Create a sequence of convolutional (`ni` to `nf`), ReLU (if `use_activ`) and batchnorm (if `bn`) layers.

def conv_layer(ni:int, nf:int, ks:int=3, stride:int=1, padding:int=None, bias:bool=None, is_1d:bool=False,
               norm_type:Optional[NormType]=NormType.Batch,  use_activ:bool=True, leaky:float=None,
               transpose:bool=False, init:Callable=nn.init.kaiming_normal_, self_attention:bool=False):
    "Create a sequence of convolutional (`ni` to `nf`), ReLU (if `use_activ`) and batchnorm (if `bn`) layers."
    if padding is None: padding = (ks-1)//2 if not transpose else 0
    bn = norm_type in (NormType.Batch, NormType.BatchZero)
    if bias is None: bias = not bn
    conv_func = nn.ConvTranspose2d if transpose else nn.Conv1d if is_1d else nn.Conv2d
    conv = init_default(conv_func(ni, nf, kernel_size=ks, bias=bias, stride=stride, padding=padding), init)
    if   norm_type==NormType.Weight:   conv = weight_norm(conv)
    elif norm_type==NormType.Spectral: conv = spectral_norm(conv)
    layers = [conv]
    if use_activ: layers.append(relu(True, leaky=leaky))
    if bn: layers.append((nn.BatchNorm1d if is_1d else nn.BatchNorm2d)(nf))
    if self_attention: layers.append(SelfAttention(nf))
    return nn.Sequential(*layers)