diff --git a/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip b/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip
index d82406b83..0a1c7b62c 100644
Binary files a/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip and b/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip differ
diff --git a/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip b/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip
index 5788b4cad..cc466e1c2 100644
Binary files a/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip and b/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip differ
diff --git a/_sources/sg_execution_times.rst.txt b/_sources/sg_execution_times.rst.txt
index 8b1cdf166..1ebfef416 100644
--- a/_sources/sg_execution_times.rst.txt
+++ b/_sources/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**02:01.082** total execution time for 10 files **from all galleries**:
+**01:59.897** total execution time for 10 files **from all galleries**:
 
 .. container::
 
@@ -33,22 +33,22 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_tutorials_tensorclass_fashion.py` (``reference/generated/tutorials/tensorclass_fashion.py``)
-     - 00:58.981
+     - 00:58.373
      - 0.0
    * - :ref:`sphx_glr_tutorials_data_fashion.py` (``reference/generated/tutorials/data_fashion.py``)
-     - 00:50.364
+     - 00:49.828
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_module.py` (``reference/generated/tutorials/tensordict_module.py``)
-     - 00:10.144
+     - 00:10.137
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensorclass_imagenet.py` (``reference/generated/tutorials/tensorclass_imagenet.py``)
-     - 00:01.535
+     - 00:01.501
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_memory.py` (``reference/generated/tutorials/tensordict_memory.py``)
-     - 00:00.023
+     - 00:00.025
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_keys.py` (``reference/generated/tutorials/tensordict_keys.py``)
-     - 00:00.010
+     - 00:00.009
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_shapes.py` (``reference/generated/tutorials/tensordict_shapes.py``)
      - 00:00.008
@@ -57,7 +57,7 @@ Computation times
      - 00:00.007
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_module_functional.py` (``reference/generated/tutorials/tensordict_module_functional.py``)
-     - 00:00.007
+     - 00:00.006
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_preallocation.py` (``reference/generated/tutorials/tensordict_preallocation.py``)
      - 00:00.003
diff --git a/_sources/tutorials/data_fashion.rst.txt b/_sources/tutorials/data_fashion.rst.txt
index f53654983..bc3a38488 100644
--- a/_sources/tutorials/data_fashion.rst.txt
+++ b/_sources/tutorials/data_fashion.rst.txt
@@ -423,156 +423,156 @@ adjust how we unpack the data to the more explicit key-based retrieval offered b
         is_shared=False)
     Epoch 1
     -------------------------
-    loss: 2.297689 [    0/60000]
-    loss: 2.280892 [ 6400/60000]
-    loss: 2.274654 [12800/60000]
-    loss: 2.279074 [19200/60000]
-    loss: 2.246684 [25600/60000]
-    loss: 2.236528 [32000/60000]
-    loss: 2.231100 [38400/60000]
-    loss: 2.205158 [44800/60000]
-    loss: 2.201136 [51200/60000]
-    loss: 2.183861 [57600/60000]
+    loss: 2.300480 [    0/60000]
+    loss: 2.293064 [ 6400/60000]
+    loss: 2.266259 [12800/60000]
+    loss: 2.261617 [19200/60000]
+    loss: 2.261147 [25600/60000]
+    loss: 2.215564 [32000/60000]
+    loss: 2.237646 [38400/60000]
+    loss: 2.195081 [44800/60000]
+    loss: 2.190470 [51200/60000]
+    loss: 2.164365 [57600/60000]
     Test Error: 
-     Accuracy: 46.9%, Avg loss: 2.167354 
+     Accuracy: 41.1%, Avg loss: 2.155924 
 
     Epoch 2
     -------------------------
-    loss: 2.171290 [    0/60000]
-    loss: 2.158412 [ 6400/60000]
-    loss: 2.111888 [12800/60000]
-    loss: 2.133768 [19200/60000]
-    loss: 2.084503 [25600/60000]
-    loss: 2.034350 [32000/60000]
-    loss: 2.050703 [38400/60000]
-    loss: 1.979632 [44800/60000]
-    loss: 1.976614 [51200/60000]
-    loss: 1.923289 [57600/60000]
+    loss: 2.162857 [    0/60000]
+    loss: 2.156318 [ 6400/60000]
+    loss: 2.098328 [12800/60000]
+    loss: 2.116032 [19200/60000]
+    loss: 2.078008 [25600/60000]
+    loss: 2.007684 [32000/60000]
+    loss: 2.046304 [38400/60000]
+    loss: 1.959306 [44800/60000]
+    loss: 1.963503 [51200/60000]
+    loss: 1.905034 [57600/60000]
     Test Error: 
-     Accuracy: 60.4%, Avg loss: 1.910134 
+     Accuracy: 56.5%, Avg loss: 1.897783 
 
     Epoch 3
     -------------------------
-    loss: 1.936399 [    0/60000]
-    loss: 1.905847 [ 6400/60000]
-    loss: 1.795367 [12800/60000]
-    loss: 1.838003 [19200/60000]
-    loss: 1.743419 [25600/60000]
-    loss: 1.686000 [32000/60000]
-    loss: 1.699681 [38400/60000]
-    loss: 1.603609 [44800/60000]
-    loss: 1.619376 [51200/60000]
-    loss: 1.525605 [57600/60000]
+    loss: 1.922749 [    0/60000]
+    loss: 1.898134 [ 6400/60000]
+    loss: 1.786096 [12800/60000]
+    loss: 1.830101 [19200/60000]
+    loss: 1.727412 [25600/60000]
+    loss: 1.672580 [32000/60000]
+    loss: 1.700479 [38400/60000]
+    loss: 1.590516 [44800/60000]
+    loss: 1.615487 [51200/60000]
+    loss: 1.517848 [57600/60000]
     Test Error: 
-     Accuracy: 61.5%, Avg loss: 1.536318 
+     Accuracy: 61.8%, Avg loss: 1.532749 
 
     Epoch 4
     -------------------------
-    loss: 1.596518 [    0/60000]
-    loss: 1.559388 [ 6400/60000]
-    loss: 1.415173 [12800/60000]
-    loss: 1.491498 [19200/60000]
-    loss: 1.381014 [25600/60000]
-    loss: 1.365470 [32000/60000]
-    loss: 1.370804 [38400/60000]
-    loss: 1.300947 [44800/60000]
-    loss: 1.330507 [51200/60000]
-    loss: 1.238865 [57600/60000]
+    loss: 1.593874 [    0/60000]
+    loss: 1.560890 [ 6400/60000]
+    loss: 1.416031 [12800/60000]
+    loss: 1.487803 [19200/60000]
+    loss: 1.369440 [25600/60000]
+    loss: 1.358816 [32000/60000]
+    loss: 1.374556 [38400/60000]
+    loss: 1.287937 [44800/60000]
+    loss: 1.326393 [51200/60000]
+    loss: 1.228221 [57600/60000]
     Test Error: 
-     Accuracy: 63.2%, Avg loss: 1.261017 
+     Accuracy: 64.3%, Avg loss: 1.257580 
 
     Epoch 5
     -------------------------
-    loss: 1.331413 [    0/60000]
-    loss: 1.310852 [ 6400/60000]
-    loss: 1.153299 [12800/60000]
-    loss: 1.264552 [19200/60000]
-    loss: 1.141564 [25600/60000]
-    loss: 1.159877 [32000/60000]
-    loss: 1.169980 [38400/60000]
-    loss: 1.117384 [44800/60000]
-    loss: 1.152227 [51200/60000]
-    loss: 1.074354 [57600/60000]
+    loss: 1.331571 [    0/60000]
+    loss: 1.314824 [ 6400/60000]
+    loss: 1.154153 [12800/60000]
+    loss: 1.257705 [19200/60000]
+    loss: 1.134324 [25600/60000]
+    loss: 1.153518 [32000/60000]
+    loss: 1.174747 [38400/60000]
+    loss: 1.101449 [44800/60000]
+    loss: 1.145374 [51200/60000]
+    loss: 1.062342 [57600/60000]
     Test Error: 
-     Accuracy: 64.7%, Avg loss: 1.091176 
+     Accuracy: 65.2%, Avg loss: 1.087329 
 
-    TensorDict training done! time:  8.2466 s
+    TensorDict training done! time:  8.1166 s
     Epoch 1
     -------------------------
-    loss: 2.302497 [    0/60000]
-    loss: 2.293708 [ 6400/60000]
-    loss: 2.275291 [12800/60000]
-    loss: 2.277487 [19200/60000]
-    loss: 2.256965 [25600/60000]
-    loss: 2.226137 [32000/60000]
-    loss: 2.235041 [38400/60000]
-    loss: 2.204915 [44800/60000]
-    loss: 2.205817 [51200/60000]
-    loss: 2.175438 [57600/60000]
+    loss: 2.298001 [    0/60000]
+    loss: 2.287161 [ 6400/60000]
+    loss: 2.270800 [12800/60000]
+    loss: 2.270414 [19200/60000]
+    loss: 2.245303 [25600/60000]
+    loss: 2.225265 [32000/60000]
+    loss: 2.231304 [38400/60000]
+    loss: 2.200896 [44800/60000]
+    loss: 2.198071 [51200/60000]
+    loss: 2.161562 [57600/60000]
     Test Error: 
-     Accuracy: 41.0%, Avg loss: 2.167767 
+     Accuracy: 50.7%, Avg loss: 2.157821 
 
     Epoch 2
     -------------------------
-    loss: 2.173955 [    0/60000]
-    loss: 2.167189 [ 6400/60000]
-    loss: 2.113836 [12800/60000]
-    loss: 2.137049 [19200/60000]
-    loss: 2.091704 [25600/60000]
-    loss: 2.024699 [32000/60000]
-    loss: 2.058104 [38400/60000]
-    loss: 1.987428 [44800/60000]
-    loss: 1.997960 [51200/60000]
-    loss: 1.928781 [57600/60000]
+    loss: 2.168909 [    0/60000]
+    loss: 2.158751 [ 6400/60000]
+    loss: 2.096658 [12800/60000]
+    loss: 2.108140 [19200/60000]
+    loss: 2.061918 [25600/60000]
+    loss: 2.008899 [32000/60000]
+    loss: 2.032122 [38400/60000]
+    loss: 1.955184 [44800/60000]
+    loss: 1.954244 [51200/60000]
+    loss: 1.873682 [57600/60000]
     Test Error: 
-     Accuracy: 57.3%, Avg loss: 1.920751 
+     Accuracy: 58.9%, Avg loss: 1.876599 
 
     Epoch 3
     -------------------------
-    loss: 1.947450 [    0/60000]
-    loss: 1.923763 [ 6400/60000]
-    loss: 1.809623 [12800/60000]
-    loss: 1.859243 [19200/60000]
-    loss: 1.749499 [25600/60000]
-    loss: 1.684024 [32000/60000]
-    loss: 1.717253 [38400/60000]
-    loss: 1.618240 [44800/60000]
-    loss: 1.651242 [51200/60000]
-    loss: 1.544094 [57600/60000]
+    loss: 1.909632 [    0/60000]
+    loss: 1.884026 [ 6400/60000]
+    loss: 1.755143 [12800/60000]
+    loss: 1.790438 [19200/60000]
+    loss: 1.690238 [25600/60000]
+    loss: 1.638952 [32000/60000]
+    loss: 1.660928 [38400/60000]
+    loss: 1.561142 [44800/60000]
+    loss: 1.582468 [51200/60000]
+    loss: 1.474488 [57600/60000]
     Test Error: 
-     Accuracy: 61.4%, Avg loss: 1.550630 
+     Accuracy: 61.6%, Avg loss: 1.500441 
 
