def METHOD_NAME(self, layer_config, input_channels):
    """
    Function to create convolutional layers for the VGG model based on the given layer configuration
    for VGG based models including VGG11, VGG13, VGG16, VGG19.
    Args:
        layer_config (list): A list containing the configuration of convolutional layers and max pooling layers
        in_channels (int): The number of input channels
    Returns:
        nn.Sequential: A sequential module containing the convolutional layers
    """
    layers = []
    for layer in layer_config:
        if layer == "M":
            # If you found M, then add a maxpool layer
            layers += [self.MaxPool(kernel_size=2, stride=2)]
        else:
            # Otherwise, add a convolutional layer with the number of channels
            conv = self.Conv(input_channels, layer, kernel_size=3, padding=1)
            if self.norm_type in ["batch", "instance"]:
                layers += [conv, self.Norm(layer), nn.ReLU(inplace=True)]
            else:
                layers += [conv, nn.ReLU(inplace=True)]
            input_channels = layer
    return nn.Sequential(*layers)