+    logger.info(f"前向传播成功，输出形状: {output.shape}")
+    
+    # 测试反向传播
+    logger.info("测试反向传播...")
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)
+    optimizer.zero_grad()
+    output = model(batch)
+    
+    # 对输出进行全局池化，得到图级别的表示
+    # 从 [batch_size, seq_len, hidden_dim] 变为 [batch_size, hidden_dim]
+    graph_output = output.mean(dim=1)
+    
+    # 使用 MSE 损失，只比较前 1 个维度（与 batch.y 形状匹配）
+    loss = torch.nn.MSELoss()(graph_output[:, :1], batch.y)
+    loss.backward()
+    optimizer.step()
+    logger.info(f"反向传播成功，损失值: {loss.item()}")
+    
+    logger.info("监督训练测试完成，模型可以正常工作！")
+
+def test_self_supervised_training():
+    """测试自监督训练"""
+    logger = get_logger("Test_Self_Supervised_Training")
+    logger.info("\n=== 测试自监督训练 ===")
+    
+    # 加载配置
+    config = load_config('configs/default.yaml')
+    
+    # 生成随机数据
+    batch = generate_random_graph_data()
+    logger.info(f"生成的批次数据: {batch}")
+    logger.info(f"批次大小: {batch.num_graphs}")
+    logger.info(f"总节点数: {batch.num_nodes}")
+    logger.info(f"总边数: {batch.num_edges}")
+    
+    # 初始化模型
+    logger.info("初始化模型...")
+    model = GeoLayoutTransformer(config)
+    logger.info("模型初始化成功")
+    
+    # 初始化自监督训练器
+    logger.info("初始化自监督训练器...")
+    trainer = SelfSupervisedTrainer(model, config)
+    logger.info("自监督训练器初始化成功")
+    
+    # 测试前向传播
+    logger.info("测试前向传播...")
+    with torch.no_grad():
+        # 测试 GNN 编码器
+        gnn_output = model.gnn_encoder(batch)
+        logger.info(f"GNN 编码器输出形状: {gnn_output.shape}")
+        
+        # 测试 Transformer 核心
+        num_graphs = batch.num_graphs
+        nodes_per_graph = batch.ptr[1:] - batch.ptr[:-1]
+        if not torch.all(nodes_per_graph == nodes_per_graph[0]):
+            logger.warning("批次中图形的节点数不一致，使用第一个图形的节点数")
+        nodes_per_graph = nodes_per_graph[0]
+        
+        gnn_output_reshaped = gnn_output.view(num_graphs, nodes_per_graph, -1)
+        transformer_output = model.transformer_core(gnn_output_reshaped)
+        logger.info(f"Transformer 核心输出形状: {transformer_output.shape}")
+    
+    # 测试完整模型前向传播
+    logger.info("测试完整模型前向传播...")
+    with torch.no_grad():
+        output = model(batch)
+    logger.info(f"完整模型前向传播成功，输出形状: {output.shape}")
+    
+    logger.info("自监督训练测试完成，模型可以正常工作！")
+
+def main():
+    """主函数"""
+    logger = get_logger("Test_Model_Run")
+    logger.info("开始测试模型是否可以正常跑通...")