     Epoch 4
     -------------------------
-    loss: 1.611791 [    0/60000]
-    loss: 1.576542 [ 6400/60000]
-    loss: 1.422823 [12800/60000]
-    loss: 1.509073 [19200/60000]
-    loss: 1.374687 [25600/60000]
-    loss: 1.361015 [32000/60000]
-    loss: 1.386337 [38400/60000]
-    loss: 1.310513 [44800/60000]
-    loss: 1.355154 [51200/60000]
-    loss: 1.253993 [57600/60000]
+    loss: 1.560883 [    0/60000]
+    loss: 1.537471 [ 6400/60000]
+    loss: 1.383097 [12800/60000]
+    loss: 1.453247 [19200/60000]
+    loss: 1.343728 [25600/60000]
+    loss: 1.335195 [32000/60000]
+    loss: 1.352697 [38400/60000]
+    loss: 1.274515 [44800/60000]
+    loss: 1.309380 [51200/60000]
+    loss: 1.211425 [57600/60000]
     Test Error: 
-     Accuracy: 63.9%, Avg loss: 1.267246 
+     Accuracy: 63.2%, Avg loss: 1.241849 
 
     Epoch 5
     -------------------------
-    loss: 1.339752 [    0/60000]
-    loss: 1.319095 [ 6400/60000]
-    loss: 1.151958 [12800/60000]
-    loss: 1.273998 [19200/60000]
-    loss: 1.129366 [25600/60000]
-    loss: 1.151123 [32000/60000]
-    loss: 1.181103 [38400/60000]
-    loss: 1.123158 [44800/60000]
-    loss: 1.170845 [51200/60000]
-    loss: 1.086041 [57600/60000]
+    loss: 1.310621 [    0/60000]
+    loss: 1.302632 [ 6400/60000]
+    loss: 1.136563 [12800/60000]
+    loss: 1.238647 [19200/60000]
+    loss: 1.124559 [25600/60000]
+    loss: 1.142481 [32000/60000]
+    loss: 1.167050 [38400/60000]
+    loss: 1.099875 [44800/60000]
+    loss: 1.140567 [51200/60000]
+    loss: 1.059313 [57600/60000]
     Test Error: 
-     Accuracy: 65.0%, Avg loss: 1.093209 
+     Accuracy: 64.6%, Avg loss: 1.082458 
 
-    Training done! time:  32.7524 s
+    Training done! time:  32.8354 s
 
 
 
@@ -580,7 +580,7 @@ adjust how we unpack the data to the more explicit key-based retrieval offered b
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 50.364 seconds)
+   **Total running time of the script:** (0 minutes 49.828 seconds)
 
 
 .. _sphx_glr_download_tutorials_data_fashion.py:
diff --git a/_sources/tutorials/sg_execution_times.rst.txt b/_sources/tutorials/sg_execution_times.rst.txt
index cb0a8771e..f63d3e7db 100644
--- a/_sources/tutorials/sg_execution_times.rst.txt
+++ b/_sources/tutorials/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**02:01.082** total execution time for 10 files **from tutorials**:
+**01:59.897** total execution time for 10 files **from tutorials**:
 
 .. container::
 
@@ -33,22 +33,22 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_tutorials_tensorclass_fashion.py` (``tensorclass_fashion.py``)
-     - 00:58.981
+     - 00:58.373
      - 0.0
    * - :ref:`sphx_glr_tutorials_data_fashion.py` (``data_fashion.py``)
-     - 00:50.364
+     - 00:49.828
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_module.py` (``tensordict_module.py``)
-     - 00:10.144
+     - 00:10.137
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensorclass_imagenet.py` (``tensorclass_imagenet.py``)
-     - 00:01.535
+     - 00:01.501
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_memory.py` (``tensordict_memory.py``)
-     - 00:00.023
+     - 00:00.025
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_keys.py` (``tensordict_keys.py``)
-     - 00:00.010
+     - 00:00.009
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_shapes.py` (``tensordict_shapes.py``)
      - 00:00.008
@@ -57,7 +57,7 @@ Computation times
      - 00:00.007
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_module_functional.py` (``tensordict_module_functional.py``)
-     - 00:00.007
+     - 00:00.006
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_preallocation.py` (``tensordict_preallocation.py``)
      - 00:00.003
diff --git a/_sources/tutorials/tensorclass_fashion.rst.txt b/_sources/tutorials/tensorclass_fashion.rst.txt
index 340ff1979..f6f03dde4 100644
--- a/_sources/tutorials/tensorclass_fashion.rst.txt
+++ b/_sources/tutorials/tensorclass_fashion.rst.txt
@@ -97,22 +97,22 @@ the image (e.g. "Bag", "Sneaker" etc.).
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz
-
      0%|          | 0/26421880 [00:00<?, ?it/s]
      0%|          | 65536/26421880 [00:00<01:11, 366529.78it/s]
      1%|          | 229376/26421880 [00:00<00:38, 688397.37it/s]
      4%|▎         | 950272/26421880 [00:00<00:11, 2208013.10it/s]
     14%|█▍        | 3768320/26421880 [00:00<00:02, 8248474.55it/s]
     26%|██▌       | 6881280/26421880 [00:00<00:01, 13109654.28it/s]
     49%|████▉     | 12976128/26421880 [00:00<00:00, 23117960.87it/s]
     61%|██████▏   | 16187392/26421880 [00:01<00:00, 23413161.19it/s]
     84%|████████▎ | 22118400/26421880 [00:01<00:00, 29644180.74it/s]
     96%|█████████▌| 25362432/26421880 [00:01<00:00, 28040475.24it/s]
    100%|██████████| 26421880/26421880 [00:01<00:00, 19497898.83it/s]
+
      0%|          | 0/26421880 [00:00<?, ?it/s]
      0%|          | 65536/26421880 [00:00<01:12, 361577.27it/s]
      1%|          | 229376/26421880 [00:00<00:38, 681040.77it/s]
      3%|▎         | 917504/26421880 [00:00<00:09, 2641761.98it/s]
      7%|▋         | 1933312/26421880 [00:00<00:06, 4071838.92it/s]
     26%|██▌       | 6750208/26421880 [00:00<00:01, 15809304.40it/s]
     38%|███▊      | 10092544/26421880 [00:00<00:00, 17248107.43it/s]
     58%|█████▊    | 15400960/26421880 [00:01<00:00, 26081929.66it/s]
     73%|███████▎  | 19169280/26421880 [00:01<00:00, 24513394.41it/s]
     92%|█████████▏| 24412160/26421880 [00:01<00:00, 31119845.37it/s]
    100%|██████████| 26421880/26421880 [00:01<00:00, 19357445.87it/s]
     Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
-
      0%|          | 0/29515 [00:00<?, ?it/s]
    100%|██████████| 29515/29515 [00:00<00:00, 328895.32it/s]
+
      0%|          | 0/29515 [00:00<?, ?it/s]
    100%|██████████| 29515/29515 [00:00<00:00, 326262.83it/s]
     Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
-
      0%|          | 0/4422102 [00:00<?, ?it/s]
      1%|▏         | 65536/4422102 [00:00<00:11, 364322.80it/s]
      5%|▌         | 229376/4422102 [00:00<00:06, 685186.31it/s]
     19%|█▊        | 819200/4422102 [00:00<00:01, 2334471.80it/s]
     40%|████      | 1769472/4422102 [00:00<00:00, 3764378.13it/s]
    100%|██████████| 4422102/4422102 [00:00<00:00, 6119438.97it/s]
+
      0%|          | 0/4422102 [00:00<?, ?it/s]
      1%|▏         | 65536/4422102 [00:00<00:12, 362287.33it/s]
      5%|▌         | 229376/4422102 [00:00<00:06, 682024.54it/s]
     21%|██        | 917504/4422102 [00:00<00:01, 2104319.86it/s]
     70%|██████▉   | 3080192/4422102 [00:00<00:00, 5985861.78it/s]
    100%|██████████| 4422102/4422102 [00:00<00:00, 6084624.81it/s]
     Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
-
      0%|          | 0/5148 [00:00<?, ?it/s]
    100%|██████████| 5148/5148 [00:00<00:00, 72701269.33it/s]
+
      0%|          | 0/5148 [00:00<?, ?it/s]
    100%|██████████| 5148/5148 [00:00<00:00, 46837911.05it/s]
     Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw
 
 
@@ -451,156 +451,156 @@ tensor.
         is_shared=False)
     Epoch 1
     -------------------------
-    loss: 2.310976 [    0/60000]
-    loss: 2.295230 [ 6400/60000]
-    loss: 2.283326 [12800/60000]
-    loss: 2.278642 [19200/60000]
-    loss: 2.255610 [25600/60000]
-    loss: 2.232225 [32000/60000]
-    loss: 2.231061 [38400/60000]
-    loss: 2.200477 [44800/60000]
-    loss: 2.201424 [51200/60000]
-    loss: 2.166960 [57600/60000]
+    loss: 2.302387 [    0/60000]
+    loss: 2.293101 [ 6400/60000]
+    loss: 2.279598 [12800/60000]
+    loss: 2.276973 [19200/60000]
+    loss: 2.251420 [25600/60000]
+    loss: 2.238299 [32000/60000]
+    loss: 2.237562 [38400/60000]
+    loss: 2.215429 [44800/60000]
+    loss: 2.208091 [51200/60000]
+    loss: 2.175614 [57600/60000]
     Test Error: 
-     Accuracy: 40.3%, Avg loss: 2.167791 
+     Accuracy: 48.3%, Avg loss: 2.176059 
 
     Epoch 2
     -------------------------
-    loss: 2.177774 [    0/60000]
-    loss: 2.166296 [ 6400/60000]
-    loss: 2.122235 [12800/60000]
-    loss: 2.137978 [19200/60000]
-    loss: 2.079019 [25600/60000]
-    loss: 2.028171 [32000/60000]
-    loss: 2.046031 [38400/60000]
-    loss: 1.973215 [44800/60000]
-    loss: 1.979031 [51200/60000]
-    loss: 1.902367 [57600/60000]
+    loss: 2.183834 [    0/60000]
+    loss: 2.175081 [ 6400/60000]
+    loss: 2.129839 [12800/60000]
+    loss: 2.143353 [19200/60000]
+    loss: 2.089312 [25600/60000]
+    loss: 2.056283 [32000/60000]
+    loss: 2.060929 [38400/60000]
+    loss: 2.006752 [44800/60000]
+    loss: 2.002385 [51200/60000]
+    loss: 1.926731 [57600/60000]
     Test Error: 
-     Accuracy: 48.2%, Avg loss: 1.911800 
+     Accuracy: 61.7%, Avg loss: 1.935310 
 
     Epoch 3
     -------------------------
-    loss: 1.940137 [    0/60000]
-    loss: 1.909616 [ 6400/60000]
-    loss: 1.814698 [12800/60000]
-    loss: 1.854172 [19200/60000]
-    loss: 1.726293 [25600/60000]
-    loss: 1.688812 [32000/60000]
-    loss: 1.703465 [38400/60000]
-    loss: 1.610862 [44800/60000]
-    loss: 1.633218 [51200/60000]
-    loss: 1.529064 [57600/60000]
+    loss: 1.960144 [    0/60000]
+    loss: 1.936480 [ 6400/60000]
+    loss: 1.833049 [12800/60000]
+    loss: 1.864682 [19200/60000]
+    loss: 1.759800 [25600/60000]
+    loss: 1.725796 [32000/60000]
+    loss: 1.714761 [38400/60000]
+    loss: 1.638511 [44800/60000]
+    loss: 1.644561 [51200/60000]
+    loss: 1.533933 [57600/60000]
     Test Error: 
-     Accuracy: 59.2%, Avg loss: 1.552986 
+     Accuracy: 61.5%, Avg loss: 1.563183 
 
