diff --git a/main/_downloads/0935354946f64f2fcf766478aa4572c3/tensordict_module.zip b/main/_downloads/0935354946f64f2fcf766478aa4572c3/tensordict_module.zip
index 4dccbfd59..2785ced86 100644
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diff --git a/main/_downloads/150528e38f6816824f1e81ed67476a9f/export.zip b/main/_downloads/150528e38f6816824f1e81ed67476a9f/export.zip
index 555a28a43..63f1bb00d 100644
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diff --git a/main/_downloads/30cf47ff46cc4701c97ed46f4d91a0f2/tensordict_keys.zip b/main/_downloads/30cf47ff46cc4701c97ed46f4d91a0f2/tensordict_keys.zip
index 77f505c96..220f32001 100644
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diff --git a/main/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip b/main/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip
index 1959e577a..12a0a3614 100644
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diff --git a/main/_downloads/5b6beccb0b7dcf81c69b35cec5a445ee/tensorclass_imagenet.zip b/main/_downloads/5b6beccb0b7dcf81c69b35cec5a445ee/tensorclass_imagenet.zip
index 6313fdf49..34bff707c 100644
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diff --git a/main/_downloads/71264da051c82d595130ca8e60350024/tensordict_shapes.zip b/main/_downloads/71264da051c82d595130ca8e60350024/tensordict_shapes.zip
index ffc143876..a99957aa2 100644
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diff --git a/main/_downloads/777fb49680f0121bef451f089b823d24/tensorclass_fashion.zip b/main/_downloads/777fb49680f0121bef451f089b823d24/tensorclass_fashion.zip
index 657269800..b59d38b53 100644
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diff --git a/main/_downloads/89ddb148138028541dafab3ced0e0421/tensordict_memory.zip b/main/_downloads/89ddb148138028541dafab3ced0e0421/tensordict_memory.zip
index 9016d1303..9f5fc3e9f 100644
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diff --git a/main/_downloads/a3a53a08487286803e7e9713be78fb54/tensordict_preallocation.zip b/main/_downloads/a3a53a08487286803e7e9713be78fb54/tensordict_preallocation.zip
index efc46c9be..a26f2b965 100644
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diff --git a/main/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip b/main/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip
index 4aced48fd..2d53d535f 100644
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diff --git a/main/_downloads/bddca9d9cab285e6fedc82c7769291ef/data_fashion.zip b/main/_downloads/bddca9d9cab285e6fedc82c7769291ef/data_fashion.zip
index 6a31f1b29..33c39b8df 100644
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diff --git a/main/_downloads/c1f74b953ed757c75e2d2bc64666eae1/streamed_tensordict.zip b/main/_downloads/c1f74b953ed757c75e2d2bc64666eae1/streamed_tensordict.zip
index a9feb12be..1bd01f66a 100644
Binary files a/main/_downloads/c1f74b953ed757c75e2d2bc64666eae1/streamed_tensordict.zip and b/main/_downloads/c1f74b953ed757c75e2d2bc64666eae1/streamed_tensordict.zip differ
diff --git a/main/_downloads/c1fc5aa7548e6b1437d36fb47bc36ce5/tensordict_slicing.zip b/main/_downloads/c1fc5aa7548e6b1437d36fb47bc36ce5/tensordict_slicing.zip
index 938f54b1e..00babb38e 100644
Binary files a/main/_downloads/c1fc5aa7548e6b1437d36fb47bc36ce5/tensordict_slicing.zip and b/main/_downloads/c1fc5aa7548e6b1437d36fb47bc36ce5/tensordict_slicing.zip differ
diff --git a/main/_sources/sg_execution_times.rst.txt b/main/_sources/sg_execution_times.rst.txt
index 32b28cc65..b6988dd52 100644
--- a/main/_sources/sg_execution_times.rst.txt
+++ b/main/_sources/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**02:33.910** total execution time for 11 files **from all galleries**:
+**02:29.308** total execution time for 11 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``)
-     - 01:03.156
+     - 01:00.787
      - 0.0
    * - :ref:`sphx_glr_tutorials_data_fashion.py` (``reference/generated/tutorials/data_fashion.py``)
-     - 00:57.776
+     - 00:55.623
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_module.py` (``reference/generated/tutorials/tensordict_module.py``)
-     - 00:18.536
+     - 00:18.477
      - 0.0
    * - :ref:`sphx_glr_tutorials_streamed_tensordict.py` (``reference/generated/tutorials/streamed_tensordict.py``)
-     - 00:11.020
+     - 00:11.021
      - 0.0
    * - :ref:`sphx_glr_tutorials_export.py` (``reference/generated/tutorials/export.py``)
-     - 00:01.749
+     - 00:01.699
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensorclass_imagenet.py` (``reference/generated/tutorials/tensorclass_imagenet.py``)
-     - 00:01.644
+     - 00:01.673
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_keys.py` (``reference/generated/tutorials/tensordict_keys.py``)
      - 00:00.009
@@ -57,7 +57,7 @@ Computation times
      - 00:00.008
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_slicing.py` (``reference/generated/tutorials/tensordict_slicing.py``)
-     - 00:00.005
+     - 00:00.004
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_memory.py` (``reference/generated/tutorials/tensordict_memory.py``)
      - 00:00.004
diff --git a/main/_sources/tutorials/data_fashion.rst.txt b/main/_sources/tutorials/data_fashion.rst.txt
index ef08cbf6a..81cd118f8 100644
--- a/main/_sources/tutorials/data_fashion.rst.txt
+++ b/main/_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: 2.295148 [    0/60000]
-    loss: 2.288687 [ 6400/60000]
-    loss: 2.264276 [12800/60000]
-    loss: 2.257121 [19200/60000]
-    loss: 2.249283 [25600/60000]
-    loss: 2.211193 [32000/60000]
-    loss: 2.216204 [38400/60000]
-    loss: 2.180449 [44800/60000]
-    loss: 2.179368 [51200/60000]
-    loss: 2.146407 [57600/60000]
+    loss: 2.297034 [    0/60000]
+    loss: 2.293723 [ 6400/60000]
+    loss: 2.269874 [12800/60000]
+    loss: 2.265041 [19200/60000]
+    loss: 2.247218 [25600/60000]
+    loss: 2.226938 [32000/60000]
+    loss: 2.226363 [38400/60000]
+    loss: 2.194768 [44800/60000]
+    loss: 2.195367 [51200/60000]
+    loss: 2.161224 [57600/60000]
     Test Error: 
-     Accuracy: 48.0%, Avg loss: 2.141438 
+     Accuracy: 43.4%, Avg loss: 2.156612 
 
