diff --git a/src/mace/mace.py b/src/mace/mace.py
index 616c9f4..b4c8825 100644
--- a/src/mace/mace.py
+++ b/src/mace/mace.py
@@ -13,7 +13,6 @@
 import torchode             as to      # Lienen, M., & Günnemann, S. 2022, in The Symbiosis of Deep Learning and Differential Equations II, NeurIPS. https://openreview.net/forum?id=uiKVKTiUYB0
 import src.mace.autoencoder as ae
 import src.mace.latentODE   as lODE
-from scipy.stats            import gmean
 from time                   import time
 
 
@@ -199,87 +198,90 @@ def forward(self, n_0, p, tstep):
 ## ---------- OLD VERSION OF THE SOLVER CLASS ---------- ##
 ## This class is compatible with an older version of the autoencoder
 
-class Solver_old(nn.Module):
-    '''
-    The Solver class presents the architecture of MACE.
-    Components:
-        1) Encoder; neural network with adjustable amount of nodes and layers
-        2) Neural ODE; ODE given by function g, with trainable elements 
-        3) Decoder; neural network with adjustable amount of nodes and layers
-
-    '''
-    def __init__(self, p_dim, z_dim, DEVICE,  n_dim=466, g_nn = False, atol = 1e-5, rtol = 1e-2):
-        super(Solver_old, self).__init__() # type: ignore
-
-        self.status_train = list()
-        self.status_test = list()
-
-        self.z_dim = z_dim
-        self.n_dim = n_dim
-        self.DEVICE = DEVICE
-        self.g_nn = g_nn
-
-        ## Setting the neural ODE
-        input_ae_dim  = n_dim
-        if not self.g_nn:
-            self.g = lODE.G(z_dim)
-            input_ae_dim  = input_ae_dim+p_dim
-            self.odeterm = to.ODETerm(self.g, with_args=False)
-        if self.g_nn:
-            self.g = lODE.Gnn(p_dim, z_dim)
-            self.odeterm = to.ODETerm(self.g, with_args=True)
-
-        self.step_method          = to.Dopri5(term=self.odeterm)
-        self.step_size_controller = to.IntegralController(atol=atol, rtol=rtol, term=self.odeterm)
-        self.adjoint              = to.AutoDiffAdjoint(self.step_method, self.step_size_controller).to(self.DEVICE) # type: ignore
-
-        self.jit_solver = torch.compile(self.adjoint)
-
-        ## Setting the autoencoder (enocder + decoder)
-        hidden_ae_dim = int(gmean([input_ae_dim, z_dim]))
-        self.encoder = ae.Encoder_old(input_dim=input_ae_dim, hidden_dim=hidden_ae_dim, latent_dim=z_dim)
-        self.decoder = ae.Decoder_old(latent_dim=z_dim      , hidden_dim=hidden_ae_dim, output_dim=n_dim)
 
