human
Solve the following python programming problem given the following code:
# DetNet config
layers = 3*K
v_len = 2*K
z_len = 8*K

# Training params
training_steps = 10000
batch_size_train = 5000
snr_var_train = 3.0 # Maximum absolute deviation of the SNR from its mean in logarithmic scale.

# Test params
test_steps= 1000
batch_size_test = 5000
snr_range = np.arange(8, 14, 1)

# Definition of the Loss function
def own_loss(t, t_train, t_ZF):
    loss_l = torch.zeros(len(t), 1, device=device)        # Denotes the loss in Layer L
    for layer in range(1,len(t)+1):
        loss_l[layer-1] = torch.log(torch.Tensor([layer+1]).to(device))*torch.mean(torch.mean(torch.square(t_train - t[layer-1]),1)/torch.mean(torch.square(t_train - t_ZF),1))
    return loss_l
     

# Definition of the DetNet
class DetNet(nn.Module):
    # Build DetNet
    def __init__(self, layers, K, v_len, z_len):
        # Here we define the trainable parameter (Net)
        super(DetNet, self).__init__()
        # We have to use here nn.ModuleList instead of a PythonList. (Otherwise you’ll get an error saying
        # that your model has no parameters, because PyTorch does not see the parameters of the layers stored
        # in a Python list)
        # Furtheremore, we initialize the linear trafo with normailzed weights
        # Linear Traffos W_1l, W_2l, W_3l
        self.linear_trafo_1_l = nn.ModuleList()
        self.linear_trafo_1_l.extend([nn.Linear(3*K + v_len, z_len) for i in range(1, layers+1)])
        for i in range(0, layers):
            nn.init.normal_(self.linear_trafo_1_l[i].weight, std = 0.01)
            nn.init.normal_(self.linear_trafo_1_l[i].bias, std = 0.01)
        self.linear_trafo_2_l = nn.ModuleList()
        self.linear_trafo_2_l.extend([nn.Linear(z_len, K) for i in range(1, layers+1)])
        for i in range(0, layers):
            nn.init.normal_(self.linear_trafo_2_l[i].weight, std = 0.01)
            nn.init.normal_(self.linear_trafo_2_l[i].bias, std = 0.01)
        self.linear_trafo_3_l = nn.ModuleList()
        self.linear_trafo_3_l.extend([nn.Linear(z_len , v_len) for i in range(1,...
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Configure and build the neural network.
Each layer has the 4 inputs $\mathbf{H}^{T}\mathbf{r}$, $\mathbf{H}^{T}\mathbf{H}$, $\mathbf{t}_k$ and $\mathbf{v}_k$. The index $k$ denotes the layer. The layers can also be interpreted as iterations of an optimization algorithm [1].
The nonlinear operation
$
\begin{align}
&\quad z_{k} = \rho\left(\text{W}{1k}\begin{bmatrix}
\mathbf{H}^{T}\mathbf{r}\
\hat{\mathbf{t}}{k}\
\mathbf{H}^{T}\mathbf{H}\hat{\text{t}}{k}\
\mathbf{v}{k}
\end{bmatrix}+\mathbf{b}{1k}\right)\
&\hat{\mathbf{t}}{k+1} = \psi_{t_{k}}(\mathbf{W}{2k}\mathbf{z}{k}+\mathbf{b}{2k})\
&\hat{\mathbf{v}}{k+1} = \mathbf{W}{3k}\mathbf{z}{k}+\mathbf{b}{3k}\
&\qquad\hat{\mathbf{t}}{1} = \mathbf{0}\tag{10}
\end{align}
$
is applied to the input. $\mathbf{t}_0$ is the received data vector.
Summarized, each layer does roughly the following steps:
* Concatenate the inputs.
* Linear transformation.
* Apply ReLU function.
* Calculate $\mathbf{v}{k+1}$ as a linear trafo of the ReLU output and use ResNet feature.
* Calculate $\hat{\mathbf{t}}{k+1}$ as a linear trafo of the ReLU output which is then fed to the linear soft sign function.