I am trying to convert a lasagne DNN to Pytorch DNN, so far this is my translation. I am not sure if this is correct. Can someone please confirm.
l_in = lasagne.layers.InputLayer(
    shape=(None, 1, in_height, in_width),
)

l_hid = lasagne.layers.DenseLayer(
    l_in,
    num_units=200,
    nonlinearity=lasagne.nonlinearities.rectify,
    W=lasagne.init.Normal(.0201),
    b=lasagne.init.Constant(0)
)

l_out = lasagne.layers.DenseLayer(
    l_hid,
    num_units=out_length,
    nonlinearity=lasagne.nonlinearities.softmax,
    W=lasagne.init.Normal(.001),
    b=lasagne.init.Constant(0)
)

return l_out

def init(self, pa):
super().init()
# MLP
# Inputs to hidden layer linear transformation
self.hidden = nn.Linear(int(self.input_width) , int(self.input_height))
self.hidden.weight.data.fill_(0.0201)
self.hidden.bias.data.fill_(0)
self.relu = nn.ReLU()
    # Output layer
    self.output = nn.Linear(int(self.input_height), int(self.output_height))
    self.output.weight.data.fill_(0.001)
    self.output.bias.data.fill_(0)
    self.softmax = nn.Softmax(dim=1)
    
def forward(self, x):
    hid = self.hidden(x)
    hid_relu = self.relu(hid)
    out = self.output(hid_relu)
    output = self.softmax(out)
    
    return output