     Epoch 4
     -------------------------
-    loss: 1.612865 [    0/60000]
-    loss: 1.574731 [ 6400/60000]
-    loss: 1.449299 [12800/60000]
-    loss: 1.519882 [19200/60000]
-    loss: 1.382809 [25600/60000]
-    loss: 1.389852 [32000/60000]
-    loss: 1.397839 [38400/60000]
-    loss: 1.323305 [44800/60000]
-    loss: 1.355896 [51200/60000]
-    loss: 1.258844 [57600/60000]
+    loss: 1.621519 [    0/60000]
+    loss: 1.590653 [ 6400/60000]
+    loss: 1.447826 [12800/60000]
+    loss: 1.512125 [19200/60000]
+    loss: 1.394294 [25600/60000]
+    loss: 1.402328 [32000/60000]
+    loss: 1.389260 [38400/60000]
+    loss: 1.328610 [44800/60000]
+    loss: 1.353315 [51200/60000]
+    loss: 1.252565 [57600/60000]
     Test Error: 
-     Accuracy: 63.2%, Avg loss: 1.284397 
+     Accuracy: 62.3%, Avg loss: 1.282288 
 
     Epoch 5
     -------------------------
-    loss: 1.358123 [    0/60000]
-    loss: 1.332349 [ 6400/60000]
-    loss: 1.188338 [12800/60000]
-    loss: 1.291905 [19200/60000]
-    loss: 1.153745 [25600/60000]
-    loss: 1.188745 [32000/60000]
-    loss: 1.200132 [38400/60000]
-    loss: 1.134685 [44800/60000]
-    loss: 1.174266 [51200/60000]
-    loss: 1.089714 [57600/60000]
+    loss: 1.356653 [    0/60000]
+    loss: 1.337689 [ 6400/60000]
+    loss: 1.176921 [12800/60000]
+    loss: 1.280601 [19200/60000]
+    loss: 1.154103 [25600/60000]
+    loss: 1.194018 [32000/60000]
+    loss: 1.192287 [38400/60000]
+    loss: 1.138430 [44800/60000]
+    loss: 1.173223 [51200/60000]
+    loss: 1.091239 [57600/60000]
     Test Error: 
-     Accuracy: 64.8%, Avg loss: 1.110009 
+     Accuracy: 64.0%, Avg loss: 1.110041 
 
-    Tensorclass training done! time:  8.5711 s
+    Tensorclass training done! time:  8.3111 s
     Epoch 1
     -------------------------
-    loss: 2.303066 [    0/60000]
-    loss: 2.290508 [ 6400/60000]
-    loss: 2.268382 [12800/60000]
-    loss: 2.267677 [19200/60000]
-    loss: 2.243165 [25600/60000]
-    loss: 2.219261 [32000/60000]
-    loss: 2.226329 [38400/60000]
-    loss: 2.190671 [44800/60000]
-    loss: 2.192858 [51200/60000]
-    loss: 2.160490 [57600/60000]
+    loss: 2.297948 [    0/60000]
+    loss: 2.282624 [ 6400/60000]
+    loss: 2.271977 [12800/60000]
+    loss: 2.270170 [19200/60000]
+    loss: 2.244367 [25600/60000]
+    loss: 2.214798 [32000/60000]
+    loss: 2.220860 [38400/60000]
+    loss: 2.188947 [44800/60000]
+    loss: 2.186659 [51200/60000]
+    loss: 2.154645 [57600/60000]
     Test Error: 
-     Accuracy: 47.0%, Avg loss: 2.155192 
+     Accuracy: 53.5%, Avg loss: 2.150539 
 
     Epoch 2
     -------------------------
-    loss: 2.164057 [    0/60000]
-    loss: 2.159464 [ 6400/60000]
-    loss: 2.098034 [12800/60000]
-    loss: 2.120966 [19200/60000]
-    loss: 2.061083 [25600/60000]
-    loss: 2.001219 [32000/60000]
-    loss: 2.026425 [38400/60000]
-    loss: 1.953357 [44800/60000]
-    loss: 1.962681 [51200/60000]
-    loss: 1.875333 [57600/60000]
+    loss: 2.160149 [    0/60000]
+    loss: 2.149896 [ 6400/60000]
+    loss: 2.097871 [12800/60000]
+    loss: 2.114169 [19200/60000]
+    loss: 2.062091 [25600/60000]
+    loss: 1.994953 [32000/60000]
+    loss: 2.024422 [38400/60000]
+    loss: 1.950521 [44800/60000]
+    loss: 1.953714 [51200/60000]
+    loss: 1.877175 [57600/60000]
     Test Error: 
-     Accuracy: 58.2%, Avg loss: 1.887924 
+     Accuracy: 61.8%, Avg loss: 1.881925 
 
     Epoch 3
     -------------------------
-    loss: 1.922889 [    0/60000]
-    loss: 1.895636 [ 6400/60000]
-    loss: 1.781903 [12800/60000]
-    loss: 1.824525 [19200/60000]
-    loss: 1.701341 [25600/60000]
-    loss: 1.654470 [32000/60000]
-    loss: 1.671035 [38400/60000]
-    loss: 1.587242 [44800/60000]
-    loss: 1.609092 [51200/60000]
-    loss: 1.483521 [57600/60000]
+    loss: 1.919103 [    0/60000]
+    loss: 1.886964 [ 6400/60000]
+    loss: 1.774065 [12800/60000]
+    loss: 1.809855 [19200/60000]
+    loss: 1.709801 [25600/60000]
+    loss: 1.652143 [32000/60000]
+    loss: 1.673265 [38400/60000]
+    loss: 1.587407 [44800/60000]
+    loss: 1.611894 [51200/60000]
+    loss: 1.495472 [57600/60000]
     Test Error: 
-     Accuracy: 61.2%, Avg loss: 1.520375 
+     Accuracy: 62.1%, Avg loss: 1.522582 
 
     Epoch 4
     -------------------------
-    loss: 1.591304 [    0/60000]
-    loss: 1.553071 [ 6400/60000]
-    loss: 1.408833 [12800/60000]
-    loss: 1.483236 [19200/60000]
-    loss: 1.347654 [25600/60000]
-    loss: 1.352496 [32000/60000]
-    loss: 1.357938 [38400/60000]
-    loss: 1.298349 [44800/60000]
-    loss: 1.325898 [51200/60000]
-    loss: 1.211882 [57600/60000]
+    loss: 1.595548 [    0/60000]
+    loss: 1.556155 [ 6400/60000]
+    loss: 1.412991 [12800/60000]
+    loss: 1.479574 [19200/60000]
+    loss: 1.363339 [25600/60000]
+    loss: 1.351150 [32000/60000]
+    loss: 1.366776 [38400/60000]
+    loss: 1.303567 [44800/60000]
+    loss: 1.337294 [51200/60000]
+    loss: 1.226468 [57600/60000]
     Test Error: 
-     Accuracy: 63.6%, Avg loss: 1.250856 
+     Accuracy: 63.7%, Avg loss: 1.258048 
 
     Epoch 5
     -------------------------
-    loss: 1.331446 [    0/60000]
-    loss: 1.307861 [ 6400/60000]
-    loss: 1.147009 [12800/60000]
-    loss: 1.259104 [19200/60000]
-    loss: 1.119098 [25600/60000]
-    loss: 1.154116 [32000/60000]
-    loss: 1.165835 [38400/60000]
-    loss: 1.116898 [44800/60000]
-    loss: 1.149531 [51200/60000]
-    loss: 1.052141 [57600/60000]
+    loss: 1.340565 [    0/60000]
+    loss: 1.316801 [ 6400/60000]
+    loss: 1.157856 [12800/60000]
+    loss: 1.260136 [19200/60000]
+    loss: 1.127928 [25600/60000]
+    loss: 1.149427 [32000/60000]
+    loss: 1.173949 [38400/60000]
+    loss: 1.122528 [44800/60000]
+    loss: 1.160417 [51200/60000]
+    loss: 1.064355 [57600/60000]
     Test Error: 
-     Accuracy: 64.8%, Avg loss: 1.085073 
+     Accuracy: 64.7%, Avg loss: 1.089903 
 
-    Training done! time:  33.1519 s
+    Training done! time:  32.9774 s
 
 
 
@@ -608,7 +608,7 @@ tensor.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 58.981 seconds)
+   **Total running time of the script:** (0 minutes 58.373 seconds)
 
 
 .. _sphx_glr_download_tutorials_tensorclass_fashion.py:
diff --git a/_sources/tutorials/tensorclass_imagenet.rst.txt b/_sources/tutorials/tensorclass_imagenet.rst.txt
index be0faa2fd..e1a2e1b7d 100644
--- a/_sources/tutorials/tensorclass_imagenet.rst.txt
+++ b/_sources/tutorials/tensorclass_imagenet.rst.txt
@@ -419,7 +419,7 @@ though we also give results of our experiments running the same code on ImageNet
  .. code-block:: none
 
     Using device: cpu
-
      0%|          | 0/244 [00:00<?, ?it/s]
     26%|██▌       | 64/244 [00:00<00:00, 233.18it/s]ImageNetData(
+
      0%|          | 0/244 [00:00<?, ?it/s]
     26%|██▌       | 64/244 [00:00<00:00, 227.26it/s]ImageNetData(
         images=MemoryMappedTensor(shape=torch.Size([244, 3, 256, 256]), device=cpu, dtype=torch.uint8, is_shared=False),
         targets=MemoryMappedTensor(shape=torch.Size([244]), device=cpu, dtype=torch.int64, is_shared=False),
         batch_size=torch.Size([244]),
@@ -467,8 +467,8 @@ though we also give results of our experiments running the same code on ImageNet
         batch_size=torch.Size([52]),
         device=None,
         is_shared=False)
-
    100%|██████████| 244/244 [00:00<00:00, 756.46it/s]
-
      0%|          | 0/153 [00:00<?, ?it/s]
     42%|████▏     | 64/153 [00:00<00:00, 191.86it/s]ImageNetData(
+
    100%|██████████| 244/244 [00:00<00:00, 771.51it/s]
+
      0%|          | 0/153 [00:00<?, ?it/s]
     42%|████▏     | 64/153 [00:00<00:00, 204.96it/s]ImageNetData(
         images=MemoryMappedTensor(shape=torch.Size([153, 3, 224, 224]), device=cpu, dtype=torch.uint8, is_shared=False),
         targets=MemoryMappedTensor(shape=torch.Size([153]), device=cpu, dtype=torch.int64, is_shared=False),
         batch_size=torch.Size([153]),
@@ -504,11 +504,11 @@ though we also give results of our experiments running the same code on ImageNet
         batch_size=torch.Size([25]),
         device=None,
         is_shared=False)
-
    100%|██████████| 153/153 [00:00<00:00, 372.18it/s]
-    One iteration over dataloader done! Rate: 1077.1951 fps, time:  0.2042s
-    One iteration over tensorclass dataloader done! Rate: 1733.7566 fps, time:  0.1269s
-    One iteration over val data done! Rate: 695.0831 fps, time:  0.1856s
-    One iteration over tensorclass val data done! Rate: 18441.2139 fps, time:  0.0070s
+
    100%|██████████| 153/153 [00:00<00:00, 377.96it/s]
+    One iteration over dataloader done! Rate: 1022.5849 fps, time:  0.2151s
+    One iteration over tensorclass dataloader done! Rate: 1766.7430 fps, time:  0.1245s
+    One iteration over val data done! Rate: 837.4922 fps, time:  0.1540s
+    One iteration over tensorclass val data done! Rate: 16062.4971 fps, time:  0.0080s
 
 
 
@@ -516,7 +516,7 @@ though we also give results of our experiments running the same code on ImageNet
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.535 seconds)
+   **Total running time of the script:** (0 minutes 1.501 seconds)
 