-    def set_status(self, status, phase):
-        if phase == 'train':
-            self.status_train.append(status)
-        elif phase == 'test':
-            self.status_test.append(status)
-
-    def get_status(self, phase):
-        if phase == 'train':
-            return np.array(self.status_train)
-        elif phase == 'test':
-            return np.array(self.status_test)
-
-
-    def forward(self, n_0, p, tstep):
-        '''
-        Forward function giving the workflow of the MACE architecture.
-        '''
-
-        x_0 = n_0               ## use NN version of G
-        if not self.g_nn:       ## DON'T use NN version of G
-            ## Ravel the abundances n_0 and physical input p to x_0
-            x_0 = torch.cat((p, n_0), axis=-1) # type: ignore
-
-        ## Encode x_0, returning the encoded z_0 in latent space
-        z_0 = self.encoder(x_0)
+# from scipy.stats            import gmean
+
+# class Solver_old(nn.Module):
+#     '''
+#     The Solver class presents the architecture of MACE.
+#     Components:
+#         1) Encoder; neural network with adjustable amount of nodes and layers
+#         2) Neural ODE; ODE given by function g, with trainable elements 
+#         3) Decoder; neural network with adjustable amount of nodes and layers
+
+#     '''
+#     def __init__(self, p_dim, z_dim, DEVICE,  n_dim=466, g_nn = False, atol = 1e-5, rtol = 1e-2):
+#         super(Solver_old, self).__init__() # type: ignore
+
+#         self.status_train = list()
+#         self.status_test = list()
+
+#         self.z_dim = z_dim
+#         self.n_dim = n_dim
+#         self.DEVICE = DEVICE
+#         self.g_nn = g_nn
+
+#         ## Setting the neural ODE
+#         input_ae_dim  = n_dim
+#         if not self.g_nn:
+#             self.g = lODE.G(z_dim)
+#             input_ae_dim  = input_ae_dim+p_dim
+#             self.odeterm = to.ODETerm(self.g, with_args=False)
+#         if self.g_nn:
+#             self.g = lODE.Gnn(p_dim, z_dim)
+#             self.odeterm = to.ODETerm(self.g, with_args=True)
+
+#         self.step_method          = to.Dopri5(term=self.odeterm)
+#         self.step_size_controller = to.IntegralController(atol=atol, rtol=rtol, term=self.odeterm)
+#         self.adjoint              = to.AutoDiffAdjoint(self.step_method, self.step_size_controller).to(self.DEVICE) # type: ignore
+
+#         self.jit_solver = torch.compile(self.adjoint)
+
+#         ## Setting the autoencoder (enocder + decoder)
+#         hidden_ae_dim = int(gmean([input_ae_dim, z_dim]))
+#         self.encoder = ae.Encoder_old(input_dim=input_ae_dim, hidden_dim=hidden_ae_dim, latent_dim=z_dim)
+#         self.decoder = ae.Decoder_old(latent_dim=z_dim      , hidden_dim=hidden_ae_dim, output_dim=n_dim)
+
+#     def set_status(self, status, phase):
+#         if phase == 'train':
+#             self.status_train.append(status)
+#         elif phase == 'test':
+#             self.status_test.append(status)
+
+#     def get_status(self, phase):
+#         if phase == 'train':
+#             return np.array(self.status_train)
+#         elif phase == 'test':
+#             return np.array(self.status_test)
+
+
+#     def forward(self, n_0, p, tstep):
+#         '''
+#         Forward function giving the workflow of the MACE architecture.
+#         '''
+
+#         x_0 = n_0               ## use NN version of G
+#         if not self.g_nn:       ## DON'T use NN version of G
+#             ## Ravel the abundances n_0 and physical input p to x_0
+#             x_0 = torch.cat((p, n_0), axis=-1) # type: ignore
+
+#         ## Encode x_0, returning the encoded z_0 in latent space
+#         z_0 = self.encoder(x_0)
         
-        ## Create initial value problem
-        problem = to.InitialValueProblem(
-            y0     = z_0.to(self.DEVICE),  ## "view" is om met de batches om te gaan
-            t_eval = tstep.view(z_0.shape[0],-1).to(self.DEVICE),
-        )
+#         ## Create initial value problem
+#         problem = to.InitialValueProblem(
+#             y0     = z_0.to(self.DEVICE),  ## "view" is om met de batches om te gaan
+#             t_eval = tstep.view(z_0.shape[0],-1).to(self.DEVICE),
+#         )
 
-        ## Solve initial value problem. Details are set in the __init__() of this class.
-        solution = self.jit_solver.solve(problem, args=p)
-        z_s = solution.ys.view(-1, self.z_dim)  ## want batches 
+#         ## Solve initial value problem. Details are set in the __init__() of this class.
+#         solution = self.jit_solver.solve(problem, args=p)
+#         z_s = solution.ys.view(-1, self.z_dim)  ## want batches 
 
-        ## Decode the resulting values from latent space z_s back to physical space
-        n_s_ravel = self.decoder(z_s)
+#         ## Decode the resulting values from latent space z_s back to physical space
+#         n_s_ravel = self.decoder(z_s)
 
-        ## Reshape correctly
-        n_s = n_s_ravel.reshape(1,tstep.shape[-1], self.n_dim)
+#         ## Reshape correctly
+#         n_s = n_s_ravel.reshape(1,tstep.shape[-1], self.n_dim)
 
-        return n_s, z_s, solution.status
\ No newline at end of file
+#         return n_s, z_s, solution.status