     Epoch 2
     -------------------------
-    loss: 2.148195 [    0/60000]
-    loss: 2.141373 [ 6400/60000]
-    loss: 2.079053 [12800/60000]
-    loss: 2.093180 [19200/60000]
-    loss: 2.041999 [25600/60000]
-    loss: 1.976888 [32000/60000]
-    loss: 1.994016 [38400/60000]
-    loss: 1.914325 [44800/60000]
-    loss: 1.923628 [51200/60000]
-    loss: 1.836888 [57600/60000]
+    loss: 2.160449 [    0/60000]
+    loss: 2.160634 [ 6400/60000]
+    loss: 2.090734 [12800/60000]
+    loss: 2.112868 [19200/60000]
+    loss: 2.055511 [25600/60000]
+    loss: 2.001153 [32000/60000]
+    loss: 2.027912 [38400/60000]
+    loss: 1.945694 [44800/60000]
+    loss: 1.961530 [51200/60000]
+    loss: 1.883855 [57600/60000]
     Test Error: 
-     Accuracy: 57.2%, Avg loss: 1.842130 
+     Accuracy: 53.6%, Avg loss: 1.881469 
 
     Epoch 3
     -------------------------
-    loss: 1.879584 [    0/60000]
-    loss: 1.845570 [ 6400/60000]
-    loss: 1.727267 [12800/60000]
-    loss: 1.765438 [19200/60000]
-    loss: 1.662461 [25600/60000]
-    loss: 1.614136 [32000/60000]
-    loss: 1.628378 [38400/60000]
-    loss: 1.535969 [44800/60000]
-    loss: 1.567617 [51200/60000]
-    loss: 1.456037 [57600/60000]
+    loss: 1.905408 [    0/60000]
+    loss: 1.890264 [ 6400/60000]
+    loss: 1.755859 [12800/60000]
+    loss: 1.807360 [19200/60000]
+    loss: 1.687582 [25600/60000]
+    loss: 1.644098 [32000/60000]
+    loss: 1.667204 [38400/60000]
+    loss: 1.568138 [44800/60000]
+    loss: 1.598023 [51200/60000]
+    loss: 1.490839 [57600/60000]
     Test Error: 
-     Accuracy: 60.1%, Avg loss: 1.480593 
+     Accuracy: 61.1%, Avg loss: 1.508179 
 
     Epoch 4
     -------------------------
-    loss: 1.552394 [    0/60000]
-    loss: 1.519605 [ 6400/60000]
-    loss: 1.368929 [12800/60000]
-    loss: 1.442735 [19200/60000]
-    loss: 1.331060 [25600/60000]
-    loss: 1.328798 [32000/60000]
-    loss: 1.342121 [38400/60000]
-    loss: 1.268073 [44800/60000]
-    loss: 1.305307 [51200/60000]
-    loss: 1.209298 [57600/60000]
+    loss: 1.564770 [    0/60000]
+    loss: 1.547288 [ 6400/60000]
+    loss: 1.384363 [12800/60000]
+    loss: 1.462441 [19200/60000]
+    loss: 1.338688 [25600/60000]
+    loss: 1.338367 [32000/60000]
+    loss: 1.350146 [38400/60000]
+    loss: 1.280336 [44800/60000]
+    loss: 1.319443 [51200/60000]
+    loss: 1.217629 [57600/60000]
     Test Error: 
-     Accuracy: 62.6%, Avg loss: 1.234078 
+     Accuracy: 64.0%, Avg loss: 1.243834 
 
     Epoch 5
     -------------------------
-    loss: 1.312373 [    0/60000]
-    loss: 1.298895 [ 6400/60000]
-    loss: 1.127474 [12800/60000]
-    loss: 1.239774 [19200/60000]
-    loss: 1.117170 [25600/60000]
-    loss: 1.143492 [32000/60000]
-    loss: 1.166954 [38400/60000]
-    loss: 1.100850 [44800/60000]
-    loss: 1.139860 [51200/60000]
-    loss: 1.060362 [57600/60000]
+    loss: 1.309603 [    0/60000]
+    loss: 1.309187 [ 6400/60000]
+    loss: 1.132445 [12800/60000]
+    loss: 1.242703 [19200/60000]
+    loss: 1.116270 [25600/60000]
+    loss: 1.142112 [32000/60000]
+    loss: 1.159446 [38400/60000]
+    loss: 1.102855 [44800/60000]
+    loss: 1.148942 [51200/60000]
+    loss: 1.061529 [57600/60000]
     Test Error: 
-     Accuracy: 64.5%, Avg loss: 1.079705 
+     Accuracy: 65.6%, Avg loss: 1.082501 
 