 
 .. _sphx_glr_download_tutorials_tensorclass_imagenet.py:
diff --git a/_sources/tutorials/tensordict_keys.rst.txt b/_sources/tutorials/tensordict_keys.rst.txt
index 9b6fed0f3..9f7324611 100644
--- a/_sources/tutorials/tensordict_keys.rst.txt
+++ b/_sources/tutorials/tensordict_keys.rst.txt
@@ -769,7 +769,7 @@ We can obtain a new :class:`~.TensorDict` with a subset of the keys by using
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.010 seconds)
+   **Total running time of the script:** (0 minutes 0.009 seconds)
 
 
 .. _sphx_glr_download_tutorials_tensordict_keys.py:
diff --git a/_sources/tutorials/tensordict_memory.rst.txt b/_sources/tutorials/tensordict_memory.rst.txt
index ea19611b5..ccc99a5ab 100644
--- a/_sources/tutorials/tensordict_memory.rst.txt
+++ b/_sources/tutorials/tensordict_memory.rst.txt
@@ -222,7 +222,7 @@ the above :class:`TensorDict` is saved as follows:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.023 seconds)
+   **Total running time of the script:** (0 minutes 0.025 seconds)
 
 
 .. _sphx_glr_download_tutorials_tensordict_memory.py:
diff --git a/_sources/tutorials/tensordict_module.rst.txt b/_sources/tutorials/tensordict_module.rst.txt
index 2a0c78c6a..426f5c775 100644
--- a/_sources/tutorials/tensordict_module.rst.txt
+++ b/_sources/tutorials/tensordict_module.rst.txt
@@ -1043,7 +1043,7 @@ Benchmarking
 
  .. code-block:: none
 
-    Execution time: 0.011106491088867188 seconds
+    Execution time: 0.01023244857788086 seconds
 
 
 
@@ -1066,7 +1066,7 @@ Benchmarking
 
  .. code-block:: none
 
-    Execution time: 0.006401538848876953 seconds
+    Execution time: 0.0059583187103271484 seconds
 
 
 
@@ -1081,7 +1081,7 @@ Have fun with TensorDictModule!
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 10.144 seconds)
+   **Total running time of the script:** (0 minutes 10.137 seconds)
 
 
 .. _sphx_glr_download_tutorials_tensordict_module.py:
diff --git a/_sources/tutorials/tensordict_module_functional.rst.txt b/_sources/tutorials/tensordict_module_functional.rst.txt
index 5cf811875..54c349b17 100644
--- a/_sources/tutorials/tensordict_module_functional.rst.txt
+++ b/_sources/tutorials/tensordict_module_functional.rst.txt
@@ -220,7 +220,7 @@ parameters as regular inputs:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.007 seconds)
+   **Total running time of the script:** (0 minutes 0.006 seconds)
 
 
 .. _sphx_glr_download_tutorials_tensordict_module_functional.py:
diff --git a/_sources/tutorials/tensordict_shapes.rst.txt b/_sources/tutorials/tensordict_shapes.rst.txt
index ff77b57f9..ba94a7c03 100644
--- a/_sources/tutorials/tensordict_shapes.rst.txt
+++ b/_sources/tutorials/tensordict_shapes.rst.txt
@@ -521,19 +521,19 @@ like :func:`torch.gather`.
  .. code-block:: none
 
     index:
-     tensor([[1, 3, 1, 0],
-            [1, 2, 3, 1],
-            [1, 1, 0, 1]])
+     tensor([[0, 3, 2, 1],
+            [2, 2, 3, 2],
+            [2, 0, 2, 0]])
 
     tensordict['a']:
-     tensor([[0.6435, 0.8054, 0.9983, 0.4238],
-            [0.9842, 0.4340, 0.8523, 0.5265],
-            [0.1708, 0.0143, 0.2981, 0.9721]])
+     tensor([[0.4003, 0.3863, 0.8916, 0.4065],
+            [0.2656, 0.6441, 0.3445, 0.6833],
+            [0.6153, 0.8126, 0.9607, 0.1182]])
 
     gathered_tensordict['a']:
-     tensor([[0.8054, 0.4238, 0.8054, 0.6435],
-            [0.4340, 0.8523, 0.5265, 0.4340],
-            [0.0143, 0.0143, 0.1708, 0.0143]])
+     tensor([[0.4003, 0.4065, 0.8916, 0.3863],
+            [0.3445, 0.3445, 0.6833, 0.3445],
+            [0.9607, 0.6153, 0.9607, 0.6153]])
 
 
 
diff --git a/reference/generated/tensordict.nn.ProbabilisticTensorDictModule.html b/reference/generated/tensordict.nn.ProbabilisticTensorDictModule.html
index 0d3b4018f..ddf9a86e4 100644
--- a/reference/generated/tensordict.nn.ProbabilisticTensorDictModule.html
+++ b/reference/generated/tensordict.nn.ProbabilisticTensorDictModule.html
@@ -471,8 +471,9 @@ <h1>ProbabilisticTensorDictModule<a class="headerlink" href="#probabilistictenso
 <span class="gp">&gt;&gt;&gt; </span><span class="n">td_module</span> <span class="o">=</span> <span class="n">ProbabilisticTensorDictSequential</span><span class="p">(</span>
 <span class="gp">... </span>    <span class="n">module</span><span class="p">,</span> <span class="n">normal_params</span><span class="p">,</span> <span class="n">prob_module</span>
 <span class="gp">... </span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">params</span> <span class="o">=</span> <span class="n">make_functional</span><span class="p">(</span><span class="n">td_module</span><span class="p">,</span> <span class="n">funs_to_decorate</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;forward&quot;</span><span class="p">,</span> <span class="s2">&quot;get_dist&quot;</span><span class="p">,</span> <span class="s2">&quot;log_prob&quot;</span><span class="p">])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">_</span> <span class="o">=</span> <span class="n">td_module</span><span class="p">(</span><span class="n">td</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">params</span> <span class="o">=</span> <span class="n">TensorDict</span><span class="o">.</span><span class="n">from_module</span><span class="p">(</span><span class="n">td_module</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">with</span> <span class="n">params</span><span class="o">.</span><span class="n">to_module</span><span class="p">(</span><span class="n">td_module</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="n">_</span> <span class="o">=</span> <span class="n">td_module</span><span class="p">(</span><span class="n">td</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">td</span><span class="p">)</span>
 <span class="go">TensorDict(</span>
 <span class="go">    fields={</span>
@@ -486,13 +487,17 @@ <h1>ProbabilisticTensorDictModule<a class="headerlink" href="#probabilistictenso
 <span class="go">    batch_size=torch.Size([3]),</span>
 <span class="go">    device=None,</span>
 <span class="go">    is_shared=False)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">dist</span> <span class="o">=</span> <span class="n">td_module</span><span class="o">.</span><span class="n">get_dist</span><span class="p">(</span><span class="n">td</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">with</span> <span class="n">params</span><span class="o">.</span><span class="n">to_module</span><span class="p">(</span><span class="n">td_module</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="n">dist</span> <span class="o">=</span> <span class="n">td_module</span><span class="o">.</span><span class="n">get_dist</span><span class="p">(</span><span class="n">td</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
 <span class="go">Normal(loc: torch.Size([3, 4]), scale: torch.Size([3, 4]))</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># we can also apply the module to the TensorDict with vmap</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">vmap</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">params</span> <span class="o">=</span> <span class="n">params</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">td_vmap</span> <span class="o">=</span> <span class="n">vmap</span><span class="p">(</span><span class="n">td_module</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="mi">0</span><span class="p">))(</span><span class="n">td</span><span class="p">,</span> <span class="n">params</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">td</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="k">with</span> <span class="n">params</span><span class="o">.</span><span class="n">to_module</span><span class="p">(</span><span class="n">td_module</span><span class="p">):</span>
+<span class="gp">... </span>        <span class="k">return</span> <span class="n">td_module</span><span class="p">(</span><span class="n">td</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">td_vmap</span> <span class="o">=</span> <span class="n">vmap</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="mi">0</span><span class="p">))(</span><span class="n">td</span><span class="p">,</span> <span class="n">params</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">td_vmap</span><span class="p">)</span>
 <span class="go">TensorDict(</span>
 <span class="go">    fields={</span>
diff --git a/reference/generated/tensordict.nn.TensorDictModule.html b/reference/generated/tensordict.nn.TensorDictModule.html
index d4e988a46..37b0179ab 100644
--- a/reference/generated/tensordict.nn.TensorDictModule.html
+++ b/reference/generated/tensordict.nn.TensorDictModule.html
@@ -371,9 +371,6 @@ <h1>TensorDictModule<a class="headerlink" href="#tensordictmodule" title="Permal
 <dt class="sig sig-object py" id="tensordict.nn.TensorDictModule">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tensordict.nn.</span></span><span class="sig-name descname"><span class="pre">TensorDictModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#tensordict.nn.TensorDictModule" title="Permalink to this definition">¶</a></dt>
 <dd><p>A TensorDictModule, is a python wrapper around a <code class="xref py py-obj docutils literal notranslate"><span class="pre">nn.Module</span></code> that reads and writes to a TensorDict.</p>
-<p>By default, <a class="reference internal" href="#tensordict.nn.TensorDictModule" title="tensordict.nn.TensorDictModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">TensorDictModule</span></code></a> subclasses are always functional,
-meaning that they support the <code class="docutils literal notranslate"><span class="pre">td_module(input,</span> <span class="pre">params=params)</span></code> function
-call signature.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -474,14 +471,15 @@ <h1>TensorDictModule<a class="headerlink" href="#tensordictmodule" title="Permal
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">torch</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">tensordict</span> <span class="kn">import</span> <span class="n">TensorDict</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">tensordict.nn</span> <span class="kn">import</span> <span class="n">TensorDictModule</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">tensordict.nn.functional_modules</span> <span class="kn">import</span> <span class="n">make_functional</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">td</span> <span class="o">=</span> <span class="n">TensorDict</span><span class="p">({</span><span class="s2">&quot;input&quot;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="s2">&quot;hidden&quot;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">8</span><span class="p">)},</span> <span class="p">[</span><span class="mi">3</span><span class="p">,])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">GRUCell</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">td_module</span> <span class="o">=</span> <span class="n">TensorDictModule</span><span class="p">(</span>
 <span class="gp">... </span>   <span class="n">module</span><span class="o">=</span><span class="n">module</span><span class="p">,</span> <span class="n">in_keys</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;input&quot;</span><span class="p">,</span> <span class="s2">&quot;hidden&quot;</span><span class="p">],</span> <span class="n">out_keys</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;output&quot;</span><span class="p">]</span>
 <span class="gp">... </span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">params</span> <span class="o">=</span> <span class="n">make_functional</span><span class="p">(</span><span class="n">td_module</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">td_functional</span> <span class="o">=</span> <span class="n">td_module</span><span class="p">(</span><span class="n">td</span><span class="o">.</span><span class="n">clone</span><span class="p">(),</span> <span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">params</span> <span class="o">=</span> <span class="n">TensorDict</span><span class="o">.</span><span class="n">from_module</span><span class="p">(</span><span class="n">td_module</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="c1"># functional API</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">with</span> <span class="n">params</span><span class="o">.</span><span class="n">to_module</span><span class="p">(</span><span class="n">td_module</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="n">td_functional</span> <span class="o">=</span> <span class="n">td_module</span><span class="p">(</span><span class="n">td</span><span class="o">.</span><span class="n">clone</span><span class="p">())</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">td_functional</span><span class="p">)</span>
 <span class="go">TensorDict(</span>
 <span class="go">    fields={</span>
@@ -533,7 +531,10 @@ <h1>TensorDictModule<a class="headerlink" href="#tensordictmodule" title="Permal
 <span class="go">    batch_size=torch.Size([4]),</span>
 <span class="go">    device=None,</span>
 <span class="go">    is_shared=False)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">td_vmap</span> <span class="o">=</span> <span class="n">vmap</span><span class="p">(</span><span class="n">td_module</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="mi">0</span><span class="p">))(</span><span class="n">td</span><span class="o">.</span><span class="n">clone</span><span class="p">(),</span> <span class="n">params_repeat</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">td</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="k">with</span> <span class="n">params</span><span class="o">.</span><span class="n">to_module</span><span class="p">(</span><span class="n">td_module</span><span class="p">):</span>
+<span class="gp">... </span>        <span class="k">return</span> <span class="n">td_module</span><span class="p">(</span><span class="n">td</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">td_vmap</span> <span class="o">=</span> <span class="n">vmap</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="mi">0</span><span class="p">))(</span><span class="n">td</span><span class="o">.</span><span class="n">clone</span><span class="p">(),</span> <span class="n">params_repeat</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">td_vmap</span><span class="p">)</span>
 <span class="go">TensorDict(</span>
 <span class="go">    fields={</span>
diff --git a/reference/generated/tensordict.nn.TensorDictSequential.html b/reference/generated/tensordict.nn.TensorDictSequential.html
index 9d2e6cc52..12c1c7567 100644
--- a/reference/generated/tensordict.nn.TensorDictSequential.html
+++ b/reference/generated/tensordict.nn.TensorDictSequential.html
@@ -371,9 +371,6 @@ <h1>TensorDictSequential<a class="headerlink" href="#tensordictsequential" title
 <dt class="sig sig-object py" id="tensordict.nn.TensorDictSequential">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">tensordict.nn.</span></span><span class="sig-name descname"><span class="pre">TensorDictSequential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#tensordict.nn.TensorDictSequential" title="Permalink to this definition">¶</a></dt>
 <dd><p>A sequence of TensorDictModules.</p>
-<p>By default, <a class="reference internal" href="#tensordict.nn.TensorDictSequential" title="tensordict.nn.TensorDictSequential"><code class="xref py py-class docutils literal notranslate"><span class="pre">TensorDictSequential</span></code></a> subclasses are always functional,
-meaning that they support the <code class="docutils literal notranslate"><span class="pre">td_module(input,</span> <span class="pre">params=params)</span></code> function
-call signature.</p>
 <p>Similarly to <code class="xref py py-obj docutils literal notranslate"><span class="pre">nn.Sequence</span></code> which passes a tensor through a chain of mappings that read and write a single tensor
 each, this module will read and write over a tensordict by querying each of the input modules.
 When calling a <code class="xref py py-obj docutils literal notranslate"><span class="pre">TensorDictSequencial</span></code> instance with a functional module, it is expected that the parameter lists (and
@@ -451,8 +448,9 @@ <h1>TensorDictSequential<a class="headerlink" href="#tensordictsequential" title
 <span class="gp">... </span>   <span class="n">module</span><span class="o">=</span><span class="n">module2</span><span class="p">,</span> <span class="n">in_keys</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;hidden&quot;</span><span class="p">],</span> <span class="n">out_keys</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;output&quot;</span><span class="p">]</span>
 <span class="gp">... </span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">td_module</span> <span class="o">=</span> <span class="n">TensorDictSequential</span><span class="p">(</span><span class="n">td_module1</span><span class="p">,</span> <span class="n">td_module2</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">params</span> <span class="o">=</span> <span class="n">make_functional</span><span class="p">(</span><span class="n">td_module</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">_</span> <span class="o">=</span> <span class="n">td_module</span><span class="p">(</span><span class="n">td</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">params</span> <span class="o">=</span> <span class="n">TensorDict</span><span class="o">.</span><span class="n">from_module</span><span class="p">(</span><span class="n">td_module</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">with</span> <span class="n">params</span><span class="o">.</span><span class="n">to_module</span><span class="p">(</span><span class="n">td_module</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="n">_</span> <span class="o">=</span> <span class="n">td_module</span><span class="p">(</span><span class="n">td</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">td</span><span class="p">)</span>
 <span class="go">TensorDict(</span>
 <span class="go">    fields={</span>
@@ -471,7 +469,10 @@ <h1>TensorDictSequential<a class="headerlink" href="#tensordictsequential" title
 <dl>
 <dt>In the vmap case:</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">vmap</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">params</span> <span class="o">=</span> <span class="n">params</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">td_vmap</span> <span class="o">=</span> <span class="n">vmap</span><span class="p">(</span><span class="n">td_module</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="mi">0</span><span class="p">))(</span><span class="n">td</span><span class="p">,</span> <span class="n">params</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">td</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="k">with</span> <span class="n">params</span><span class="o">.</span><span class="n">to_module</span><span class="p">(</span><span class="n">td_module</span><span class="p">):</span>
+<span class="gp">... </span>        <span class="k">return</span> <span class="n">td_module</span><span class="p">(</span><span class="n">td</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">td_vmap</span> <span class="o">=</span> <span class="n">vmap</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="mi">0</span><span class="p">))(</span><span class="n">td</span><span class="p">,</span> <span class="n">params</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">td_vmap</span><span class="p">)</span>
 <span class="go">TensorDict(</span>
 <span class="go">    fields={</span>
diff --git a/searchindex.js b/searchindex.js
index 6322df54a..4d3589bd5 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["distributed", "fx", "index", "overview", "reference/generated/tensordict.LazyStackedTensorDict", "reference/generated/tensordict.MemoryMappedTensor", "reference/generated/tensordict.NonTensorData", "reference/generated/tensordict.PersistentTensorDict", "reference/generated/tensordict.SubTensorDict", "reference/generated/tensordict.TensorDict", "reference/generated/tensordict.TensorDictBase", "reference/generated/tensordict.TensorDictParams", "reference/generated/tensordict.dense_stack_tds", "reference/generated/tensordict.is_batchedtensor", "reference/generated/tensordict.is_memmap", "reference/generated/tensordict.is_tensor_collection", "reference/generated/tensordict.lazy_legacy", "reference/generated/tensordict.make_tensordict", "reference/generated/tensordict.merge_tensordicts", "reference/generated/tensordict.nn.EnsembleModule", "reference/generated/tensordict.nn.ProbabilisticTensorDictModule", "reference/generated/tensordict.nn.TensorDictModule", "reference/generated/tensordict.nn.TensorDictModuleBase", "reference/generated/tensordict.nn.TensorDictModuleWrapper", "reference/generated/tensordict.nn.TensorDictParams", "reference/generated/tensordict.nn.TensorDictSequential", "reference/generated/tensordict.nn.biased_softplus", "reference/generated/tensordict.nn.dispatch", "reference/generated/tensordict.nn.distributions.AddStateIndependentNormalScale", "reference/generated/tensordict.nn.distributions.CompositeDistribution", "reference/generated/tensordict.nn.distributions.Delta", "reference/generated/tensordict.nn.distributions.OneHotCategorical", "reference/generated/tensordict.nn.distributions.TruncatedNormal", "reference/generated/tensordict.nn.get_functional", "reference/generated/tensordict.nn.inv_softplus", "reference/generated/tensordict.nn.is_functional", "reference/generated/tensordict.nn.make_functional", "reference/generated/tensordict.nn.make_tensordict", "reference/generated/tensordict.nn.repopulate_module", 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\ No newline at end of file
diff --git a/sg_execution_times.html b/sg_execution_times.html
index c31d65856..0412c6303 100644
--- a/sg_execution_times.html
+++ b/sg_execution_times.html
@@ -361,7 +361,7 @@
               
