PyTorch Implementation of a Network Architecture for Time Series Prediction

import torch.nn as nn

class Network(nn.Module): def init(self): super(Network, self).init()

    self.dense_layers = nn.Sequential(
        nn.Linear(100, 500),
        nn.Linear(500, 1000),
        nn.Linear(1000, 1500),
        nn.Linear(1500, 500),
        nn.Linear(500, 2000),
        nn.Linear(2000, 1000),
        nn.Linear(1000, 500),
        nn.Linear(500, 165),
    )
    
    self.transposed_layers = nn.Sequential(
        nn.ConvTranspose1d(165, 165, kernel_size=8),
        nn.ConvTranspose1d(165, 165, kernel_size=4),
        nn.ConvTranspose1d(165, 330, kernel_size=4),
    )
    
    self.conv_layer = nn.Conv1d(1, 4, kernel_size=4, stride=1)
    
def forward(self, x):
    x = self.dense_layers(x)
    x = x.unsqueeze(1)
    x = self.transposed_layers(x)
    x = self.conv_layer(x)
    x = x[:, :, 15:-15]
    return x.squeeze()