-    TensorDict training done! time:  8.6095 s
+    TensorDict training done! time:  8.6850 s
     Epoch 1
     -------------------------
-    loss: 2.295774 [    0/60000]
-    loss: 2.289083 [ 6400/60000]
-    loss: 2.267943 [12800/60000]
-    loss: 2.271904 [19200/60000]
-    loss: 2.251286 [25600/60000]
-    loss: 2.229369 [32000/60000]
-    loss: 2.237860 [38400/60000]
-    loss: 2.203740 [44800/60000]
-    loss: 2.204681 [51200/60000]
-    loss: 2.181303 [57600/60000]
+    loss: 2.303568 [    0/60000]
+    loss: 2.292461 [ 6400/60000]
+    loss: 2.262461 [12800/60000]
+    loss: 2.258972 [19200/60000]
+    loss: 2.241117 [25600/60000]
+    loss: 2.201943 [32000/60000]
+    loss: 2.217089 [38400/60000]
+    loss: 2.180319 [44800/60000]
+    loss: 2.187558 [51200/60000]
+    loss: 2.131742 [57600/60000]
     Test Error: 
-     Accuracy: 47.0%, Avg loss: 2.171147 
+     Accuracy: 31.7%, Avg loss: 2.140259 
 
     Epoch 2
     -------------------------
-    loss: 2.176008 [    0/60000]
-    loss: 2.166186 [ 6400/60000]
-    loss: 2.107857 [12800/60000]
-    loss: 2.134267 [19200/60000]
-    loss: 2.080149 [25600/60000]
-    loss: 2.030640 [32000/60000]
-    loss: 2.056546 [38400/60000]
-    loss: 1.976407 [44800/60000]
-    loss: 1.991985 [51200/60000]
-    loss: 1.931681 [57600/60000]
+    loss: 2.164973 [    0/60000]
+    loss: 2.150137 [ 6400/60000]
+    loss: 2.077849 [12800/60000]
+    loss: 2.093443 [19200/60000]
+    loss: 2.038481 [25600/60000]
+    loss: 1.973073 [32000/60000]
+    loss: 1.998879 [38400/60000]
+    loss: 1.921673 [44800/60000]
+    loss: 1.933168 [51200/60000]
+    loss: 1.831749 [57600/60000]
     Test Error: 
-     Accuracy: 53.7%, Avg loss: 1.918879 
+     Accuracy: 48.7%, Avg loss: 1.849003 
 
     Epoch 3
     -------------------------
-    loss: 1.944126 [    0/60000]
-    loss: 1.917355 [ 6400/60000]
-    loss: 1.795971 [12800/60000]
-    loss: 1.848915 [19200/60000]
-    loss: 1.745302 [25600/60000]
-    loss: 1.685439 [32000/60000]
-    loss: 1.715304 [38400/60000]
-    loss: 1.603045 [44800/60000]
-    loss: 1.642661 [51200/60000]
-    loss: 1.552419 [57600/60000]
+    loss: 1.898297 [    0/60000]
+    loss: 1.863893 [ 6400/60000]
+    loss: 1.733816 [12800/60000]
+    loss: 1.774636 [19200/60000]
+    loss: 1.670297 [25600/60000]
+    loss: 1.623713 [32000/60000]
+    loss: 1.639363 [38400/60000]
+    loss: 1.557734 [44800/60000]
+    loss: 1.581697 [51200/60000]
+    loss: 1.458045 [57600/60000]
     Test Error: 
-     Accuracy: 60.2%, Avg loss: 1.555570 
+     Accuracy: 59.3%, Avg loss: 1.494883 
 
     Epoch 4
     -------------------------
-    loss: 1.612758 [    0/60000]
-    loss: 1.585966 [ 6400/60000]
-    loss: 1.427406 [12800/60000]
-    loss: 1.507616 [19200/60000]
-    loss: 1.402105 [25600/60000]
-    loss: 1.379785 [32000/60000]
-    loss: 1.396852 [38400/60000]
-    loss: 1.307169 [44800/60000]
-    loss: 1.354686 [51200/60000]
-    loss: 1.271429 [57600/60000]
+    loss: 1.569354 [    0/60000]
+    loss: 1.539536 [ 6400/60000]
+    loss: 1.385659 [12800/60000]
+    loss: 1.456576 [19200/60000]
+    loss: 1.343820 [25600/60000]
+    loss: 1.342496 [32000/60000]
+    loss: 1.348758 [38400/60000]
+    loss: 1.292623 [44800/60000]
+    loss: 1.323011 [51200/60000]
+    loss: 1.209712 [57600/60000]
     Test Error: 
-     Accuracy: 62.9%, Avg loss: 1.284187 
+     Accuracy: 63.5%, Avg loss: 1.249484 
 
     Epoch 5
     -------------------------
-    loss: 1.352535 [    0/60000]
-    loss: 1.344217 [ 6400/60000]
-    loss: 1.170571 [12800/60000]
-    loss: 1.279448 [19200/60000]
-    loss: 1.167800 [25600/60000]
-    loss: 1.178811 [32000/60000]
-    loss: 1.196409 [38400/60000]
-    loss: 1.121528 [44800/60000]
-    loss: 1.170856 [51200/60000]
-    loss: 1.104489 [57600/60000]
+    loss: 1.326457 [    0/60000]
+    loss: 1.315519 [ 6400/60000]
+    loss: 1.147514 [12800/60000]
+    loss: 1.252299 [19200/60000]
+    loss: 1.127516 [25600/60000]
+    loss: 1.154889 [32000/60000]
+    loss: 1.166634 [38400/60000]
+    loss: 1.122377 [44800/60000]
+    loss: 1.159742 [51200/60000]
+    loss: 1.059098 [57600/60000]
     Test Error: 
-     Accuracy: 64.3%, Avg loss: 1.112184 
+     Accuracy: 65.0%, Avg loss: 1.092335 
 
-    Training done! time:  36.0000 s
+    Training done! time:  34.4519 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 57.776 seconds)
+   **Total running time of the script:** (0 minutes 55.623 seconds)
 