   <section id="computation-times">
 <span id="sphx-glr-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">¶</a></h1>
-<p><strong>02:01.082</strong> total execution time for 10 files <strong>from all galleries</strong>:</p>
+<p><strong>01:59.897</strong> total execution time for 10 files <strong>from all galleries</strong>:</p>
 <div class="docutils container">
 <style scoped>
 <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
@@ -383,27 +383,27 @@
 </thead>
 <tbody>
 <tr class="row-even"><td><p><a class="reference internal" href="tutorials/tensorclass_fashion.html#sphx-glr-tutorials-tensorclass-fashion-py"><span class="std std-ref">Using tensorclasses for datasets</span></a> (<code class="docutils literal notranslate"><span class="pre">reference/generated/tutorials/tensorclass_fashion.py</span></code>)</p></td>
-<td><p>00:58.981</p></td>
+<td><p>00:58.373</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="tutorials/data_fashion.html#sphx-glr-tutorials-data-fashion-py"><span class="std std-ref">Using TensorDict for datasets</span></a> (<code class="docutils literal notranslate"><span class="pre">reference/generated/tutorials/data_fashion.py</span></code>)</p></td>
-<td><p>00:50.364</p></td>
+<td><p>00:49.828</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="tutorials/tensordict_module.html#sphx-glr-tutorials-tensordict-module-py"><span class="std std-ref">TensorDictModule</span></a> (<code class="docutils literal notranslate"><span class="pre">reference/generated/tutorials/tensordict_module.py</span></code>)</p></td>
-<td><p>00:10.144</p></td>
+<td><p>00:10.137</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="tutorials/tensorclass_imagenet.html#sphx-glr-tutorials-tensorclass-imagenet-py"><span class="std std-ref">Batched data loading with tensorclasses</span></a> (<code class="docutils literal notranslate"><span class="pre">reference/generated/tutorials/tensorclass_imagenet.py</span></code>)</p></td>
-<td><p>00:01.535</p></td>
+<td><p>00:01.501</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="tutorials/tensordict_memory.html#sphx-glr-tutorials-tensordict-memory-py"><span class="std std-ref">Simplifying PyTorch Memory Management with TensorDict</span></a> (<code class="docutils literal notranslate"><span class="pre">reference/generated/tutorials/tensordict_memory.py</span></code>)</p></td>
-<td><p>00:00.023</p></td>
+<td><p>00:00.025</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="tutorials/tensordict_keys.html#sphx-glr-tutorials-tensordict-keys-py"><span class="std std-ref">Manipulating the keys of a TensorDict</span></a> (<code class="docutils literal notranslate"><span class="pre">reference/generated/tutorials/tensordict_keys.py</span></code>)</p></td>
-<td><p>00:00.010</p></td>
+<td><p>00:00.009</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="tutorials/tensordict_shapes.html#sphx-glr-tutorials-tensordict-shapes-py"><span class="std std-ref">Manipulating the shape of a TensorDict</span></a> (<code class="docutils literal notranslate"><span class="pre">reference/generated/tutorials/tensordict_shapes.py</span></code>)</p></td>
@@ -415,7 +415,7 @@
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="tutorials/tensordict_module_functional.html#sphx-glr-tutorials-tensordict-module-functional-py"><span class="std std-ref">Functionalizing TensorDictModule</span></a> (<code class="docutils literal notranslate"><span class="pre">reference/generated/tutorials/tensordict_module_functional.py</span></code>)</p></td>
-<td><p>00:00.007</p></td>
+<td><p>00:00.006</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="tutorials/tensordict_preallocation.html#sphx-glr-tutorials-tensordict-preallocation-py"><span class="std std-ref">Pre-allocating memory with TensorDict</span></a> (<code class="docutils literal notranslate"><span class="pre">reference/generated/tutorials/tensordict_preallocation.py</span></code>)</p></td>
diff --git a/tutorials/data_fashion.html b/tutorials/data_fashion.html
index 0b01b3663..ba695de92 100644
--- a/tutorials/data_fashion.html
+++ b/tutorials/data_fashion.html
@@ -641,159 +641,159 @@ <h2>Optimizing the parameters<a class="headerlink" href="#optimizing-the-paramet
     is_shared=False)
 Epoch 1
 -------------------------
-loss: 2.297689 [    0/60000]
-loss: 2.280892 [ 6400/60000]
-loss: 2.274654 [12800/60000]
-loss: 2.279074 [19200/60000]
-loss: 2.246684 [25600/60000]
-loss: 2.236528 [32000/60000]
-loss: 2.231100 [38400/60000]
-loss: 2.205158 [44800/60000]
-loss: 2.201136 [51200/60000]
-loss: 2.183861 [57600/60000]
+loss: 2.300480 [    0/60000]
+loss: 2.293064 [ 6400/60000]
+loss: 2.266259 [12800/60000]
+loss: 2.261617 [19200/60000]
+loss: 2.261147 [25600/60000]
+loss: 2.215564 [32000/60000]
+loss: 2.237646 [38400/60000]
+loss: 2.195081 [44800/60000]
+loss: 2.190470 [51200/60000]
+loss: 2.164365 [57600/60000]
 Test Error:
- Accuracy: 46.9%, Avg loss: 2.167354
+ Accuracy: 41.1%, Avg loss: 2.155924
 
