请用PyTorch提供了的预先实现好的ConvLSTM模型写一个神经网络模型

好的，以下是一个简单的ConvLSTM模型的实现：

import torch
import torch.nn as nn

class ConvLSTM(nn.Module):
    def __init__(self, input_size, hidden_size, kernel_size, num_layers):
        super(ConvLSTM, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.kernel_size = kernel_size
        self.num_layers = num_layers
        self.padding = kernel_size // 2
        
        self.conv_lstm = nn.ModuleList()
        for i in range(num_layers):
            input_dim = input_size if i == 0 else hidden_size
            convlstm = nn.Sequential(
                nn.Conv2d(input_dim + hidden_size, 4 * hidden_size, kernel_size, 1, self.padding),
                nn.BatchNorm2d(4 * hidden_size),
                nn.ReLU(inplace=True)
            )
            self.conv_lstm.append(convlstm)
            
    def forward(self, x, hidden_states=None):
        if hidden_states is None:
            hidden_states = self._init_hidden(batch_size=x.size(0), device=x.device)
        
        h, c = hidden_states
        outputs = []
        
        for layer_idx in range(self.num_layers):
            # Prepare input tensor
            if layer_idx == 0:
                layer_input = x
            else:
                layer_input = outputs[-1][0]
                
            # Concatenate hidden state with input tensor along channel dimension
            cat_input = torch.cat([layer_input, h[layer_idx]], dim=1)
            
            # Forward pass through ConvLSTM cell
            cell_output = self.conv_lstm[layer_idx](cat_input)
            cc_i, cc_f, cc_o, cc_g = torch.split(cell_output, self.hidden_size, dim=1)
            i = torch.sigmoid(cc_i)
            f = torch.sigmoid(cc_f)
            o = torch.sigmoid(cc_o)
            g = torch.tanh(cc_g)
            c[layer_idx] = f * c[layer_idx] + i * g
            h[layer_idx] = o * torch.tanh(c[layer_idx])
            
            # Save output and hidden state for next layer
            outputs.append((h[layer_idx], c[layer_idx]))
            
        return outputs[-1][0], outputs
    
    def _init_hidden(self, batch_size, device):
        init_states = []
        for i in range(self.num_layers):
            init_states.append(
                (torch.zeros(batch_size, self.hidden_size, self.input_size, self.input_size).to(device),
                 torch.zeros(batch_size, self.hidden_size, self.input_size, self.input_size).to(device))
            )
        return init_states

该模型包括了多层的ConvLSTM单元，每一层都由一个4个卷积层的神经网络组成，其中输入是当前层的输入和上一层的隐藏状态，输出是当前层的输出和新的隐藏状态。在每一层中，我们首先将输入张量和上一层的隐藏状态张量进行拼接，然后将其传递给ConvLSTM单元进行前向传递，最后输出当前层的输出和新的隐藏状态，用于下一层的输入。该模型的前向传递方法接受一个输入张量和一个可选的隐藏状态张量（默认为None），并返回最后一层的输出张量和所有层的输出和隐藏状态。