 
 .. _sphx_glr_download_tutorials_data_fashion.py:
diff --git a/main/_sources/tutorials/export.rst.txt b/main/_sources/tutorials/export.rst.txt
index 01444c0ea..a39cbb5ef 100644
--- a/main/_sources/tutorials/export.rst.txt
+++ b/main/_sources/tutorials/export.rst.txt
@@ -141,7 +141,7 @@ Let us run this model and see what the output looks like:
 
  .. code-block:: none
 
-    (tensor([[0., 0., 0., 0.]], grad_fn=<ReluBackward0>), tensor([[-0.2587, -0.1564,  0.1451,  0.2811]], grad_fn=<AddmmBackward0>), tensor([[-0.2587, -0.1564]], grad_fn=<SplitBackward0>), tensor([[1.0936, 1.1856]], grad_fn=<ClampMinBackward0>), tensor([[-0.2587, -0.1564]], grad_fn=<SplitBackward0>))
+    (tensor([[0.0000, 0.1837, 1.0734, 0.8747]], grad_fn=<ReluBackward0>), tensor([[-0.4912,  0.6789,  0.5227,  0.0275]], grad_fn=<AddmmBackward0>), tensor([[-0.4912,  0.6789]], grad_fn=<SplitBackward0>), tensor([[1.3587, 1.0174]], grad_fn=<ClampMinBackward0>), tensor([[-0.4912,  0.6789]], grad_fn=<SplitBackward0>))
 
 
 
@@ -266,8 +266,8 @@ This module can be run exactly like our original module (with a lower overhead):
 
  .. code-block:: none
 
-    Time for TDModule:  680.21 micro-seconds
-    Time for exported module:  379.09 micro-seconds
+    Time for TDModule:  677.82 micro-seconds
+    Time for exported module:  369.31 micro-seconds
 
 
 
@@ -450,7 +450,7 @@ distribution:
 
  .. code-block:: none
 
-    tensor([[-0.2587, -0.1564]], grad_fn=<SplitBackward0>)
+    tensor([[-0.4912,  0.6789]], grad_fn=<SplitBackward0>)
 
 
 
@@ -657,7 +657,7 @@ Next steps and further reading
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.749 seconds)
+   **Total running time of the script:** (0 minutes 1.699 seconds)
 
 
 .. _sphx_glr_download_tutorials_export.py:
diff --git a/main/_sources/tutorials/sg_execution_times.rst.txt b/main/_sources/tutorials/sg_execution_times.rst.txt
index e2af7592f..22e59bc66 100644
--- a/main/_sources/tutorials/sg_execution_times.rst.txt
+++ b/main/_sources/tutorials/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**02:33.910** total execution time for 11 files **from tutorials**:
+**02:29.308** total execution time for 11 files **from tutorials**:
 
 .. container::
 
@@ -33,22 +33,22 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_tutorials_tensorclass_fashion.py` (``tensorclass_fashion.py``)
-     - 01:03.156
+     - 01:00.787
      - 0.0
    * - :ref:`sphx_glr_tutorials_data_fashion.py` (``data_fashion.py``)
-     - 00:57.776
+     - 00:55.623
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_module.py` (``tensordict_module.py``)
-     - 00:18.536
+     - 00:18.477
      - 0.0
    * - :ref:`sphx_glr_tutorials_streamed_tensordict.py` (``streamed_tensordict.py``)
-     - 00:11.020
+     - 00:11.021
      - 0.0
    * - :ref:`sphx_glr_tutorials_export.py` (``export.py``)
-     - 00:01.749
+     - 00:01.699
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensorclass_imagenet.py` (``tensorclass_imagenet.py``)
-     - 00:01.644
+     - 00:01.673
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_keys.py` (``tensordict_keys.py``)
      - 00:00.009
@@ -57,7 +57,7 @@ Computation times
      - 00:00.008
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_slicing.py` (``tensordict_slicing.py``)
-     - 00:00.005
+     - 00:00.004
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_memory.py` (``tensordict_memory.py``)
      - 00:00.004
diff --git a/main/_sources/tutorials/streamed_tensordict.rst.txt b/main/_sources/tutorials/streamed_tensordict.rst.txt
index 8d33459c1..f886ea474 100644
--- a/main/_sources/tutorials/streamed_tensordict.rst.txt
+++ b/main/_sources/tutorials/streamed_tensordict.rst.txt
@@ -444,7 +444,7 @@ Thanks for following along with this tutorial! We hope you've found it helpful a
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 11.020 seconds)
+   **Total running time of the script:** (0 minutes 11.021 seconds)
 
 
 .. _sphx_glr_download_tutorials_streamed_tensordict.py:
diff --git a/main/_sources/tutorials/tensorclass_fashion.rst.txt b/main/_sources/tutorials/tensorclass_fashion.rst.txt
index d331c8d7a..cbb722d1c 100644
--- a/main/_sources/tutorials/tensorclass_fashion.rst.txt
+++ b/main/_sources/tutorials/tensorclass_fashion.rst.txt
@@ -94,10 +94,10 @@ the image (e.g. "Bag", "Sneaker" etc.).
 