 Epoch 2
 -------------------------
-loss: 2.171290 [    0/60000]
-loss: 2.158412 [ 6400/60000]
-loss: 2.111888 [12800/60000]
-loss: 2.133768 [19200/60000]
-loss: 2.084503 [25600/60000]
-loss: 2.034350 [32000/60000]
-loss: 2.050703 [38400/60000]
-loss: 1.979632 [44800/60000]
-loss: 1.976614 [51200/60000]
-loss: 1.923289 [57600/60000]
+loss: 2.162857 [    0/60000]
+loss: 2.156318 [ 6400/60000]
+loss: 2.098328 [12800/60000]
+loss: 2.116032 [19200/60000]
+loss: 2.078008 [25600/60000]
+loss: 2.007684 [32000/60000]
+loss: 2.046304 [38400/60000]
+loss: 1.959306 [44800/60000]
+loss: 1.963503 [51200/60000]
+loss: 1.905034 [57600/60000]
 Test Error:
- Accuracy: 60.4%, Avg loss: 1.910134
+ Accuracy: 56.5%, Avg loss: 1.897783
 
 Epoch 3
 -------------------------
-loss: 1.936399 [    0/60000]
-loss: 1.905847 [ 6400/60000]
-loss: 1.795367 [12800/60000]
-loss: 1.838003 [19200/60000]
-loss: 1.743419 [25600/60000]
-loss: 1.686000 [32000/60000]
-loss: 1.699681 [38400/60000]
-loss: 1.603609 [44800/60000]
-loss: 1.619376 [51200/60000]
-loss: 1.525605 [57600/60000]
+loss: 1.922749 [    0/60000]
+loss: 1.898134 [ 6400/60000]
+loss: 1.786096 [12800/60000]
+loss: 1.830101 [19200/60000]
+loss: 1.727412 [25600/60000]
+loss: 1.672580 [32000/60000]
+loss: 1.700479 [38400/60000]
+loss: 1.590516 [44800/60000]
+loss: 1.615487 [51200/60000]
+loss: 1.517848 [57600/60000]
 Test Error:
- Accuracy: 61.5%, Avg loss: 1.536318
+ Accuracy: 61.8%, Avg loss: 1.532749
 
 Epoch 4
 -------------------------
-loss: 1.596518 [    0/60000]
-loss: 1.559388 [ 6400/60000]
-loss: 1.415173 [12800/60000]
-loss: 1.491498 [19200/60000]
-loss: 1.381014 [25600/60000]
-loss: 1.365470 [32000/60000]
-loss: 1.370804 [38400/60000]
-loss: 1.300947 [44800/60000]
-loss: 1.330507 [51200/60000]
-loss: 1.238865 [57600/60000]
+loss: 1.593874 [    0/60000]
+loss: 1.560890 [ 6400/60000]
+loss: 1.416031 [12800/60000]
+loss: 1.487803 [19200/60000]
+loss: 1.369440 [25600/60000]
+loss: 1.358816 [32000/60000]
+loss: 1.374556 [38400/60000]
+loss: 1.287937 [44800/60000]
+loss: 1.326393 [51200/60000]
+loss: 1.228221 [57600/60000]
 Test Error:
- Accuracy: 63.2%, Avg loss: 1.261017
+ Accuracy: 64.3%, Avg loss: 1.257580
 
 Epoch 5
 -------------------------
-loss: 1.331413 [    0/60000]
-loss: 1.310852 [ 6400/60000]
-loss: 1.153299 [12800/60000]
-loss: 1.264552 [19200/60000]
-loss: 1.141564 [25600/60000]
-loss: 1.159877 [32000/60000]
-loss: 1.169980 [38400/60000]
-loss: 1.117384 [44800/60000]
-loss: 1.152227 [51200/60000]
-loss: 1.074354 [57600/60000]
+loss: 1.331571 [    0/60000]
+loss: 1.314824 [ 6400/60000]
+loss: 1.154153 [12800/60000]
+loss: 1.257705 [19200/60000]
+loss: 1.134324 [25600/60000]
+loss: 1.153518 [32000/60000]
+loss: 1.174747 [38400/60000]
+loss: 1.101449 [44800/60000]
+loss: 1.145374 [51200/60000]
+loss: 1.062342 [57600/60000]
 Test Error:
- Accuracy: 64.7%, Avg loss: 1.091176
+ Accuracy: 65.2%, Avg loss: 1.087329
 
-TensorDict training done! time:  8.2466 s
+TensorDict training done! time:  8.1166 s
 Epoch 1
 -------------------------
-loss: 2.302497 [    0/60000]
-loss: 2.293708 [ 6400/60000]
-loss: 2.275291 [12800/60000]
-loss: 2.277487 [19200/60000]
-loss: 2.256965 [25600/60000]
-loss: 2.226137 [32000/60000]
-loss: 2.235041 [38400/60000]
-loss: 2.204915 [44800/60000]
-loss: 2.205817 [51200/60000]
-loss: 2.175438 [57600/60000]
+loss: 2.298001 [    0/60000]
+loss: 2.287161 [ 6400/60000]
+loss: 2.270800 [12800/60000]
+loss: 2.270414 [19200/60000]
+loss: 2.245303 [25600/60000]
+loss: 2.225265 [32000/60000]
+loss: 2.231304 [38400/60000]
+loss: 2.200896 [44800/60000]
+loss: 2.198071 [51200/60000]
+loss: 2.161562 [57600/60000]
 Test Error:
- Accuracy: 41.0%, Avg loss: 2.167767
+ Accuracy: 50.7%, Avg loss: 2.157821
 
 Epoch 2
 -------------------------
-loss: 2.173955 [    0/60000]
-loss: 2.167189 [ 6400/60000]
-loss: 2.113836 [12800/60000]
-loss: 2.137049 [19200/60000]
-loss: 2.091704 [25600/60000]
-loss: 2.024699 [32000/60000]
-loss: 2.058104 [38400/60000]
-loss: 1.987428 [44800/60000]
-loss: 1.997960 [51200/60000]
-loss: 1.928781 [57600/60000]
+loss: 2.168909 [    0/60000]
+loss: 2.158751 [ 6400/60000]
+loss: 2.096658 [12800/60000]
+loss: 2.108140 [19200/60000]
+loss: 2.061918 [25600/60000]
+loss: 2.008899 [32000/60000]
+loss: 2.032122 [38400/60000]
+loss: 1.955184 [44800/60000]
+loss: 1.954244 [51200/60000]
+loss: 1.873682 [57600/60000]
 Test Error:
- Accuracy: 57.3%, Avg loss: 1.920751
+ Accuracy: 58.9%, Avg loss: 1.876599
 
 Epoch 3
 -------------------------
-loss: 1.947450 [    0/60000]
-loss: 1.923763 [ 6400/60000]
-loss: 1.809623 [12800/60000]
-loss: 1.859243 [19200/60000]
-loss: 1.749499 [25600/60000]
-loss: 1.684024 [32000/60000]
-loss: 1.717253 [38400/60000]
-loss: 1.618240 [44800/60000]
-loss: 1.651242 [51200/60000]
-loss: 1.544094 [57600/60000]
+loss: 1.909632 [    0/60000]
+loss: 1.884026 [ 6400/60000]
+loss: 1.755143 [12800/60000]
+loss: 1.790438 [19200/60000]
+loss: 1.690238 [25600/60000]
+loss: 1.638952 [32000/60000]
+loss: 1.660928 [38400/60000]
+loss: 1.561142 [44800/60000]
+loss: 1.582468 [51200/60000]
+loss: 1.474488 [57600/60000]
 Test Error:
- Accuracy: 61.4%, Avg loss: 1.550630
+ Accuracy: 61.6%, Avg loss: 1.500441
 
 Epoch 4
 -------------------------
-loss: 1.611791 [    0/60000]
-loss: 1.576542 [ 6400/60000]
-loss: 1.422823 [12800/60000]
-loss: 1.509073 [19200/60000]
-loss: 1.374687 [25600/60000]
-loss: 1.361015 [32000/60000]
-loss: 1.386337 [38400/60000]
-loss: 1.310513 [44800/60000]
-loss: 1.355154 [51200/60000]
-loss: 1.253993 [57600/60000]
+loss: 1.560883 [    0/60000]
+loss: 1.537471 [ 6400/60000]
+loss: 1.383097 [12800/60000]
+loss: 1.453247 [19200/60000]
+loss: 1.343728 [25600/60000]
+loss: 1.335195 [32000/60000]
+loss: 1.352697 [38400/60000]
+loss: 1.274515 [44800/60000]
+loss: 1.309380 [51200/60000]
+loss: 1.211425 [57600/60000]
 Test Error:
- Accuracy: 63.9%, Avg loss: 1.267246
+ Accuracy: 63.2%, Avg loss: 1.241849
 
 Epoch 5
 -------------------------
-loss: 1.339752 [    0/60000]
-loss: 1.319095 [ 6400/60000]
-loss: 1.151958 [12800/60000]
-loss: 1.273998 [19200/60000]
-loss: 1.129366 [25600/60000]
-loss: 1.151123 [32000/60000]
-loss: 1.181103 [38400/60000]
-loss: 1.123158 [44800/60000]
-loss: 1.170845 [51200/60000]
-loss: 1.086041 [57600/60000]
+loss: 1.310621 [    0/60000]
+loss: 1.302632 [ 6400/60000]
+loss: 1.136563 [12800/60000]
+loss: 1.238647 [19200/60000]
+loss: 1.124559 [25600/60000]
+loss: 1.142481 [32000/60000]
+loss: 1.167050 [38400/60000]
+loss: 1.099875 [44800/60000]
+loss: 1.140567 [51200/60000]
+loss: 1.059313 [57600/60000]
 Test Error:
- Accuracy: 65.0%, Avg loss: 1.093209
+ Accuracy: 64.6%, Avg loss: 1.082458
 
-Training done! time:  32.7524 s
+Training done! time:  32.8354 s
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 50.364 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 49.828 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorials-data-fashion-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/e330622d5b746a093c669dc804b45baf/data_fashion.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">data_fashion.ipynb</span></code></a></p>
diff --git a/tutorials/sg_execution_times.html b/tutorials/sg_execution_times.html
index 16b355665..0f7046ec9 100644
--- a/tutorials/sg_execution_times.html
+++ b/tutorials/sg_execution_times.html
@@ -361,7 +361,7 @@
               