+    
+    try:
+        # 测试监督训练
+        test_supervised_training()
+        
+        # 测试自监督训练
+        test_self_supervised_training()
+        
+        logger.info("\n✅ 所有测试通过，模型可以正常跑通！")
+        logger.info("模型已准备就绪，可以使用真实数据进行训练。")
+    except Exception as e:
+        logger.error(f"❌ 测试失败: {e}")
+        import traceback
+        traceback.print_exc()
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()
diff --git a/uv.lock b/uv.lock
index d11ebc7..c08d9b8 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,8 +1,16 @@
-# uv.lock
 version = 1
 revision = 2
 requires-python = ">=3.12"
 
+[[package]]
+name = "absl-py"
+version = "2.4.0"
+source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
+sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/64/c7/8de93764ad66968d19329a7e0c147a2bb3c7054c554d4a119111b8f9440f/absl_py-2.4.0.tar.gz", hash = "sha256:8c6af82722b35cf71e0f4d1d47dcaebfff286e27110a99fc359349b247dfb5d4", size = 116543, upload-time = "2026-01-28T10:17:05.322Z" }
+wheels = [
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d", size = 135750, upload-time = "2026-01-28T10:17:04.19Z" },
+]
+
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
@@ -234,20 +242,35 @@ sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/e8/b5/a12ef182943856
 wheels = [
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/51/a8/cff9bd17789b41c2f09f4e4b574fd05c5860e0b5602b3708b6c1de4b6c82/gdstk-0.9.61-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:61f0ee05cdce9b4163ea812cbf2e2f5d8d01a293fa118ff98348280306bd91d6", size = 923143, upload-time = "2025-08-28T10:16:42.55Z" },
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/9b/13/c97316d18510e2dcb3aeba0e13f4c6d7ad34884be62006b910f382bdfbc6/gdstk-0.9.61-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:fff1b104b6775e4c27ab2751b3f4ac6c1ce86a4e9afd5e5535ac4acefa6a7a07", size = 475724, upload-time = "2025-08-28T10:16:44.434Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/19/49/40aed8aae97054b08a0063a583ef55e3ab0e335441d6d339615d8593892a/gdstk-0.9.61-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5218f8c5ab13b6e979665c0a7dc1272768003a1cb7add0682483837f7485faed", size = 536850, upload-time = "2025-10-20T11:25:50.578Z" },
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/35/7c/4d324ae83dac2ba15fcc61d688019ad79b8938c027d2467415cb804f8058/gdstk-0.9.61-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4e79f3881d3b3666a600efd5b2c131454507f69d3c9b9eaf383d106cfbd6e7bc", size = 600640, upload-time = "2025-08-28T10:16:45.627Z" },
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/ef/cd/addb66d3740a654495e413fb28d6c4e4d9ac94bc0c25dc31f2868fb6e18c/gdstk-0.9.61-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e90a6e24c2145320e53e953a59c6297fd25c17c6ef098fa8602e64e64a5390ea", size = 536849, upload-time = "2025-08-28T10:16:47.039Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/d7/37/43fb416068f0722f1a9ccc67fe6a8f78c93ef3bb9cdf37a2edd276316b23/gdstk-0.9.61-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a3a49401cbd26c5a17a4152d1befa73efb21af694524557bf09d15f4c8a874e6", size = 540029, upload-time = "2025-10-20T11:26:06.539Z" },
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/f9/f0/d4a24c1b6636454812b4990dc590d72b0a37f185122c6fa19d4b0d22a90e/gdstk-0.9.61-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:8738ac63bbe29dcb5abae6a19d207c4e0857f9dc1bd405c85af8a87f0dcfb348", size = 535749, upload-time = "2025-08-28T10:16:48.157Z" },
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/85/67/ec1ef9f67ac26554d55f4e667e602698fb7f520ef89dfe6cb4550152eec1/gdstk-0.9.61-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:23bb023a49f3321673d0e32cdce2e2705a51d9e12328c928723ded49af970520", size = 1711671, upload-time = "2025-08-28T10:16:49.963Z" },
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/89/73/4fd73bbc7500e383500a9edca4c09ce38bffd44eefb256ce17a41c526dbe/gdstk-0.9.61-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:81c2f19cab89623d1f56848e7a16e2fab82a93c61c8f7aa73f5ff59840b60c0f", size = 1535161, upload-time = "2025-08-28T10:16:51.181Z" },
