class OverlapPatchEmbed(nn.Module):

这是一个 PyTorch 中的自定义模块，用于将输入的图像划分成若干个重叠的补丁，并将每个补丁转换为向量形式。具体实现请参考以下代码：

import torch.nn as nn
import torch.nn.functional as F

class OverlapPatchEmbed(nn.Module):
    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
        super().__init__()
        self.img_size = img_size
        self.patch_size = patch_size
        self.n_patches = (img_size // patch_size) ** 2
        self.patch_embed = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        self.proj = nn.Linear(embed_dim * (patch_size ** 2), embed_dim)

    def forward(self, x):
        # Input shape: [batch_size, channels, height, width]
        patches = self.patch_embed(x)  # [batch_size, embed_dim, n_patches_sqrt, n_patches_sqrt]
        patches = patches.flatten(2)  # [batch_size, embed_dim, n_patches]
        patches = patches.transpose(1, 2)  # [batch_size, n_patches, embed_dim]
        patches = self.proj(patches)  # [batch_size, n_patches, embed_dim]
        return patches

其中，`img_size` 表示输入图像的大小，`patch_size` 表示每个补丁的大小，`in_chans` 表示输入图像的通道数，`embed_dim` 表示每个补丁转换后的向量维度。 `patch_embed` 使用 `nn.Conv2d` 对输入图像进行划分，`proj` 使用 `nn.Linear` 将每个补丁的向量维度转换为 `embed_dim`。在 `forward` 方法中，首先对输入图像进行划分得到补丁，然后将补丁的向量维度转换为 `embed_dim`，最后返回补丁的向量表示。