   <section id="computation-times">
 <span id="sphx-glr-tutorials-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">¶</a></h1>
-<p><strong>02:01.082</strong> total execution time for 10 files <strong>from tutorials</strong>:</p>
+<p><strong>01:59.897</strong> total execution time for 10 files <strong>from tutorials</strong>:</p>
 <div class="docutils container">
 <style scoped>
 <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
@@ -383,27 +383,27 @@
 </thead>
 <tbody>
 <tr class="row-even"><td><p><a class="reference internal" href="tensorclass_fashion.html#sphx-glr-tutorials-tensorclass-fashion-py"><span class="std std-ref">Using tensorclasses for datasets</span></a> (<code class="docutils literal notranslate"><span class="pre">tensorclass_fashion.py</span></code>)</p></td>
-<td><p>00:58.981</p></td>
+<td><p>00:58.373</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="data_fashion.html#sphx-glr-tutorials-data-fashion-py"><span class="std std-ref">Using TensorDict for datasets</span></a> (<code class="docutils literal notranslate"><span class="pre">data_fashion.py</span></code>)</p></td>
-<td><p>00:50.364</p></td>
+<td><p>00:49.828</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="tensordict_module.html#sphx-glr-tutorials-tensordict-module-py"><span class="std std-ref">TensorDictModule</span></a> (<code class="docutils literal notranslate"><span class="pre">tensordict_module.py</span></code>)</p></td>
-<td><p>00:10.144</p></td>
+<td><p>00:10.137</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="tensorclass_imagenet.html#sphx-glr-tutorials-tensorclass-imagenet-py"><span class="std std-ref">Batched data loading with tensorclasses</span></a> (<code class="docutils literal notranslate"><span class="pre">tensorclass_imagenet.py</span></code>)</p></td>
-<td><p>00:01.535</p></td>
+<td><p>00:01.501</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="tensordict_memory.html#sphx-glr-tutorials-tensordict-memory-py"><span class="std std-ref">Simplifying PyTorch Memory Management with TensorDict</span></a> (<code class="docutils literal notranslate"><span class="pre">tensordict_memory.py</span></code>)</p></td>
-<td><p>00:00.023</p></td>
+<td><p>00:00.025</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="tensordict_keys.html#sphx-glr-tutorials-tensordict-keys-py"><span class="std std-ref">Manipulating the keys of a TensorDict</span></a> (<code class="docutils literal notranslate"><span class="pre">tensordict_keys.py</span></code>)</p></td>
-<td><p>00:00.010</p></td>
+<td><p>00:00.009</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="tensordict_shapes.html#sphx-glr-tutorials-tensordict-shapes-py"><span class="std std-ref">Manipulating the shape of a TensorDict</span></a> (<code class="docutils literal notranslate"><span class="pre">tensordict_shapes.py</span></code>)</p></td>
@@ -415,7 +415,7 @@
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="tensordict_module_functional.html#sphx-glr-tutorials-tensordict-module-functional-py"><span class="std std-ref">Functionalizing TensorDictModule</span></a> (<code class="docutils literal notranslate"><span class="pre">tensordict_module_functional.py</span></code>)</p></td>
-<td><p>00:00.007</p></td>
+<td><p>00:00.006</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="tensordict_preallocation.html#sphx-glr-tutorials-tensordict-preallocation-py"><span class="std std-ref">Pre-allocating memory with TensorDict</span></a> (<code class="docutils literal notranslate"><span class="pre">tensordict_preallocation.py</span></code>)</p></td>
diff --git a/tutorials/tensorclass_fashion.html b/tutorials/tensorclass_fashion.html
index 7694933fd..fc23da960 100644
--- a/tutorials/tensorclass_fashion.html
+++ b/tutorials/tensorclass_fashion.html
@@ -412,41 +412,41 @@
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz
 
   0%|          | 0/26421880 [00:00&lt;?, ?it/s]
-  0%|          | 65536/26421880 [00:00&lt;01:11, 366529.78it/s]
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- 26%|██▌       | 6881280/26421880 [00:00&lt;00:01, 13109654.28it/s]
- 49%|████▉     | 12976128/26421880 [00:00&lt;00:00, 23117960.87it/s]
- 61%|██████▏   | 16187392/26421880 [00:01&lt;00:00, 23413161.19it/s]
- 84%|████████▎ | 22118400/26421880 [00:01&lt;00:00, 29644180.74it/s]
- 96%|█████████▌| 25362432/26421880 [00:01&lt;00:00, 28040475.24it/s]
-100%|██████████| 26421880/26421880 [00:01&lt;00:00, 19497898.83it/s]
+  0%|          | 65536/26421880 [00:00&lt;01:12, 361577.27it/s]
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+100%|██████████| 26421880/26421880 [00:01&lt;00:00, 19357445.87it/s]
 Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw
 
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
 
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 Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw
 
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
 
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+100%|██████████| 4422102/4422102 [00:00&lt;00:00, 6084624.81it/s]
 Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw
 
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
 
   0%|          | 0/5148 [00:00&lt;?, ?it/s]
-100%|██████████| 5148/5148 [00:00&lt;00:00, 72701269.33it/s]
+100%|██████████| 5148/5148 [00:00&lt;00:00, 46837911.05it/s]
 Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw
 </pre></div>
 </div>
@@ -685,159 +685,159 @@ <h2>Optimizing the parameters<a class="headerlink" href="#optimizing-the-paramet
     is_shared=False)
 Epoch 1
 -------------------------
-loss: 2.310976 [    0/60000]
-loss: 2.295230 [ 6400/60000]
-loss: 2.283326 [12800/60000]
-loss: 2.278642 [19200/60000]
-loss: 2.255610 [25600/60000]
-loss: 2.232225 [32000/60000]
-loss: 2.231061 [38400/60000]
-loss: 2.200477 [44800/60000]
-loss: 2.201424 [51200/60000]
-loss: 2.166960 [57600/60000]
+loss: 2.302387 [    0/60000]
+loss: 2.293101 [ 6400/60000]
+loss: 2.279598 [12800/60000]
+loss: 2.276973 [19200/60000]
+loss: 2.251420 [25600/60000]
+loss: 2.238299 [32000/60000]
+loss: 2.237562 [38400/60000]
+loss: 2.215429 [44800/60000]
+loss: 2.208091 [51200/60000]
+loss: 2.175614 [57600/60000]
 Test Error:
- Accuracy: 40.3%, Avg loss: 2.167791
+ Accuracy: 48.3%, Avg loss: 2.176059
 
 Epoch 2
 -------------------------
-loss: 2.177774 [    0/60000]
-loss: 2.166296 [ 6400/60000]
-loss: 2.122235 [12800/60000]
-loss: 2.137978 [19200/60000]
-loss: 2.079019 [25600/60000]
-loss: 2.028171 [32000/60000]
-loss: 2.046031 [38400/60000]
-loss: 1.973215 [44800/60000]
-loss: 1.979031 [51200/60000]
-loss: 1.902367 [57600/60000]
+loss: 2.183834 [    0/60000]
+loss: 2.175081 [ 6400/60000]
+loss: 2.129839 [12800/60000]
+loss: 2.143353 [19200/60000]
+loss: 2.089312 [25600/60000]
+loss: 2.056283 [32000/60000]
+loss: 2.060929 [38400/60000]
+loss: 2.006752 [44800/60000]
+loss: 2.002385 [51200/60000]
+loss: 1.926731 [57600/60000]
 Test Error:
- Accuracy: 48.2%, Avg loss: 1.911800
+ Accuracy: 61.7%, Avg loss: 1.935310
 
 Epoch 3
 -------------------------
-loss: 1.940137 [    0/60000]
-loss: 1.909616 [ 6400/60000]
-loss: 1.814698 [12800/60000]
-loss: 1.854172 [19200/60000]
-loss: 1.726293 [25600/60000]
-loss: 1.688812 [32000/60000]
-loss: 1.703465 [38400/60000]
-loss: 1.610862 [44800/60000]
-loss: 1.633218 [51200/60000]
-loss: 1.529064 [57600/60000]
+loss: 1.960144 [    0/60000]
+loss: 1.936480 [ 6400/60000]
+loss: 1.833049 [12800/60000]
+loss: 1.864682 [19200/60000]
+loss: 1.759800 [25600/60000]
+loss: 1.725796 [32000/60000]
+loss: 1.714761 [38400/60000]
+loss: 1.638511 [44800/60000]
+loss: 1.644561 [51200/60000]
+loss: 1.533933 [57600/60000]
 Test Error:
- Accuracy: 59.2%, Avg loss: 1.552986
+ Accuracy: 61.5%, Avg loss: 1.563183
 
 Epoch 4
 -------------------------
-loss: 1.612865 [    0/60000]
-loss: 1.574731 [ 6400/60000]
-loss: 1.449299 [12800/60000]
-loss: 1.519882 [19200/60000]
-loss: 1.382809 [25600/60000]
-loss: 1.389852 [32000/60000]
-loss: 1.397839 [38400/60000]
-loss: 1.323305 [44800/60000]
-loss: 1.355896 [51200/60000]
-loss: 1.258844 [57600/60000]
+loss: 1.621519 [    0/60000]
+loss: 1.590653 [ 6400/60000]
+loss: 1.447826 [12800/60000]
+loss: 1.512125 [19200/60000]
+loss: 1.394294 [25600/60000]
+loss: 1.402328 [32000/60000]
+loss: 1.389260 [38400/60000]
+loss: 1.328610 [44800/60000]
+loss: 1.353315 [51200/60000]
+loss: 1.252565 [57600/60000]
 Test Error:
- Accuracy: 63.2%, Avg loss: 1.284397
+ Accuracy: 62.3%, Avg loss: 1.282288
 
 Epoch 5
 -------------------------
-loss: 1.358123 [    0/60000]
-loss: 1.332349 [ 6400/60000]
-loss: 1.188338 [12800/60000]
-loss: 1.291905 [19200/60000]
-loss: 1.153745 [25600/60000]
-loss: 1.188745 [32000/60000]
-loss: 1.200132 [38400/60000]
-loss: 1.134685 [44800/60000]
-loss: 1.174266 [51200/60000]
-loss: 1.089714 [57600/60000]
+loss: 1.356653 [    0/60000]
+loss: 1.337689 [ 6400/60000]
+loss: 1.176921 [12800/60000]
+loss: 1.280601 [19200/60000]
+loss: 1.154103 [25600/60000]
+loss: 1.194018 [32000/60000]
+loss: 1.192287 [38400/60000]
+loss: 1.138430 [44800/60000]
+loss: 1.173223 [51200/60000]
+loss: 1.091239 [57600/60000]
 Test Error:
- Accuracy: 64.8%, Avg loss: 1.110009
+ Accuracy: 64.0%, Avg loss: 1.110041
 
-Tensorclass training done! time:  8.5711 s
+Tensorclass training done! time:  8.3111 s
 Epoch 1
 -------------------------
-loss: 2.303066 [    0/60000]
-loss: 2.290508 [ 6400/60000]
-loss: 2.268382 [12800/60000]
-loss: 2.267677 [19200/60000]
-loss: 2.243165 [25600/60000]
-loss: 2.219261 [32000/60000]
-loss: 2.226329 [38400/60000]
-loss: 2.190671 [44800/60000]
-loss: 2.192858 [51200/60000]
-loss: 2.160490 [57600/60000]
+loss: 2.297948 [    0/60000]
+loss: 2.282624 [ 6400/60000]
+loss: 2.271977 [12800/60000]
+loss: 2.270170 [19200/60000]
+loss: 2.244367 [25600/60000]
+loss: 2.214798 [32000/60000]
+loss: 2.220860 [38400/60000]
+loss: 2.188947 [44800/60000]
+loss: 2.186659 [51200/60000]
+loss: 2.154645 [57600/60000]
 Test Error:
- Accuracy: 47.0%, Avg loss: 2.155192
+ Accuracy: 53.5%, Avg loss: 2.150539
 
 Epoch 2
 -------------------------
-loss: 2.164057 [    0/60000]
-loss: 2.159464 [ 6400/60000]
-loss: 2.098034 [12800/60000]
-loss: 2.120966 [19200/60000]
-loss: 2.061083 [25600/60000]
-loss: 2.001219 [32000/60000]
-loss: 2.026425 [38400/60000]
-loss: 1.953357 [44800/60000]
-loss: 1.962681 [51200/60000]
-loss: 1.875333 [57600/60000]
+loss: 2.160149 [    0/60000]
+loss: 2.149896 [ 6400/60000]
+loss: 2.097871 [12800/60000]
+loss: 2.114169 [19200/60000]
+loss: 2.062091 [25600/60000]
+loss: 1.994953 [32000/60000]
+loss: 2.024422 [38400/60000]
+loss: 1.950521 [44800/60000]
+loss: 1.953714 [51200/60000]
+loss: 1.877175 [57600/60000]
 Test Error:
- Accuracy: 58.2%, Avg loss: 1.887924
+ Accuracy: 61.8%, Avg loss: 1.881925
 
