I still cannot reproduce this error using:
class ResNet(nn.Module):
    def __init__(self, Block, layers, img_channels, seq):
        super(ResNet, self).__init__()
        self.sequence = seq
        self.in_channels = 64*self.sequence
        self.temporal_channels = 64
        self.conv_init = nn.Conv2d(img_channels*self.sequence, 64*self.sequence, kernel_size = 4,
                                                 stride = 2, padding = 1, groups = self.sequence)
        self.bn = nn.BatchNorm2d(64*self.sequence)
        self.relu = nn.ReLU()
        self.maxpool = nn.MaxPool2d(3, stride = 2, padding = 1)
        self.layer1 = self._make_layer(Block, layers[0], 1)
        self.layer2 = self._make_layer(Block, layers[1], 2)
        self.layer3 = self._make_layer(Block, layers[2], 4)
        self.layer4 = self._make_layer(Block, layers[3], 8)
       
    def _make_layer(self, Block, num_blocks, step):
        layers = []
        channels = self.in_channels * step
        for i in range(num_blocks):
            layers.append(Block(channels, self.sequence))
        return nn.Sequential(*layers, nn.Conv2d(channels, channels*2, 3, stride = 2, padding = 1, groups = self.sequence),
                                      nn.BatchNorm2d(channels*2))
    
    def forward(self, x):
        temporal_list = []
        x = self.conv_init(x)
        x = self.bn(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        
        return x


model = ResNet(Block, [2, 2, 2, 2], 3, 1)
x = torch.randn(1, 3, 224, 224)
out = model(x)

Could you post an executable code snippet using random input data and show how you are creating the model so that I could reproduce it?