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/a0/b1/d740423bd7436c522295a09cadc0e21d59afa418185cffffce46a6eb85b0/gdstk-0.9.61-cp312-cp312-win_amd64.whl", hash = "sha256:4474f015ecc228b210165287cb7eea65639ea6308f60105cb49e970079bddc2b", size = 500139, upload-time = "2025-08-28T10:16:52.496Z" },
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/07/9a/7ea7b7a295e029542d4aeb252d01c1bcc8e724df26155f9b5f432b02d02a/gdstk-0.9.61-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:3beeae846fc523c7e3a01c47edcd3b7dd83c29650e56b82a371e528f9cb0ec3e", size = 923024, upload-time = "2025-08-28T10:16:53.613Z" },
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/b8/3d/5aa9b1a4665259702e9f17e03a9a114a873df25c9ba2c9e782ff25fb11e9/gdstk-0.9.61-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:575a21639b31e2fab4d9e918468b8b40a58183028db563e5963be594bff1403d", size = 475687, upload-time = "2025-08-28T10:16:54.886Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/80/13/ec783d8de5d9b4e51763102cac6da124f16747e4c73f166c36a105065008/gdstk-0.9.61-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:90d54b48223dcbb8257769faaa87542d12a749d8486e8d1187a45d06e9422859", size = 536872, upload-time = "2025-10-20T11:25:52.902Z" },
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/20/4a/365ca49b76ee3d70a0d044fcc44272c85754fd763e0b3f3e9f498b8bf4a1/gdstk-0.9.61-cp313-cp313-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:35405bed95542a0b10f343b165ce0ad80740bf8127a4507565ec74222e6ec8d3", size = 600630, upload-time = "2025-08-28T10:16:56.326Z" },
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/62/3e/815fd2977ff1d885ad87d0a54deb19926fd025933325c7a27625c1c0a0c3/gdstk-0.9.61-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b311ddf8982995b52ac3bf3b32a6cf6d918afc4e66dea527d531e8af73896231", size = 536873, upload-time = "2025-08-28T10:16:57.516Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/b0/8b/1ba0abc4fb3c60015d23894aa9d093f473fdc337584f9c1d7afe96d6f9f5/gdstk-0.9.61-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6dcbfc60fba92d10f1c7d612b5409c343fcaf2a380640e9fb01c504ca948b412", size = 540029, upload-time = "2025-10-20T11:26:09.727Z" },
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/74/72/cc46f132741e541995ede7fccf9820f105fb2296ab70192bd27de56190f2/gdstk-0.9.61-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:fab67ccdd8029ef7eb873f8c98f875dc2665a5e45af7cf3d2a7a0f401826a1d3", size = 535763, upload-time = "2025-08-28T10:16:58.621Z" },
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/5b/db/72196721fedfc38cf21158e5a436d73b41ea244b78c1053462a35d1e42cb/gdstk-0.9.61-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:5852749e203d6978e06d02f8ef9e29ce4512cb1aedeb62c37b8e8b2c10c4f529", size = 1711669, upload-time = "2025-08-28T10:16:59.838Z" },
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/36/ec/eeebcc95c2741e1f39da08c95fdcc56b3d0f5305ad732d8a66213b6fa0b8/gdstk-0.9.61-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:7ee38a54c799e77dbe219266f765bbd3b2906b62bc7b6fb64b1387e6db3dd187", size = 1535165, upload-time = "2025-08-28T10:17:01.027Z" },
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/cb/a8/653335b1ec13306b023a9aa1e2072e8bab5c0a5376c138066006504198c3/gdstk-0.9.61-cp313-cp313-win_amd64.whl", hash = "sha256:6abb396873b2660dd7863d664b3822f00547bf7f216af27be9f1f812bc5e8027", size = 500117, upload-time = "2025-08-28T10:17:02.459Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/33/52/28cd8720357d6b892ac19684e4af57f7264ba8eea0ea8078e17f0476408c/gdstk-0.9.61-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:a674af8be5cf1f8ea9f6c5b5f165f797d7e2ed74cbca68b4a22adb92b515fb35", size = 916091, upload-time = "2025-10-20T11:25:35.497Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/fe/f1/c55c7b7b0158a8540716a4fea3aaf0288726122afc0e9af1f0564b79605b/gdstk-0.9.61-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:38ec0b7285d6c9bf8cbc279731dc0d314633cda2ce9e6f9053554b3e5f004fcd", size = 474548, upload-time = "2025-10-20T11:25:38.086Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/ab/83/23907af9b349d79af54c4a79ac7972b9a52f8cee48b366ee9635cf9a32d9/gdstk-0.9.61-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3b63a77b57fb441c8017217aaf1e8b13d93cbee822031a8e2826adb716e01dd4", size = 537072, upload-time = "2025-10-20T11:25:55.473Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/2c/e7/e930928f9a03b896f51b6e975dbb652cffa81c61338609d6289c3f48313c/gdstk-0.9.61-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f7fae6eee627e837d1405b47d381ccd33dbba85473b1bb3822bdc8ae41dbc0dc", size = 540132, upload-time = "2025-10-20T11:26:11.379Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/6d/9d/56afbb84cccb07751f8532591bfc3a1608833943446b51978b52daaaa2b1/gdstk-0.9.61-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:9e396694cac24bd87d0e38c37e6740d9ba0c13f6c9f2211a871d62288430f069", size = 1578033, upload-time = "2025-10-20T11:26:18.707Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/58/14/c854d3ef2b0ece30c0cb4c034d3b7f58425086383a229c960d813a163861/gdstk-0.9.61-cp314-cp314-win_amd64.whl", hash = "sha256:7ea0c1200dc53b794e9c0cc6fe3ea51e49113dfdd9c3109e1961cda3cc2197c7", size = 513072, upload-time = "2025-10-20T11:25:21.089Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/be/0a/5e9c3be1327d36556a5e8d16c6696614fdc1df0d28c0629b829785245d76/gdstk-0.9.61-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:616dd1c3e7aea4a98aeb03db7cf76a853d134c54690790eaa25c63eede7b869a", size = 925091, upload-time = "2025-10-20T11:25:40.772Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/6a/4a/a72de67ea1c217537ae537d7e96b6a0b3c7427177bd1439c89e7617ad3f0/gdstk-0.9.61-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:b0e898202fbb7fd4c39f8404831415a0aa0445656342102c4e77d4a7c2c15a1d", size = 477723, upload-time = "2025-10-20T11:25:44.535Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/0c/94/5034a646ee3bda58210cd377b241582161c8683489210cba762d4f7f06d1/gdstk-0.9.61-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:29bb862a1a814f5bbd6f8bbc2f99e1163df9e6307071cb6e11251dbe7542feb5", size = 541773, upload-time = "2025-10-20T11:25:57.407Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/bc/90/3e5883b528dcc7c4daa74925276a09ff274004518deea48c859b9215120b/gdstk-0.9.61-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c6c2a08d82a683aff50dc63f2943ed805d32d46bd984cbd4ac9cf876146d0ef9", size = 544525, upload-time = "2025-10-20T11:26:13.877Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/46/0b/549a0e72982b87011013705cdb552e2acfdb882ceaeb41a3fe020e981ec8/gdstk-0.9.61-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:3ba52f95763052a6968583942e6531ceca20c14c762d44fe2bd887445e2f73b6", size = 1582069, upload-time = "2025-10-20T11:26:21.189Z" },
 ]
 
 [[package]]
@@ -260,6 +283,7 @@ dependencies = [
     { name = "pandas" },
     { name = "pyyaml" },
     { name = "scikit-learn" },
+    { name = "tensorboard" },
     { name = "torch" },
     { name = "torch-geometric" },
     { name = "torchvision" },
@@ -272,11 +296,53 @@ requires-dist = [
     { name = "pandas", specifier = ">=2.3.2" },
     { name = "pyyaml", specifier = ">=6.0.2" },
     { name = "scikit-learn", specifier = ">=1.7.1" },
+    { name = "tensorboard", specifier = ">=2.20.0" },
     { name = "torch", specifier = ">=2.8.0" },
     { name = "torch-geometric", specifier = ">=2.6.1" },
     { name = "torchvision", specifier = ">=0.23.0" },
 ]
 
+[[package]]
+name = "grpcio"
+version = "1.78.0"
+source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/06/8a/3d098f35c143a89520e568e6539cc098fcd294495910e359889ce8741c84/grpcio-1.78.0.tar.gz", hash = "sha256:7382b95189546f375c174f53a5fa873cef91c4b8005faa05cc5b3beea9c4f1c5", size = 12852416, upload-time = "2026-02-06T09:57:18.093Z" }
+wheels = [