 Epoch 3
 -------------------------
-loss: 1.922889 [    0/60000]
-loss: 1.895636 [ 6400/60000]
-loss: 1.781903 [12800/60000]
-loss: 1.824525 [19200/60000]
-loss: 1.701341 [25600/60000]
-loss: 1.654470 [32000/60000]
-loss: 1.671035 [38400/60000]
-loss: 1.587242 [44800/60000]
-loss: 1.609092 [51200/60000]
-loss: 1.483521 [57600/60000]
+loss: 1.919103 [    0/60000]
+loss: 1.886964 [ 6400/60000]
+loss: 1.774065 [12800/60000]
+loss: 1.809855 [19200/60000]
+loss: 1.709801 [25600/60000]
+loss: 1.652143 [32000/60000]
+loss: 1.673265 [38400/60000]
+loss: 1.587407 [44800/60000]
+loss: 1.611894 [51200/60000]
+loss: 1.495472 [57600/60000]
 Test Error:
- Accuracy: 61.2%, Avg loss: 1.520375
+ Accuracy: 62.1%, Avg loss: 1.522582
 
 Epoch 4
 -------------------------
-loss: 1.591304 [    0/60000]
-loss: 1.553071 [ 6400/60000]
-loss: 1.408833 [12800/60000]
-loss: 1.483236 [19200/60000]
-loss: 1.347654 [25600/60000]
-loss: 1.352496 [32000/60000]
-loss: 1.357938 [38400/60000]
-loss: 1.298349 [44800/60000]
-loss: 1.325898 [51200/60000]
-loss: 1.211882 [57600/60000]
+loss: 1.595548 [    0/60000]
+loss: 1.556155 [ 6400/60000]
+loss: 1.412991 [12800/60000]
+loss: 1.479574 [19200/60000]
+loss: 1.363339 [25600/60000]
+loss: 1.351150 [32000/60000]
+loss: 1.366776 [38400/60000]
+loss: 1.303567 [44800/60000]
+loss: 1.337294 [51200/60000]
+loss: 1.226468 [57600/60000]
 Test Error:
- Accuracy: 63.6%, Avg loss: 1.250856
+ Accuracy: 63.7%, Avg loss: 1.258048
 
 Epoch 5
 -------------------------
-loss: 1.331446 [    0/60000]
-loss: 1.307861 [ 6400/60000]
-loss: 1.147009 [12800/60000]
-loss: 1.259104 [19200/60000]
-loss: 1.119098 [25600/60000]
-loss: 1.154116 [32000/60000]
-loss: 1.165835 [38400/60000]
-loss: 1.116898 [44800/60000]
-loss: 1.149531 [51200/60000]
-loss: 1.052141 [57600/60000]
+loss: 1.340565 [    0/60000]
+loss: 1.316801 [ 6400/60000]
+loss: 1.157856 [12800/60000]
+loss: 1.260136 [19200/60000]
+loss: 1.127928 [25600/60000]
+loss: 1.149427 [32000/60000]
+loss: 1.173949 [38400/60000]
+loss: 1.122528 [44800/60000]
+loss: 1.160417 [51200/60000]
+loss: 1.064355 [57600/60000]
 Test Error:
- Accuracy: 64.8%, Avg loss: 1.085073
+ Accuracy: 64.7%, Avg loss: 1.089903
 
-Training done! time:  33.1519 s
+Training done! time:  32.9774 s
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 58.981 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 58.373 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorials-tensorclass-fashion-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/5a8f2d6b38b250cbfadc2a295255dde6/tensorclass_fashion.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">tensorclass_fashion.ipynb</span></code></a></p>
diff --git a/tutorials/tensorclass_imagenet.html b/tutorials/tensorclass_imagenet.html
index 5e4d5b301..4468e3a65 100644
--- a/tutorials/tensorclass_imagenet.html
+++ b/tutorials/tensorclass_imagenet.html
@@ -751,7 +751,7 @@
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Using device: cpu
 
   0%|          | 0/244 [00:00&lt;?, ?it/s]
- 26%|██▌       | 64/244 [00:00&lt;00:00, 233.18it/s]ImageNetData(
+ 26%|██▌       | 64/244 [00:00&lt;00:00, 227.26it/s]ImageNetData(
     images=MemoryMappedTensor(shape=torch.Size([244, 3, 256, 256]), device=cpu, dtype=torch.uint8, is_shared=False),
     targets=MemoryMappedTensor(shape=torch.Size([244]), device=cpu, dtype=torch.int64, is_shared=False),
     batch_size=torch.Size([244]),
@@ -800,10 +800,10 @@
     device=None,
     is_shared=False)
 
-100%|██████████| 244/244 [00:00&lt;00:00, 756.46it/s]
+100%|██████████| 244/244 [00:00&lt;00:00, 771.51it/s]
 
   0%|          | 0/153 [00:00&lt;?, ?it/s]
- 42%|████▏     | 64/153 [00:00&lt;00:00, 191.86it/s]ImageNetData(
+ 42%|████▏     | 64/153 [00:00&lt;00:00, 204.96it/s]ImageNetData(
     images=MemoryMappedTensor(shape=torch.Size([153, 3, 224, 224]), device=cpu, dtype=torch.uint8, is_shared=False),
     targets=MemoryMappedTensor(shape=torch.Size([153]), device=cpu, dtype=torch.int64, is_shared=False),
     batch_size=torch.Size([153]),
@@ -840,14 +840,14 @@
     device=None,
     is_shared=False)
 
-100%|██████████| 153/153 [00:00&lt;00:00, 372.18it/s]
-One iteration over dataloader done! Rate: 1077.1951 fps, time:  0.2042s
-One iteration over tensorclass dataloader done! Rate: 1733.7566 fps, time:  0.1269s
-One iteration over val data done! Rate: 695.0831 fps, time:  0.1856s
-One iteration over tensorclass val data done! Rate: 18441.2139 fps, time:  0.0070s
+100%|██████████| 153/153 [00:00&lt;00:00, 377.96it/s]
+One iteration over dataloader done! Rate: 1022.5849 fps, time:  0.2151s
+One iteration over tensorclass dataloader done! Rate: 1766.7430 fps, time:  0.1245s
+One iteration over val data done! Rate: 837.4922 fps, time:  0.1540s
+One iteration over tensorclass val data done! Rate: 16062.4971 fps, time:  0.0080s
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.535 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.501 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorials-tensorclass-imagenet-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/a4db2295c156dd86b1f4437d354c9744/tensorclass_imagenet.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">tensorclass_imagenet.ipynb</span></code></a></p>
diff --git a/tutorials/tensordict_keys.html b/tutorials/tensordict_keys.html
index 62cc8ddbc..034ef4cab 100644
--- a/tutorials/tensordict_keys.html
+++ b/tutorials/tensordict_keys.html
@@ -804,7 +804,7 @@ <h2>Selecting and excluding keys<a class="headerlink" href="#selecting-and-exclu
     is_shared=False)
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.010 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.009 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorials-tensordict-keys-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/97d8d73d25e56588177c9d8bc7211dc9/tensordict_keys.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">tensordict_keys.ipynb</span></code></a></p>
diff --git a/tutorials/tensordict_memory.html b/tutorials/tensordict_memory.html
index 8efc14f74..0cec237ff 100644
--- a/tutorials/tensordict_memory.html
+++ b/tutorials/tensordict_memory.html
@@ -514,7 +514,7 @@ <h2>Memory-mapped Tensors<a class="headerlink" href="#memory-mapped-tensors" tit
 └── meta.pt
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.023 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.025 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorials-tensordict-memory-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/8e7998c9628f210b43934d3aead5e4e6/tensordict_memory.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">tensordict_memory.ipynb</span></code></a></p>
diff --git a/tutorials/tensordict_module.html b/tutorials/tensordict_module.html
index 3312dbb72..951db1a2d 100644
--- a/tutorials/tensordict_module.html
+++ b/tutorials/tensordict_module.html
@@ -1087,7 +1087,7 @@ <h3>Benchmarking<a class="headerlink" href="#benchmarking" title="Permalink to t
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Execution time:&quot;</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t2</span></a> <span class="o">-</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t1</span></a><span class="p">,</span> <span class="s2">&quot;seconds&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time: 0.011106491088867188 seconds
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time: 0.01023244857788086 seconds
 </pre></div>
 </div>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t3</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span>
@@ -1096,13 +1096,13 @@ <h3>Benchmarking<a class="headerlink" href="#benchmarking" title="Permalink to t
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Execution time:&quot;</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t4</span></a> <span class="o">-</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">t3</span></a><span class="p">,</span> <span class="s2">&quot;seconds&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time: 0.006401538848876953 seconds
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time: 0.0059583187103271484 seconds
 </pre></div>
 </div>
 <p>We can see on this minimal example that the overhead introduced by
 <code class="docutils literal notranslate"><span class="pre">TensorDictModule</span></code> is marginal.</p>
 <p>Have fun with TensorDictModule!</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 10.144 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 10.137 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorials-tensordict-module-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/ba42555713e16dc342a5f3e550dec192/tensordict_module.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">tensordict_module.ipynb</span></code></a></p>
diff --git a/tutorials/tensordict_module_functional.html b/tutorials/tensordict_module_functional.html
index 3c7df9258..8bff2cc55 100644
--- a/tutorials/tensordict_module_functional.html
+++ b/tutorials/tensordict_module_functional.html
@@ -468,7 +468,7 @@
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>the output tensordict shape is:  torch.Size([2, 5])
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.007 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.006 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorials-tensordict-module-functional-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/bb03bdb64e6f64d44afa700f37bae266/tensordict_module_functional.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">tensordict_module_functional.ipynb</span></code></a></p>
diff --git a/tutorials/tensordict_shapes.html b/tutorials/tensordict_shapes.html
index 8d067e6f7..266985f8e 100644
--- a/tutorials/tensordict_shapes.html
+++ b/tutorials/tensordict_shapes.html
@@ -644,19 +644,19 @@ <h2>Gathering values in <code class="docutils literal notranslate"><span class="
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>index:
- tensor([[1, 3, 1, 0],
-        [1, 2, 3, 1],
-        [1, 1, 0, 1]])
+ tensor([[0, 3, 2, 1],
+        [2, 2, 3, 2],
+        [2, 0, 2, 0]])
 
 tensordict[&#39;a&#39;]:
- tensor([[0.6435, 0.8054, 0.9983, 0.4238],
-        [0.9842, 0.4340, 0.8523, 0.5265],
-        [0.1708, 0.0143, 0.2981, 0.9721]])
+ tensor([[0.4003, 0.3863, 0.8916, 0.4065],
+        [0.2656, 0.6441, 0.3445, 0.6833],
+        [0.6153, 0.8126, 0.9607, 0.1182]])
 
 gathered_tensordict[&#39;a&#39;]:
- tensor([[0.8054, 0.4238, 0.8054, 0.6435],
-        [0.4340, 0.8523, 0.5265, 0.4340],
-        [0.0143, 0.0143, 0.1708, 0.0143]])
+ tensor([[0.4003, 0.4065, 0.8916, 0.3863],
+        [0.3445, 0.3445, 0.6833, 0.3445],
+        [0.9607, 0.6153, 0.9607, 0.6153]])
 </pre></div>
 </div>
 <p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.008 seconds)</p>