  .. code-block:: none
 
-
      0%|          | 0.00/26.4M [00:00<?, ?B/s]
      0%|          | 65.5k/26.4M [00:00<01:13, 358kB/s]
      1%|          | 197k/26.4M [00:00<00:33, 775kB/s] 
      2%|▏         | 459k/26.4M [00:00<00:22, 1.17MB/s]
      5%|▌         | 1.38M/26.4M [00:00<00:07, 3.58MB/s]
     14%|█▎        | 3.57M/26.4M [00:00<00:03, 7.45MB/s]
     26%|██▋       | 6.95M/26.4M [00:00<00:01, 11.7MB/s]
     45%|████▍     | 11.8M/26.4M [00:01<00:00, 17.1MB/s]
     65%|██████▌   | 17.3M/26.4M [00:01<00:00, 21.5MB/s]
     86%|████████▋ | 22.8M/26.4M [00:01<00:00, 24.4MB/s]
    100%|██████████| 26.4M/26.4M [00:01<00:00, 17.0MB/s]
-
      0%|          | 0.00/29.5k [00:00<?, ?B/s]
    100%|██████████| 29.5k/29.5k [00:00<00:00, 327kB/s]
-
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      1%|▏         | 65.5k/4.42M [00:00<00:11, 363kB/s]
      5%|▌         | 229k/4.42M [00:00<00:06, 684kB/s] 
     21%|██        | 918k/4.42M [00:00<00:01, 2.58MB/s]
     44%|████▎     | 1.93M/4.42M [00:00<00:00, 4.12MB/s]
    100%|██████████| 4.42M/4.42M [00:00<00:00, 6.11MB/s]
-
      0%|          | 0.00/5.15k [00:00<?, ?B/s]
    100%|██████████| 5.15k/5.15k [00:00<00:00, 60.3MB/s]
+
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      0%|          | 65.5k/26.4M [00:00<01:12, 362kB/s]
      1%|          | 229k/26.4M [00:00<00:38, 681kB/s] 
      3%|▎         | 852k/26.4M [00:00<00:10, 2.37MB/s]
      7%|▋         | 1.93M/26.4M [00:00<00:05, 4.15MB/s]
     24%|██▍       | 6.42M/26.4M [00:00<00:01, 14.7MB/s]
     38%|███▊      | 9.96M/26.4M [00:00<00:00, 17.3MB/s]
     58%|█████▊    | 15.3M/26.4M [00:01<00:00, 25.7MB/s]
     73%|███████▎  | 19.2M/26.4M [00:01<00:00, 25.1MB/s]
     89%|████████▉ | 23.6M/26.4M [00:01<00:00, 29.6MB/s]
    100%|██████████| 26.4M/26.4M [00:01<00:00, 19.3MB/s]
+
      0%|          | 0.00/29.5k [00:00<?, ?B/s]
    100%|██████████| 29.5k/29.5k [00:00<00:00, 324kB/s]
+
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      1%|▏         | 65.5k/4.42M [00:00<00:12, 358kB/s]
      4%|▍         | 197k/4.42M [00:00<00:05, 758kB/s] 
     11%|█         | 492k/4.42M [00:00<00:03, 1.26MB/s]
     36%|███▋      | 1.61M/4.42M [00:00<00:00, 4.17MB/s]
     87%|████████▋ | 3.83M/4.42M [00:00<00:00, 7.94MB/s]
    100%|██████████| 4.42M/4.42M [00:00<00:00, 6.04MB/s]
+
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    100%|██████████| 5.15k/5.15k [00:00<00:00, 64.1MB/s]
 
 
 
@@ -434,156 +434,156 @@ tensor.
         is_shared=False)
     Epoch 1
     -------------------------
-    loss: 2.307725 [    0/60000]
-    loss: 2.293911 [ 6400/60000]
-    loss: 2.270989 [12800/60000]
-    loss: 2.266152 [19200/60000]
-    loss: 2.249918 [25600/60000]
-    loss: 2.222015 [32000/60000]
-    loss: 2.243351 [38400/60000]
-    loss: 2.207049 [44800/60000]
-    loss: 2.203401 [51200/60000]
-    loss: 2.178670 [57600/60000]
+    loss: 2.296910 [    0/60000]
+    loss: 2.290153 [ 6400/60000]
+    loss: 2.267184 [12800/60000]
+    loss: 2.267986 [19200/60000]
+    loss: 2.241964 [25600/60000]
+    loss: 2.218651 [32000/60000]
+    loss: 2.225486 [38400/60000]
+    loss: 2.190040 [44800/60000]
+    loss: 2.199885 [51200/60000]
+    loss: 2.154611 [57600/60000]
     Test Error: 
-     Accuracy: 42.7%, Avg loss: 2.167375 
+     Accuracy: 42.6%, Avg loss: 2.150428 
 
     Epoch 2
     -------------------------
-    loss: 2.182459 [    0/60000]
-    loss: 2.169180 [ 6400/60000]
-    loss: 2.116737 [12800/60000]
-    loss: 2.130716 [19200/60000]
-    loss: 2.082952 [25600/60000]
-    loss: 2.029855 [32000/60000]
-    loss: 2.066772 [38400/60000]
-    loss: 1.989665 [44800/60000]
-    loss: 1.990337 [51200/60000]
-    loss: 1.924362 [57600/60000]
+    loss: 2.158208 [    0/60000]
+    loss: 2.149079 [ 6400/60000]
+    loss: 2.084302 [12800/60000]
+    loss: 2.108220 [19200/60000]
+    loss: 2.045984 [25600/60000]
+    loss: 1.987296 [32000/60000]
+    loss: 2.015088 [38400/60000]
+    loss: 1.929641 [44800/60000]
+    loss: 1.958254 [51200/60000]
+    loss: 1.866098 [57600/60000]
     Test Error: 
-     Accuracy: 49.6%, Avg loss: 1.917381 
+     Accuracy: 53.5%, Avg loss: 1.865167 
 
     Epoch 3
     -------------------------
-    loss: 1.949793 [    0/60000]
-    loss: 1.919466 [ 6400/60000]
-    loss: 1.812307 [12800/60000]
-    loss: 1.849123 [19200/60000]
-    loss: 1.738953 [25600/60000]
-    loss: 1.688647 [32000/60000]
-    loss: 1.719033 [38400/60000]
-    loss: 1.611804 [44800/60000]
-    loss: 1.635073 [51200/60000]
-    loss: 1.534028 [57600/60000]
+    loss: 1.897520 [    0/60000]
+    loss: 1.866758 [ 6400/60000]
+    loss: 1.742258 [12800/60000]
+    loss: 1.795041 [19200/60000]
+    loss: 1.682887 [25600/60000]
+    loss: 1.626283 [32000/60000]
+    loss: 1.653406 [38400/60000]
+    loss: 1.553119 [44800/60000]
+    loss: 1.593457 [51200/60000]
+    loss: 1.478362 [57600/60000]
     Test Error: 
-     Accuracy: 58.3%, Avg loss: 1.543767 
+     Accuracy: 61.4%, Avg loss: 1.497962 
 