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/4e/f4/7384ed0178203d6074446b3c4f46c90a22ddf7ae0b3aee521627f54cfc2a/grpcio-1.78.0-cp312-cp312-linux_armv7l.whl", hash = "sha256:f9ab915a267fc47c7e88c387a3a28325b58c898e23d4995f765728f4e3dedb97", size = 5913985, upload-time = "2026-02-06T09:55:26.832Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/81/ed/be1caa25f06594463f685b3790b320f18aea49b33166f4141bfdc2bfb236/grpcio-1.78.0-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:3f8904a8165ab21e07e58bf3e30a73f4dffc7a1e0dbc32d51c61b5360d26f43e", size = 11811853, upload-time = "2026-02-06T09:55:29.224Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/24/a7/f06d151afc4e64b7e3cc3e872d331d011c279aaab02831e40a81c691fb65/grpcio-1.78.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:859b13906ce098c0b493af92142ad051bf64c7870fa58a123911c88606714996", size = 6475766, upload-time = "2026-02-06T09:55:31.825Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/8a/a8/4482922da832ec0082d0f2cc3a10976d84a7424707f25780b82814aafc0a/grpcio-1.78.0-cp312-cp312-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:b2342d87af32790f934a79c3112641e7b27d63c261b8b4395350dad43eff1dc7", size = 7170027, upload-time = "2026-02-06T09:55:34.7Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/54/bf/f4a3b9693e35d25b24b0b39fa46d7d8a3c439e0a3036c3451764678fec20/grpcio-1.78.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:12a771591ae40bc65ba67048fa52ef4f0e6db8279e595fd349f9dfddeef571f9", size = 6690766, upload-time = "2026-02-06T09:55:36.902Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/c7/b9/521875265cc99fe5ad4c5a17010018085cae2810a928bf15ebe7d8bcd9cc/grpcio-1.78.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:185dea0d5260cbb2d224c507bf2a5444d5abbb1fa3594c1ed7e4c709d5eb8383", size = 7266161, upload-time = "2026-02-06T09:55:39.824Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/05/86/296a82844fd40a4ad4a95f100b55044b4f817dece732bf686aea1a284147/grpcio-1.78.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:51b13f9aed9d59ee389ad666b8c2214cc87b5de258fa712f9ab05f922e3896c6", size = 8253303, upload-time = "2026-02-06T09:55:42.353Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/f3/e4/ea3c0caf5468537f27ad5aab92b681ed7cc0ef5f8c9196d3fd42c8c2286b/grpcio-1.78.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:fd5f135b1bd58ab088930b3c613455796dfa0393626a6972663ccdda5b4ac6ce", size = 7698222, upload-time = "2026-02-06T09:55:44.629Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/d7/47/7f05f81e4bb6b831e93271fb12fd52ba7b319b5402cbc101d588f435df00/grpcio-1.78.0-cp312-cp312-win32.whl", hash = "sha256:94309f498bcc07e5a7d16089ab984d42ad96af1d94b5a4eb966a266d9fcabf68", size = 4066123, upload-time = "2026-02-06T09:55:47.644Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/ad/e7/d6914822c88aa2974dbbd10903d801a28a19ce9cd8bad7e694cbbcf61528/grpcio-1.78.0-cp312-cp312-win_amd64.whl", hash = "sha256:9566fe4ababbb2610c39190791e5b829869351d14369603702e890ef3ad2d06e", size = 4797657, upload-time = "2026-02-06T09:55:49.86Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/05/a9/8f75894993895f361ed8636cd9237f4ab39ef87fd30db17467235ed1c045/grpcio-1.78.0-cp313-cp313-linux_armv7l.whl", hash = "sha256:ce3a90455492bf8bfa38e56fbbe1dbd4f872a3d8eeaf7337dc3b1c8aa28c271b", size = 5920143, upload-time = "2026-02-06T09:55:52.035Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/55/06/0b78408e938ac424100100fd081189451b472236e8a3a1f6500390dc4954/grpcio-1.78.0-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:2bf5e2e163b356978b23652c4818ce4759d40f4712ee9ec5a83c4be6f8c23a3a", size = 11803926, upload-time = "2026-02-06T09:55:55.494Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/88/93/b59fe7832ff6ae3c78b813ea43dac60e295fa03606d14d89d2e0ec29f4f3/grpcio-1.78.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:8f2ac84905d12918e4e55a16da17939eb63e433dc11b677267c35568aa63fc84", size = 6478628, upload-time = "2026-02-06T09:55:58.533Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/ed/df/e67e3734527f9926b7d9c0dde6cd998d1d26850c3ed8eeec81297967ac67/grpcio-1.78.0-cp313-cp313-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:b58f37edab4a3881bc6c9bca52670610e0c9ca14e2ea3cf9debf185b870457fb", size = 7173574, upload-time = "2026-02-06T09:56:01.786Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/a6/62/cc03fffb07bfba982a9ec097b164e8835546980aec25ecfa5f9c1a47e022/grpcio-1.78.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:735e38e176a88ce41840c21bb49098ab66177c64c82426e24e0082500cc68af5", size = 6692639, upload-time = "2026-02-06T09:56:04.529Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/bf/9a/289c32e301b85bdb67d7ec68b752155e674ee3ba2173a1858f118e399ef3/grpcio-1.78.