The easiest solution would be to put spectral_norm around the nn.Conv2d calls inside your Myconvo2d class. For example,
import torch
from torch import nn
from torch.nn.utils import spectral_norm


class MyConvo2d(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.my_conv = nn.Sequential(
            spectral_norm(nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=3, stride=1, padding=1)),
            nn.ReLU(),
            spectral_norm(nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1)),
            nn.ReLU()
        )

    def forward(self, x):
        return self.my_conv(x)


x = torch.rand(size=(1, 1, 4, 4))
Net = MyConvo2d(1, 1)
print(Net(x))