     Epoch 4
     -------------------------
-    loss: 1.605731 [    0/60000]
-    loss: 1.569755 [ 6400/60000]
-    loss: 1.430758 [12800/60000]
-    loss: 1.500959 [19200/60000]
-    loss: 1.378377 [25600/60000]
-    loss: 1.372691 [32000/60000]
-    loss: 1.397103 [38400/60000]
-    loss: 1.307562 [44800/60000]
-    loss: 1.344959 [51200/60000]
-    loss: 1.256637 [57600/60000]
+    loss: 1.561284 [    0/60000]
+    loss: 1.529328 [ 6400/60000]
+    loss: 1.378711 [12800/60000]
+    loss: 1.458048 [19200/60000]
+    loss: 1.342770 [25600/60000]
+    loss: 1.329875 [32000/60000]
+    loss: 1.346497 [38400/60000]
+    loss: 1.273600 [44800/60000]
+    loss: 1.314018 [51200/60000]
+    loss: 1.209880 [57600/60000]
     Test Error: 
-     Accuracy: 62.9%, Avg loss: 1.269797 
+     Accuracy: 63.7%, Avg loss: 1.237870 
 
     Epoch 5
     -------------------------
-    loss: 1.341925 [    0/60000]
-    loss: 1.322621 [ 6400/60000]
-    loss: 1.170070 [12800/60000]
-    loss: 1.274381 [19200/60000]
-    loss: 1.146528 [25600/60000]
-    loss: 1.172810 [32000/60000]
-    loss: 1.204830 [38400/60000]
-    loss: 1.124031 [44800/60000]
-    loss: 1.165264 [51200/60000]
-    loss: 1.097989 [57600/60000]
+    loss: 1.307323 [    0/60000]
+    loss: 1.295308 [ 6400/60000]
+    loss: 1.129464 [12800/60000]
+    loss: 1.241139 [19200/60000]
+    loss: 1.117206 [25600/60000]
+    loss: 1.134774 [32000/60000]
+    loss: 1.158498 [38400/60000]
+    loss: 1.098527 [44800/60000]
+    loss: 1.139965 [51200/60000]
+    loss: 1.053264 [57600/60000]
     Test Error: 
-     Accuracy: 65.0%, Avg loss: 1.102238 
+     Accuracy: 65.0%, Avg loss: 1.075727 
 
-    Tensorclass training done! time:  8.6376 s
+    Tensorclass training done! time:  8.7627 s
     Epoch 1
     -------------------------
-    loss: 2.308171 [    0/60000]
-    loss: 2.286401 [ 6400/60000]
-    loss: 2.275298 [12800/60000]
-    loss: 2.268542 [19200/60000]
-    loss: 2.239279 [25600/60000]
-    loss: 2.218736 [32000/60000]
-    loss: 2.222399 [38400/60000]
-    loss: 2.188224 [44800/60000]
-    loss: 2.189977 [51200/60000]
-    loss: 2.148144 [57600/60000]
+    loss: 2.323289 [    0/60000]
+    loss: 2.302869 [ 6400/60000]
+    loss: 2.289611 [12800/60000]
+    loss: 2.275158 [19200/60000]
+    loss: 2.250096 [25600/60000]
+    loss: 2.233657 [32000/60000]
+    loss: 2.245745 [38400/60000]
+    loss: 2.217353 [44800/60000]
+    loss: 2.219387 [51200/60000]
+    loss: 2.178802 [57600/60000]
     Test Error: 
-     Accuracy: 49.7%, Avg loss: 2.143384 
+     Accuracy: 36.7%, Avg loss: 2.174884 
 
     Epoch 2
     -------------------------
-    loss: 2.157794 [    0/60000]
-    loss: 2.140212 [ 6400/60000]
-    loss: 2.083794 [12800/60000]
-    loss: 2.101565 [19200/60000]
-    loss: 2.035136 [25600/60000]
-    loss: 1.974287 [32000/60000]
-    loss: 2.003978 [38400/60000]
-    loss: 1.920177 [44800/60000]
-    loss: 1.925001 [51200/60000]
-    loss: 1.841307 [57600/60000]
+    loss: 2.194728 [    0/60000]
+    loss: 2.183252 [ 6400/60000]
+    loss: 2.136127 [12800/60000]
+    loss: 2.144600 [19200/60000]
+    loss: 2.085006 [25600/60000]
+    loss: 2.040224 [32000/60000]
+    loss: 2.076125 [38400/60000]
+    loss: 2.007898 [44800/60000]
+    loss: 2.011441 [51200/60000]
+    loss: 1.941871 [57600/60000]
     Test Error: 
-     Accuracy: 59.0%, Avg loss: 1.844654 
+     Accuracy: 52.2%, Avg loss: 1.933784 
 
     Epoch 3
     -------------------------
-    loss: 1.880996 [    0/60000]
-    loss: 1.844965 [ 6400/60000]
-    loss: 1.730476 [12800/60000]
-    loss: 1.775033 [19200/60000]
-    loss: 1.649532 [25600/60000]
-    loss: 1.606074 [32000/60000]
-    loss: 1.628593 [38400/60000]
-    loss: 1.536964 [44800/60000]
-    loss: 1.557712 [51200/60000]
-    loss: 1.443908 [57600/60000]
+    loss: 1.969968 [    0/60000]
+    loss: 1.941942 [ 6400/60000]
+    loss: 1.840044 [12800/60000]
+    loss: 1.873434 [19200/60000]
+    loss: 1.745236 [25600/60000]
+    loss: 1.710266 [32000/60000]
+    loss: 1.741888 [38400/60000]
+    loss: 1.647687 [44800/60000]
+    loss: 1.656758 [51200/60000]
+    loss: 1.560415 [57600/60000]
     Test Error: 
-     Accuracy: 61.1%, Avg loss: 1.473452 
+     Accuracy: 60.3%, Avg loss: 1.564881 
 
