How to retrieve weights of individual layers from sequential .network

"""         Sequence of operations           """

        mixture_w , mixture_ws = self.encoder(mixture)
        est_mask = self.separator(mixture_w)
        est_source = self.decoder(mixture_ws, est_mask)

class Encoder(nn.Module):
    """Estimation of the nonnegative mixture weight by a 1-D conv layer.
    def __init__(self, L, N):
        super(Encoder, self).__init__()
        # Hyper-parameter
        self.L, self.N = L, N
        # Components
        # 50% overlap
        self.conv1d_U = nn.Conv1d(1, N, kernel_size=L, stride=L // 2, bias=False)

    def forward(self, mixture):
            mixture: [M, T], M is batch size, T is #samples
            mixture_w: [M, N, K], where K = (T-L)/(L/2)+1 = 2T/L-1
        mixture = torch.unsqueeze(mixture, 1)  # [M, 1, T]
        mixture_w = F.relu(self.conv1d_U(mixture))  # [M, N, K]
        mixture_ws = F.sigmoid(self.conv1d_U(mixture))
        return mixture_w

From the above code in the model, I am able to retrieve weights of conv1d_U shown below:
by using model.encoder.con1d_U.weight

 self.conv1d_U = nn.Conv1d(1, N, kernel_size=L, stride=L // 2, bias=False)

When I tried to retrieve weights of bottleneck_conv1x1 shown in the code below. I couldn’t find bottleneck_conv1x1 in the
Can you explain me how to retrieve weights of individual layers? when they are sequentially coded in a .network, like the one in my case?

class TemporalConvNet(nn.Module):
    def __init__(self, N, B, H, P, X, R, C, norm_type="gLN", causal=False,
            N: Number of filters in autoencoder
            B: Number of channels in bottleneck 1 × 1-conv block
            H: Number of channels in convolutional blocks
            P: Kernel size in convolutional blocks
            X: Number of convolutional blocks in each repeat
            R: Number of repeats
            C: Number of speakers
            norm_type: BN, gLN, cLN
            causal: causal or non-causal
            mask_nonlinear: use which non-linear function to generate mask
        super(TemporalConvNet, self).__init__()
        # Hyper-parameter
        self.C = C
        self.mask_nonlinear = mask_nonlinear
        # Components
        # [M, N, K] -> [M, N, K]
#        chomp = Chomp1d(padding)
        layer_norm = ChannelwiseLayerNorm(N)
        # [M, N, K] -> [M, B, K]
        bottleneck_conv1x1 = nn.Conv1d(N, B, 1, bias=False)
        # [M, B, K] -> [M, B, K]
        repeats = []
        for self.r in range(R):
            blocks = []
            for self.x in range(X):
                dilation = 2**self.x
                padding = (P - 1) * dilation if causal else (P - 1) * dilation // 2
                blocks += [TemporalBlock(B, H, P, stride=1,
            repeats += [nn.Sequential(*blocks)]
        temporal_conv_net = nn.Sequential(*repeats)
        # [M, B, K] -> [M, C*N, K]
        mask_conv1x1 = nn.Conv1d(B, C*N, 1, bias=False)
        # Put together = nn.Sequential(layer_norm,

    def forward(self, mixture_w):
        Keep this API same with TasNet
            mixture_w: [M, N, K], M is batch size
            est_mask: [M, C, N, K]
        M, N, K = mixture_w.size()
        score =  # [M, N, K] -> [M, C*N, K]
        score = score.view(M, self.C, N, K) # [M, C*N, K] -> [M, C, N, K]
        if self.mask_nonlinear == 'softmax':
            est_mask = F.softmax(score, dim=1)
        elif self.mask_nonlinear == 'relu':
            est_mask = F.relu(score)
        elif self.mask_nonlinear == 'sigmoid':
            est_mask = F.sigmoid(score)
        elif self.mask_nonlinear == 'softsign':
            est_mask = self.softsign(score)
            raise ValueError("Unsupported mask non-linear function")
        return est_mask

The bottleneck_conv1x1 layer should be accessible using[1].
I’m not sure, what separator refers to, but if you are assigning TemporalConvNet to it, it should be something like[1] instead.