diff --git a/tutorials/tutorial1/tutorial.ipynb b/tutorials/tutorial1/tutorial.ipynb
index ccc82ee..21b4fc2 100644
--- a/tutorials/tutorial1/tutorial.ipynb
+++ b/tutorials/tutorial1/tutorial.ipynb
@@ -137,7 +137,15 @@
    "execution_count": 2,
    "id": "f2608e2e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/matte_b/PINA/pina/operators.py: DeprecationWarning: 'pina.operators' is deprecated and will be removed in future versions. Please use 'pina.operator' instead.\n"
+     ]
+    }
+   ],
    "source": [
     "from pina.problem import SpatialProblem\n",
     "from pina.operator import grad\n",
diff --git a/tutorials/tutorial1/tutorial.py b/tutorials/tutorial1/tutorial.py
index a12dca3..4885d92 100644
--- a/tutorials/tutorial1/tutorial.py
+++ b/tutorials/tutorial1/tutorial.py
@@ -89,7 +89,7 @@ class TimeSpaceODE(SpatialProblem, TimeDependentProblem):
 # 
 # Once the `Problem` class is initialized, we need to represent the differential equation in **PINA**. In order to do this, we need to load the **PINA** operators from `pina.operators` module. Again, we'll consider Equation (1) and represent it in **PINA**:
 
-# In[2]:
+# In[ ]:
 
 
 from pina.problem import SpatialProblem
diff --git a/tutorials/tutorial4/tutorial.ipynb b/tutorials/tutorial4/tutorial.ipynb
index 505c59c..c5b16a3 100644
--- a/tutorials/tutorial4/tutorial.ipynb
+++ b/tutorials/tutorial4/tutorial.ipynb
@@ -869,16 +869,16 @@
     "output = net(input_data).detach()\n",
     "\n",
     "# visualize data\n",
-    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n",
-    "pic1 = axes[0].scatter(grid[:, 0], grid[:, 1], c=input_data[0, 0, :, -1])\n",
-    "axes[0].set_title(\"Real\")\n",
-    "fig.colorbar(pic1)\n",
-    "plt.subplot(1, 2, 2)\n",
-    "pic2 = axes[1].scatter(grid[:, 0], grid[:, 1], c=output[0, 0, :, -1])\n",
-    "axes[1].set_title(\"Autoencoder\")\n",
-    "fig.colorbar(pic2)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
+    "#fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n",
+    "#pic1 = axes[0].scatter(grid[:, 0], grid[:, 1], c=input_data[0, 0, :, -1])\n",
+    "#axes[0].set_title(\"Real\")\n",
+    "#fig.colorbar(pic1)\n",
+    "#plt.subplot(1, 2, 2)\n",
+    "#pic2 = axes[1].scatter(grid[:, 0], grid[:, 1], c=output[0, 0, :, -1])\n",
+    "#axes[1].set_title(\"Autoencoder\")\n",
+    "#fig.colorbar(pic2)\n",
+    "#plt.tight_layout()\n",
+    "#plt.show()\n"
    ]
   },
   {
@@ -963,16 +963,16 @@
     "output = net.decoder(latent, input_data2).detach()\n",
     "\n",
     "# show the picture\n",
-    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n",
-    "pic1 = axes[0].scatter(grid2[:, 0], grid2[:, 1], c=input_data2[0, 0, :, -1])\n",
-    "axes[0].set_title(\"Real\")\n",
-    "fig.colorbar(pic1)\n",
-    "plt.subplot(1, 2, 2)\n",
-    "pic2 = axes[1].scatter(grid2[:, 0], grid2[:, 1], c=output[0, 0, :, -1])\n",
-    "axes[1].set_title(\"Up-sampling\")\n",
-    "fig.colorbar(pic2)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
+    "#fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n",
+    "#pic1 = axes[0].scatter(grid2[:, 0], grid2[:, 1], c=input_data2[0, 0, :, -1])\n",
+    "#axes[0].set_title(\"Real\")\n",
+    "#fig.colorbar(pic1)\n",
+    "#plt.subplot(1, 2, 2)\n",
+    "#pic2 = axes[1].scatter(grid2[:, 0], grid2[:, 1], c=output[0, 0, :, -1])\n",
+    "# axes[1].set_title(\"Up-sampling\")\n",
+    "#fig.colorbar(pic2)\n",
+    "#plt.tight_layout()\n",
+    "#plt.show()\n"
    ]
   },
   {
@@ -1051,16 +1051,16 @@
     "output = net.decoder(latent, input_data2).detach()\n",
     "\n",
     "# show the picture\n",
-    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n",
-    "pic1 = axes[0].scatter(grid2[:, 0], grid2[:, 1], c=input_data2[0, 0, :, -1])\n",
-    "axes[0].set_title(\"Real\")\n",
-    "fig.colorbar(pic1)\n",
-    "plt.subplot(1, 2, 2)\n",
-    "pic2 = axes[1].scatter(grid2[:, 0], grid2[:, 1], c=output[0, 0, :, -1])\n",
-    "axes[1].set_title(\"Autoencoder not re-trained\")\n",
-    "fig.colorbar(pic2)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
+    "#fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n",
+    "#pic1 = axes[0].scatter(grid2[:, 0], grid2[:, 1], c=input_data2[0, 0, :, -1])\n",
+    "#axes[0].set_title(\"Real\")\n",
+    "#fig.colorbar(pic1)\n",
+    "#plt.subplot(1, 2, 2)\n",
+    "#pic2 = axes[1].scatter(grid2[:, 0], grid2[:, 1], c=output[0, 0, :, -1])\n",
+    "#axes[1].set_title(\"Autoencoder not re-trained\")\n",
+    "#fig.colorbar(pic2)\n",
+    "#plt.tight_layout()\n",
+    "#plt.show()\n",
     "\n",
     "# calculate l2 error\n",
     "print(f'l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}')"
diff --git a/tutorials/tutorial4/tutorial.py b/tutorials/tutorial4/tutorial.py
index ee1e7aa..ec191a6 100644
--- a/tutorials/tutorial4/tutorial.py
+++ b/tutorials/tutorial4/tutorial.py
@@ -530,16 +530,16 @@ net.eval()
 output = net(input_data).detach()
 