     Epoch 4
     -------------------------
-    loss: 1.543633 [    0/60000]
-    loss: 1.506534 [ 6400/60000]
-    loss: 1.365190 [12800/60000]
-    loss: 1.443080 [19200/60000]
-    loss: 1.310368 [25600/60000]
-    loss: 1.313909 [32000/60000]
-    loss: 1.327447 [38400/60000]
-    loss: 1.263996 [44800/60000]
-    loss: 1.290329 [51200/60000]
-    loss: 1.189054 [57600/60000]
+    loss: 1.630355 [    0/60000]
+    loss: 1.595403 [ 6400/60000]
+    loss: 1.451340 [12800/60000]
+    loss: 1.516528 [19200/60000]
+    loss: 1.379949 [25600/60000]
+    loss: 1.382664 [32000/60000]
+    loss: 1.401636 [38400/60000]
+    loss: 1.334157 [44800/60000]
+    loss: 1.352804 [51200/60000]
+    loss: 1.255776 [57600/60000]
     Test Error: 
-     Accuracy: 63.1%, Avg loss: 1.221911 
+     Accuracy: 63.3%, Avg loss: 1.275988 
 
     Epoch 5
     -------------------------
-    loss: 1.300781 [    0/60000]
-    loss: 1.280785 [ 6400/60000]
-    loss: 1.122259 [12800/60000]
-    loss: 1.236478 [19200/60000]
-    loss: 1.098779 [25600/60000]
-    loss: 1.127348 [32000/60000]
-    loss: 1.148480 [38400/60000]
-    loss: 1.097973 [44800/60000]
-    loss: 1.125805 [51200/60000]
-    loss: 1.042096 [57600/60000]
+    loss: 1.354131 [    0/60000]
+    loss: 1.337473 [ 6400/60000]
+    loss: 1.176684 [12800/60000]
+    loss: 1.273267 [19200/60000]
+    loss: 1.142443 [25600/60000]
+    loss: 1.167726 [32000/60000]
+    loss: 1.190811 [38400/60000]
+    loss: 1.141034 [44800/60000]
+    loss: 1.166486 [51200/60000]
+    loss: 1.081528 [57600/60000]
     Test Error: 
-     Accuracy: 64.6%, Avg loss: 1.067900 
+     Accuracy: 64.8%, Avg loss: 1.100232 
 
-    Training done! time:  36.0137 s
+    Training done! time:  34.3544 s
 
 
 
@@ -591,7 +591,7 @@ tensor.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 3.156 seconds)
+   **Total running time of the script:** (1 minutes 0.787 seconds)
 
 
 .. _sphx_glr_download_tutorials_tensorclass_fashion.py:
diff --git a/main/_sources/tutorials/tensorclass_imagenet.rst.txt b/main/_sources/tutorials/tensorclass_imagenet.rst.txt
index 38efe70d1..e78abeacd 100644
--- a/main/_sources/tutorials/tensorclass_imagenet.rst.txt
+++ b/main/_sources/tutorials/tensorclass_imagenet.rst.txt
@@ -418,7 +418,7 @@ 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, 234.58it/s]ImageNetData(
+
      0%|          | 0/244 [00:00<?, ?it/s]
     26%|██▌       | 64/244 [00:00<00:00, 231.24it/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]),
@@ -466,8 +466,8 @@ 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, 723.10it/s]
-
      0%|          | 0/153 [00:00<?, ?it/s]
     42%|████▏     | 64/153 [00:00<00:00, 184.36it/s]ImageNetData(
+
    100%|██████████| 244/244 [00:00<00:00, 711.98it/s]
+
      0%|          | 0/153 [00:00<?, ?it/s]
     42%|████▏     | 64/153 [00:00<00:00, 186.72it/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]),
@@ -503,11 +503,11 @@ 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, 326.50it/s]
-    One iteration over dataloader done! Rate: 878.3548 fps, time:  0.2505s
-    One iteration over tensorclass dataloader done! Rate: 1869.2406 fps, time:  0.1177s
-    One iteration over val data done! Rate: 547.5260 fps, time:  0.2356s
-    One iteration over tensorclass val data done! Rate: 19560.5805 fps, time:  0.0066s
+
    100%|██████████| 153/153 [00:00<00:00, 326.40it/s]
+    One iteration over dataloader done! Rate: 879.3836 fps, time:  0.2502s
+    One iteration over tensorclass dataloader done! Rate: 1681.0041 fps, time:  0.1309s
+    One iteration over val data done! Rate: 539.4555 fps, time:  0.2391s
+    One iteration over tensorclass val data done! Rate: 18891.9419 fps, time:  0.0068s
 
 
 
@@ -515,7 +515,7 @@ 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 1.644 seconds)
+   **Total running time of the script:** (0 minutes 1.673 seconds)
 
 
 .. _sphx_glr_download_tutorials_tensorclass_imagenet.py:
diff --git a/main/_sources/tutorials/tensordict_module.rst.txt b/main/_sources/tutorials/tensordict_module.rst.txt
index 78d5802ba..1622e6d39 100644
--- a/main/_sources/tutorials/tensordict_module.rst.txt
+++ b/main/_sources/tutorials/tensordict_module.rst.txt
@@ -408,13 +408,13 @@ executed, as they need to read and write from a tensordict. However, we can grea
  .. code-block:: none
 
