请你根据我给出的代码片段，帮我转换成pytorch模型

好的，以下是将提供的Keras代码片段转换为PyTorch版本的基本步骤：

import torch.nn as nn
import torch

# 注意力块函数的转换
class AttentionBlock(nn.Module):
    def __init__(self, feature_cnt, dim):
        super(AttentionBlock, self).__init__()
        self.feature_cnt = feature_cnt
        self.dim = dim
        self.fc = nn.Linear(self.feature_cnt * dim, feature_cnt * dim)
        self.softmax = nn.Softmax(dim=1)
        self.reshape = nn.Sequential(nn.Flatten(), nn.Linear(dim, (feature_cnt, dim)))
        self.reduce_dim = nn.Linear(dim, 1, bias=False)

    def forward(self, inputs):
        attention_weights = self.fc(inputs).reshape(-1, self.feature_cnt, self.dim)
        attention_weights = self.softmax(attention_weights)
        attention_weights = attention_weights.unsqueeze(2)
        attention_out = torch.bmm(inputs, attention_weights)
        return attention_out

# Pixel Branch的转换
class PixelBranch(nn.Module):
    def __init__(self):
        super(PixelBranch, self).__init__()
        self.conv_layers = nn.ModuleList([
            nn.Conv1d(8, 16, kernel_size=11, stride=1, padding=(11 - 1) // 2),
            AttentionBlock(170, 64),
            nn.Conv1d(8+170, 16, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2, stride=2),
            nn.Conv1d(16, 16, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2, stride=2),
            nn.Conv1d(16, 16, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2, stride=2),
            nn.Flatten()
        ])

    def forward(self, input_tensor):
        for layer in self.conv_layers:
            input_tensor = layer(input_tensor)
        return input_tensor

# 创建PyTorch模型实例
model = PixelBranch()

这段代码定义了一个名为`PixelBranch`的PyTorch模块，包含了与Keras代码类似的结构，包括1D卷积、注意力块、非线性激活、池化和展平操作。请注意，在实际使用中，您可能需要根据具体需求进行进一步的修改和调整。