给出具体的pytorch代码，要求输入注意力模块的为经过CLIP文本编码器的1×512的文本特征和经过resnet50输出的1×512的图像特征。

下面是一个示例的PyTorch代码，演示了如何使用CLIP文本编码器和ResNet-50模型来构建注意力模块的输入，并进行训练：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.models as models
from transformers import CLIPModel, CLIPProcessor

# 构建CLIP模型和处理器
clip_model = CLIPModel.from_pretrained('openai/clip-vit-base-patch32')
clip_processor = CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32')

# 加载ResNet-50模型
resnet_model = models.resnet50(pretrained=True)
resnet_model.fc = nn.Identity()  # 去掉最后的全连接层

# 定义注意力模块
class AttentionModule(nn.Module):
    def __init__(self):
        super(AttentionModule, self).__init__()
        self.fc1 = nn.Linear(512, 256)
        self.fc2 = nn.Linear(512, 256)
        
    def forward(self, text_features, image_features):
        text_attention = self.fc1(text_features)
        image_attention = self.fc2(image_features)
        attention_scores = torch.matmul(text_attention, image_attention.T)
        attention_weights = torch.softmax(attention_scores, dim=1)
        attended_text_features = torch.matmul(attention_weights, text_features)
        attended_image_features = torch.matmul(attention_weights.T, image_features)
        return attended_text_features, attended_image_features

# 创建注意力模块实例
attention_module = AttentionModule()

# 定义损失函数和优化器
loss_fn = nn.MSELoss()
optimizer = optim.Adam(attention_module.parameters(), lr=0.001)

# 准备示例输入数据
text_input = "example text"
image_input = torch.randn(1, 3, 224, 224)  # 示例图像输入

# 进行输入数据的预处理
text_inputs = clip_processor(text_input, return_tensors="pt", padding=True)
image_inputs = clip_processor(images=image_input, return_tensors="pt", padding=True)

# 获取CLIP文本编码器的特征
with torch.no_grad():
    text_features = clip_model.get_text_features(**text_inputs).to(device)

# 获取ResNet-50模型的特征
with torch.no_grad():
    image_features = resnet_model(image_inputs['pixel_values'].to(device))

# 将特征输入到注意力模块，并计算输出
attended_text_features, attended_image_features = attention_module(text_features, image_features)

# 计算损失并进行反向传播
loss = loss_fn(attended_text_features, attended_image_features)
optimizer.zero_grad()
loss.backward()
optimizer.step()

请注意，这只是一个示例代码，具体的实现方式和参数设置可能需要根据你的具体需求进行调整。此外，你还需要根据实际情况调整模型和训练过程中的超参数，以达到最佳性能。