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:2045397e63a7a0ee7957c25f7dbb36ddc110e0cfb418403d110c0a7a68a844e9", size = 7268838, upload-time = "2026-02-06T09:56:08.397Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/0e/79/1be93f32add280461fa4773880196572563e9c8510861ac2da0ea0f892b6/grpcio-1.78.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:a9f136fbafe7ccf4ac7e8e0c28b31066e810be52d6e344ef954a3a70234e1702", size = 8251878, upload-time = "2026-02-06T09:56:10.914Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/65/65/793f8e95296ab92e4164593674ae6291b204bb5f67f9d4a711489cd30ffa/grpcio-1.78.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:748b6138585379c737adc08aeffd21222abbda1a86a0dca2a39682feb9196c20", size = 7695412, upload-time = "2026-02-06T09:56:13.593Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/1c/9f/1e233fe697ecc82845942c2822ed06bb522e70d6771c28d5528e4c50f6a4/grpcio-1.78.0-cp313-cp313-win32.whl", hash = "sha256:271c73e6e5676afe4fc52907686670c7cea22ab2310b76a59b678403ed40d670", size = 4064899, upload-time = "2026-02-06T09:56:15.601Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/4d/27/d86b89e36de8a951501fb06a0f38df19853210f341d0b28f83f4aa0ffa08/grpcio-1.78.0-cp313-cp313-win_amd64.whl", hash = "sha256:f2d4e43ee362adfc05994ed479334d5a451ab7bc3f3fee1b796b8ca66895acb4", size = 4797393, upload-time = "2026-02-06T09:56:17.882Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/29/f2/b56e43e3c968bfe822fa6ce5bca10d5c723aa40875b48791ce1029bb78c7/grpcio-1.78.0-cp314-cp314-linux_armv7l.whl", hash = "sha256:e87cbc002b6f440482b3519e36e1313eb5443e9e9e73d6a52d43bd2004fcfd8e", size = 5920591, upload-time = "2026-02-06T09:56:20.758Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/5d/81/1f3b65bd30c334167bfa8b0d23300a44e2725ce39bba5b76a2460d85f745/grpcio-1.78.0-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:c41bc64626db62e72afec66b0c8a0da76491510015417c127bfc53b2fe6d7f7f", size = 11813685, upload-time = "2026-02-06T09:56:24.315Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/0e/1c/bbe2f8216a5bd3036119c544d63c2e592bdf4a8ec6e4a1867592f4586b26/grpcio-1.78.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:8dfffba826efcf366b1e3ccc37e67afe676f290e13a3b48d31a46739f80a8724", size = 6487803, upload-time = "2026-02-06T09:56:27.367Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/16/5c/a6b2419723ea7ddce6308259a55e8e7593d88464ce8db9f4aa857aba96fa/grpcio-1.78.0-cp314-cp314-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:74be1268d1439eaaf552c698cdb11cd594f0c49295ae6bb72c34ee31abbe611b", size = 7173206, upload-time = "2026-02-06T09:56:29.876Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/df/1e/b8801345629a415ea7e26c83d75eb5dbe91b07ffe5210cc517348a8d4218/grpcio-1.78.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:be63c88b32e6c0f1429f1398ca5c09bc64b0d80950c8bb7807d7d7fb36fb84c7", size = 6693826, upload-time = "2026-02-06T09:56:32.305Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/34/84/0de28eac0377742679a510784f049738a80424b17287739fc47d63c2439e/grpcio-1.78.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:3c586ac70e855c721bda8f548d38c3ca66ac791dc49b66a8281a1f99db85e452", size = 7277897, upload-time = "2026-02-06T09:56:34.915Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/ca/9c/ad8685cfe20559a9edb66f735afdcb2b7d3de69b13666fdfc542e1916ebd/grpcio-1.78.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:35eb275bf1751d2ffbd8f57cdbc46058e857cf3971041521b78b7db94bdaf127", size = 8252404, upload-time = "2026-02-06T09:56:37.553Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/3c/05/33a7a4985586f27e1de4803887c417ec7ced145ebd069bc38a9607059e2b/grpcio-1.78.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:207db540302c884b8848036b80db352a832b99dfdf41db1eb554c2c2c7800f65", size = 7696837, upload-time = "2026-02-06T09:56:40.173Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/73/77/7382241caf88729b106e49e7d18e3116216c778e6a7e833826eb96de22f7/grpcio-1.78.0-cp314-cp314-win32.whl", hash = "sha256:57bab6deef2f4f1ca76cc04565df38dc5713ae6c17de690721bdf30cb1e0545c", size = 4142439, upload-time = "2026-02-06T09:56:43.258Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/48/b2/b096ccce418882fbfda4f7496f9357aaa9a5af1896a9a7f60d9f2b275a06/grpcio-1.78.0-cp314-cp314-win_amd64.whl", hash = "sha256:dce09d6116df20a96acfdbf85e4866258c3758180e8c49845d6ba8248b6d0bbb", size = 4929852, upload-time = "2026-02-06T09:56:45.885Z" },
