帮我实现一个informer的结构

好的，以下是一个简单的 Informer 结构的实现代码：

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

class InpConv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0):
        super(InpConv, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding)
        self.norm = nn.LayerNorm(out_channels)

    def forward(self, x):
        x = self.conv(x)
        x = self.norm(x)
        x = F.relu(x)
        return x

class EncoderLayer(nn.Module):
    def __init__(self, d_model, n_heads, dim_feedforward=2048, dropout=0.1):
        super(EncoderLayer, self).__init__()
        self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
        self.norm1 = nn.LayerNorm(d_model)
        self.feed_forward = nn.Sequential(
            nn.Linear(d_model, dim_feedforward),
            nn.ReLU(),
            nn.Linear(dim_feedforward, d_model),
            nn.Dropout(dropout)
        )
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        attn_out, _ = self.self_attn(x, x, x)
        x = x + self.dropout(attn_out)
        x = self.norm1(x)
        ff_out = self.feed_forward(x)
        x = x + self.dropout(ff_out)
        x = self.norm2(x)
        return x

class InpEmbedding(nn.Module):
    def __init__(self, d_model, img_size, patch_size):
        super(InpEmbedding, self).__init__()
        self.img_size = img_size
        self.patch_size = patch_size
        self.n_patches = (img_size // patch_size) ** 2
        self.patch_embedding = nn.Conv2d(3, d_model, kernel_size=patch_size, stride=patch_size)
        self.pos_embedding = nn.Parameter(torch.zeros(1, self.n_patches + 1, d_model))

    def forward(self, x):
        x = self.patch_embedding(x)
        x = x.flatten(2).transpose(1, 2)
        x = torch.cat([self.pos_embedding.repeat(x.shape[0], 1, 1), x], dim=1)
        return x

class InpEncoder(nn.Module):
    def __init__(self, d_model, n_heads, dim_feedforward, dropout, img_size, patch_size, n_layers):
        super(InpEncoder, self).__init__()
        self.embedding = InpEmbedding(d_model, img_size, patch_size)
        self.layers = nn.ModuleList([EncoderLayer(d_model, n_heads, dim_feedforward, dropout) for _ in range(n_layers)])

    def forward(self, x):
        x = self.embedding(x)
        for layer in self.layers:
            x = layer(x)
        return x

class InpPooler(nn.Module):
    def __init__(self, d_model):
        super(InpPooler, self).__init__()
        self.fc = nn.Linear(d_model, d_model)

    def forward(self, x):
        x = x[:, 0]
        x = self.fc(x)
        x = F.tanh(x)
        return x

class InpClassificationHead(nn.Module):
    def __init__(self, d_model, n_classes):
        super(InpClassificationHead, self).__init__()
        self.fc = nn.Linear(d_model, n_classes)

    def forward(self, x):
        x = self.fc(x)
        return x

class InpTransformer(nn.Module):
    def __init__(self, d_model=768, n_heads=12, dim_feedforward=2048, dropout=0.1, img_size=224, patch_size=16, n_layers=12, n_classes=1000):
        super(InpTransformer, self).__init__()
        self.encoder = InpEncoder(d_model, n_heads, dim_feedforward, dropout, img_size, patch_size, n_layers)
        self.pooler = InpPooler(d_model)
        self.classification_head = InpClassificationHead(d_model, n_classes)

    def forward(self, x):
        x = self.encoder(x)
        x = self.pooler(x)
        x = self.classification_head(x)
        return x

这是一个简单的 InpConv + Transformer 的结构，用于图像分类任务。其中 InpConv 用于对输入图像进行卷积和归一化，InpEmbedding 用于将图像转换为嵌入向量，InpEncoder 用于对嵌入向量进行编码，InpPooler 用于对编码后的向量进行池化，InpClassificationHead 用于对池化后的向量进行分类。