 # visualize data
-fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))
-pic1 = axes[0].scatter(grid[:, 0], grid[:, 1], c=input_data[0, 0, :, -1])
-axes[0].set_title("Real")
-fig.colorbar(pic1)
-plt.subplot(1, 2, 2)
-pic2 = axes[1].scatter(grid[:, 0], grid[:, 1], c=output[0, 0, :, -1])
-axes[1].set_title("Autoencoder")
-fig.colorbar(pic2)
-plt.tight_layout()
-plt.show()
+#fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))
+#pic1 = axes[0].scatter(grid[:, 0], grid[:, 1], c=input_data[0, 0, :, -1])
+#axes[0].set_title("Real")
+#fig.colorbar(pic1)
+#plt.subplot(1, 2, 2)
+#pic2 = axes[1].scatter(grid[:, 0], grid[:, 1], c=output[0, 0, :, -1])
+#axes[1].set_title("Autoencoder")
+#fig.colorbar(pic2)
+#plt.tight_layout()
+#plt.show()
 
 
 # As we can see, the two solutions are really similar! We can compute the $l_2$ error quite easily as well:
@@ -579,16 +579,16 @@ latent = net.encoder(input_data)
 output = net.decoder(latent, input_data2).detach()
 
 # show the picture
-fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))
-pic1 = axes[0].scatter(grid2[:, 0], grid2[:, 1], c=input_data2[0, 0, :, -1])
-axes[0].set_title("Real")
-fig.colorbar(pic1)
-plt.subplot(1, 2, 2)
-pic2 = axes[1].scatter(grid2[:, 0], grid2[:, 1], c=output[0, 0, :, -1])
-axes[1].set_title("Up-sampling")
-fig.colorbar(pic2)
-plt.tight_layout()
-plt.show()
+#fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))
+#pic1 = axes[0].scatter(grid2[:, 0], grid2[:, 1], c=input_data2[0, 0, :, -1])
+#axes[0].set_title("Real")
+#fig.colorbar(pic1)
+#plt.subplot(1, 2, 2)
+#pic2 = axes[1].scatter(grid2[:, 0], grid2[:, 1], c=output[0, 0, :, -1])
+# axes[1].set_title("Up-sampling")
+#fig.colorbar(pic2)
+#plt.tight_layout()
+#plt.show()
 
 
 # As we can see we have a very good approximation of the original function, even thought some noise is present. Let's calculate the error now:
@@ -621,16 +621,16 @@ latent = net.encoder(input_data2)
 output = net.decoder(latent, input_data2).detach()
 
 # show the picture
-fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))
-pic1 = axes[0].scatter(grid2[:, 0], grid2[:, 1], c=input_data2[0, 0, :, -1])
-axes[0].set_title("Real")
-fig.colorbar(pic1)
-plt.subplot(1, 2, 2)
-pic2 = axes[1].scatter(grid2[:, 0], grid2[:, 1], c=output[0, 0, :, -1])
-axes[1].set_title("Autoencoder not re-trained")
-fig.colorbar(pic2)
-plt.tight_layout()
-plt.show()
+#fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))
+#pic1 = axes[0].scatter(grid2[:, 0], grid2[:, 1], c=input_data2[0, 0, :, -1])
+#axes[0].set_title("Real")
+#fig.colorbar(pic1)
+#plt.subplot(1, 2, 2)
+#pic2 = axes[1].scatter(grid2[:, 0], grid2[:, 1], c=output[0, 0, :, -1])
+#axes[1].set_title("Autoencoder not re-trained")
+#fig.colorbar(pic2)
+#plt.tight_layout()
+#plt.show()
 
 # calculate l2 error
 print(f'l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}')