+]
+
 [[package]]
 name = "idna"
 version = "3.10"
@@ -307,6 +373,15 @@ wheels = [
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/1e/e8/685f47e0d754320684db4425a0967f7d3fa70126bffd76110b7009a0090f/joblib-1.5.2-py3-none-any.whl", hash = "sha256:4e1f0bdbb987e6d843c70cf43714cb276623def372df3c22fe5266b2670bc241", size = 308396, upload-time = "2025-08-27T12:15:45.188Z" },
 ]
 
+[[package]]
+name = "markdown"
+version = "3.10.2"
+source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
+sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/2b/f4/69fa6ed85ae003c2378ffa8f6d2e3234662abd02c10d216c0ba96081a238/markdown-3.10.2.tar.gz", hash = "sha256:994d51325d25ad8aa7ce4ebaec003febcce822c3f8c911e3b17c52f7f589f950", size = 368805, upload-time = "2026-02-09T14:57:26.942Z" }
+wheels = [
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/de/1f/77fa3081e4f66ca3576c896ae5d31c3002ac6607f9747d2e3aa49227e464/markdown-3.10.2-py3-none-any.whl", hash = "sha256:e91464b71ae3ee7afd3017d9f358ef0baf158fd9a298db92f1d4761133824c36", size = 108180, upload-time = "2026-02-09T14:57:25.787Z" },
+]
+
 [[package]]
 name = "markupsafe"
 version = "3.0.2"
@@ -615,6 +690,15 @@ wheels = [
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/a2/eb/86626c1bbc2edb86323022371c39aa48df6fd8b0a1647bc274577f72e90b/nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5b17e2001cc0d751a5bc2c6ec6d26ad95913324a4adb86788c944f8ce9ba441f", size = 89954, upload-time = "2025-03-07T01:42:44.131Z" },
 ]
 
+[[package]]
+name = "packaging"
+version = "26.0"
+source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
+sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/65/ee/299d360cdc32edc7d2cf530f3accf79c4fca01e96ffc950d8a52213bd8e4/packaging-26.0.tar.gz", hash = "sha256:00243ae351a257117b6a241061796684b084ed1c516a08c48a3f7e147a9d80b4", size = 143416, upload-time = "2026-01-21T20:50:39.064Z" }
+wheels = [
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/b7/b9/c538f279a4e237a006a2c98387d081e9eb060d203d8ed34467cc0f0b9b53/packaging-26.0-py3-none-any.whl", hash = "sha256:b36f1fef9334a5588b4166f8bcd26a14e521f2b55e6b9de3aaa80d3ff7a37529", size = 74366, upload-time = "2026-01-21T20:50:37.788Z" },
+]
+
 [[package]]
 name = "pandas"
 version = "2.3.2"
@@ -772,6 +856,21 @@ wheels = [
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/cc/35/cc0aaecf278bb4575b8555f2b137de5ab821595ddae9da9d3cd1da4072c7/propcache-0.3.2-py3-none-any.whl", hash = "sha256:98f1ec44fb675f5052cccc8e609c46ed23a35a1cfd18545ad4e29002d858a43f", size = 12663, upload-time = "2025-06-09T22:56:04.484Z" },
 ]
 
+[[package]]
+name = "protobuf"
+version = "6.33.5"
+source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
+sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/ba/25/7c72c307aafc96fa87062aa6291d9f7c94836e43214d43722e86037aac02/protobuf-6.33.5.tar.gz", hash = "sha256:6ddcac2a081f8b7b9642c09406bc6a4290128fce5f471cddd165960bb9119e5c", size = 444465, upload-time = "2026-01-29T21:51:33.494Z" }
+wheels = [
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/b1/79/af92d0a8369732b027e6d6084251dd8e782c685c72da161bd4a2e00fbabb/protobuf-6.33.5-cp310-abi3-win32.whl", hash = "sha256:d71b040839446bac0f4d162e758bea99c8251161dae9d0983a3b88dee345153b", size = 425769, upload-time = "2026-01-29T21:51:21.751Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/55/75/bb9bc917d10e9ee13dee8607eb9ab963b7cf8be607c46e7862c748aa2af7/protobuf-6.33.5-cp310-abi3-win_amd64.whl", hash = "sha256:3093804752167bcab3998bec9f1048baae6e29505adaf1afd14a37bddede533c", size = 437118, upload-time = "2026-01-29T21:51:24.022Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/a2/6b/e48dfc1191bc5b52950246275bf4089773e91cb5ba3592621723cdddca62/protobuf-6.33.5-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:a5cb85982d95d906df1e2210e58f8e4f1e3cdc088e52c921a041f9c9a0386de5", size = 427766, upload-time = "2026-01-29T21:51:25.413Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/4e/b1/c79468184310de09d75095ed1314b839eb2f72df71097db9d1404a1b2717/protobuf-6.33.5-cp39-abi3-manylinux2014_aarch64.whl", hash = "sha256:9b71e0281f36f179d00cbcb119cb19dec4d14a81393e5ea220f64b286173e190", size = 324638, upload-time = "2026-01-29T21:51:26.423Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/c5/f5/65d838092fd01c44d16037953fd4c2cc851e783de9b8f02b27ec4ffd906f/protobuf-6.33.5-cp39-abi3-manylinux2014_s390x.whl", hash = "sha256:8afa18e1d6d20af15b417e728e9f60f3aa108ee76f23c3b2c07a2c3b546d3afd", size = 339411, upload-time = "2026-01-29T21:51:27.446Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/9b/53/a9443aa3ca9ba8724fdfa02dd1887c1bcd8e89556b715cfbacca6b63dbec/protobuf-6.33.5-cp39-abi3-manylinux2014_x86_64.whl", hash = "sha256:cbf16ba3350fb7b889fca858fb215967792dc125b35c7976ca4818bee3521cf0", size = 323465, upload-time = "2026-01-29T21:51:28.925Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/57/bf/2086963c69bdac3d7cff1cc7ff79b8ce5ea0bec6797a017e1be338a46248/protobuf-6.33.5-py3-none-any.whl", hash = "sha256:69915a973dd0f60f31a08b8318b73eab2bd6a392c79184b3612226b0a3f8ec02", size = 170687, upload-time = "2026-01-29T21:51:32.557Z" },
+]
+
 [[package]]
 name = "psutil"
 version = "7.0.0"
@@ -973,6 +1072,36 @@ wheels = [
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353, upload-time = "2025-04-27T18:04:59.103Z" },
 ]
 
+[[package]]
+name = "tensorboard"
+version = "2.20.0"
+source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
+dependencies = [
+    { name = "absl-py" },
+    { name = "grpcio" },
+    { name = "markdown" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pillow" },
+    { name = "protobuf" },
+    { name = "setuptools" },
+    { name = "tensorboard-data-server" },
+    { name = "werkzeug" },
+]
+wheels = [
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/9c/d9/a5db55f88f258ac669a92858b70a714bbbd5acd993820b41ec4a96a4d77f/tensorboard-2.20.0-py3-none-any.whl", hash = "sha256:9dc9f978cb84c0723acf9a345d96c184f0293d18f166bb8d59ee098e6cfaaba6", size = 5525680, upload-time = "2025-07-17T19:20:49.638Z" },
+]
+
+[[package]]
+name = "tensorboard-data-server"
+version = "0.7.2"
+source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
+wheels = [
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/7a/13/e503968fefabd4c6b2650af21e110aa8466fe21432cd7c43a84577a89438/tensorboard_data_server-0.7.2-py3-none-any.whl", hash = "sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/b7/85/dabeaf902892922777492e1d253bb7e1264cadce3cea932f7ff599e53fea/tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/73/c6/825dab04195756cf8ff2e12698f22513b3db2f64925bdd41671bfb33aaa5/tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:ef687163c24185ae9754ed5650eb5bc4d84ff257aabdc33f0cc6f74d8ba54530" },
+]
+
 [[package]]
 name = "threadpoolctl"
 version = "3.6.0"
@@ -1120,6 +1249,18 @@ wheels = [
     { url = "https://pypi.tuna.tsinghua.edu.cn/packages/a7/c2/fe1e52489ae3122415c51f387e221dd0773709bad6c6cdaa599e8a2c5185/urllib3-2.5.0-py3-none-any.whl", hash = "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc", size = 129795, upload-time = "2025-06-18T14:07:40.39Z" },
 ]
 
+[[package]]
+name = "werkzeug"
+version = "3.1.5"
+source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
+dependencies = [
+    { name = "markupsafe" },
+]
+sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/5a/70/1469ef1d3542ae7c2c7b72bd5e3a4e6ee69d7978fa8a3af05a38eca5becf/werkzeug-3.1.5.tar.gz", hash = "sha256:6a548b0e88955dd07ccb25539d7d0cc97417ee9e179677d22c7041c8f078ce67", size = 864754, upload-time = "2026-01-08T17:49:23.247Z" }
+wheels = [
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/ad/e4/8d97cca767bcc1be76d16fb76951608305561c6e056811587f36cb1316a8/werkzeug-3.1.5-py3-none-any.whl", hash = "sha256:5111e36e91086ece91f93268bb39b4a35c1e6f1feac762c9c822ded0a4e322dc", size = 225025, upload-time = "2026-01-08T17:49:21.859Z" },
+]
+
 [[package]]
 name = "yarl"
 version = "1.20.1"