def wider_next_conv(layer, start_dim, total_dim, n_add, weighted=True):
    '''wider next conv layer.
    '''
    n_dim = get_n_dim(layer)
    if not weighted:
        return get_conv_class(n_dim)(layer.input_channel + n_add,
                                     layer.filters,
                                     kernel_size=layer.kernel_size,
                                     stride=layer.stride)
    n_filters = layer.filters
    teacher_w, teacher_b = layer.get_weights()

    new_weight_shape = list(teacher_w.shape)
    new_weight_shape[1] = n_add
    new_weight = np.zeros(tuple(new_weight_shape))

    student_w = np.concatenate((teacher_w[:, :start_dim, ...].copy(),
                                add_noise(new_weight, teacher_w),
                                teacher_w[:, start_dim:total_dim, ...].copy()), axis=1)
    new_layer = get_conv_class(n_dim)(layer.input_channel + n_add,
                                      n_filters,
                                      kernel_size=layer.kernel_size,
                                      stride=layer.stride)
    new_layer.set_weights((student_w, teacher_b))
    return new_layer
wider next conv layer.