     Without compile
-    Regular:  220.6026 us
-    TDM:  277.7298 us
-    Sequential:  481.1476 us
+    Regular:  218.3131 us
+    TDM:  275.8440 us
+    Sequential:  479.7458 us
     Compiled versions
-    Compiled regular:  342.2225 us
-    Compiled TDM:  350.3220 us
-    Compiled sequential:  363.3525 us
+    Compiled regular:  303.8840 us
+    Compiled TDM:  343.8845 us
+    Compiled sequential:  356.0950 us
 
 
 
@@ -546,7 +546,7 @@ In the next tutorial, we will be seeing how ``torch.export`` can be used to isol
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 18.536 seconds)
+   **Total running time of the script:** (0 minutes 18.477 seconds)
 
 
 .. _sphx_glr_download_tutorials_tensordict_module.py:
diff --git a/main/_sources/tutorials/tensordict_shapes.rst.txt b/main/_sources/tutorials/tensordict_shapes.rst.txt
index 30830803c..3688fe5e1 100644
--- a/main/_sources/tutorials/tensordict_shapes.rst.txt
+++ b/main/_sources/tutorials/tensordict_shapes.rst.txt
@@ -561,19 +561,19 @@ like :func:`torch.gather`.
  .. code-block:: none
 
     index:
-     tensor([[2, 2, 0, 1],
-            [1, 0, 3, 2],
-            [2, 0, 1, 2]])
+     tensor([[2, 3, 2, 2],
+            [3, 0, 0, 2],
+            [3, 2, 2, 3]])
 
     tensordict['a']:
-     tensor([[0.7156, 0.9267, 0.7474, 0.7576],
-            [0.9559, 0.2976, 0.4468, 0.1741],
-            [0.6645, 0.0389, 0.9629, 0.7605]])
+     tensor([[0.2576, 0.1782, 0.4756, 0.7252],
+            [0.8398, 0.6909, 0.8339, 0.9424],
+            [0.8191, 0.7667, 0.1551, 0.6584]])
 
     gathered_tensordict['a']:
-     tensor([[0.7474, 0.7474, 0.7156, 0.9267],
-            [0.2976, 0.9559, 0.1741, 0.4468],
-            [0.9629, 0.6645, 0.0389, 0.9629]])
+     tensor([[0.4756, 0.7252, 0.4756, 0.4756],
+            [0.9424, 0.8398, 0.8398, 0.8339],
+            [0.6584, 0.1551, 0.1551, 0.6584]])
 
 
 
diff --git a/main/_sources/tutorials/tensordict_slicing.rst.txt b/main/_sources/tutorials/tensordict_slicing.rst.txt
index e16f24893..990917b6c 100644
--- a/main/_sources/tutorials/tensordict_slicing.rst.txt
+++ b/main/_sources/tutorials/tensordict_slicing.rst.txt
@@ -257,7 +257,7 @@ We mask :class:`TensorDict` as we mask tensors.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.005 seconds)
+   **Total running time of the script:** (0 minutes 0.004 seconds)
 
 
 .. _sphx_glr_download_tutorials_tensordict_slicing.py:
diff --git a/main/searchindex.js b/main/searchindex.js
index d23ea2272..6369af8b4 100644
--- a/main/searchindex.js
+++ b/main/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.NonTensorStack", "reference/generated/tensordict.PersistentTensorDict", "reference/generated/tensordict.TensorClass", "reference/generated/tensordict.TensorDict", "reference/generated/tensordict.TensorDictBase", "reference/generated/tensordict.TensorDictParams", "reference/generated/tensordict.cat", "reference/generated/tensordict.dense_stack_tds", "reference/generated/tensordict.from_any", "reference/generated/tensordict.from_consolidated", "reference/generated/tensordict.from_dataclass", "reference/generated/tensordict.from_dict", "reference/generated/tensordict.from_h5", "reference/generated/tensordict.from_module", "reference/generated/tensordict.from_modules", "reference/generated/tensordict.from_namedtuple", 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"reference/generated/tensordict.nn.TensorDictModuleBase", "reference/generated/tensordict.nn.TensorDictModuleWrapper", "reference/generated/tensordict.nn.TensorDictSequential", "reference/generated/tensordict.nn.WrapModule", "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.NormalParamExtractor", "reference/generated/tensordict.nn.distributions.OneHotCategorical", "reference/generated/tensordict.nn.distributions.TruncatedNormal", "reference/generated/tensordict.nn.inv_softplus", "reference/generated/tensordict.nn.make_tensordict", "reference/generated/tensordict.nn.set_interaction_type", "reference/generated/tensordict.nn.set_skip_existing", "reference/generated/tensordict.nn.skip_existing", "reference/generated/tensordict.pad", "reference/generated/tensordict.pad_sequence", "reference/generated/tensordict.parse_tensor_dict_string", "reference/generated/tensordict.save", "reference/generated/tensordict.set_lazy_legacy", "reference/generated/tensordict.stack", "reference/generated/tensordict.tensorclass", "reference/generated/tensordict.utils.expand_as_right", "reference/generated/tensordict.utils.expand_right", "reference/generated/tensordict.utils.isin", "reference/generated/tensordict.utils.remove_duplicates", "reference/index", "reference/nn", "reference/tensorclass", "reference/tensordict", "saving", "sg_execution_times", "tutorials/data_fashion", "tutorials/export", "tutorials/index", "tutorials/sg_execution_times", "tutorials/streamed_tensordict", "tutorials/tensorclass_fashion", "tutorials/tensorclass_imagenet", "tutorials/tensordict_keys", "tutorials/tensordict_memory", "tutorials/tensordict_module", "tutorials/tensordict_preallocation", "tutorials/tensordict_shapes", "tutorials/tensordict_slicing"], "filenames": ["distributed.rst", "fx.rst", "index.rst", "overview.rst", "reference/generated/tensordict.LazyStackedTensorDict.rst", "reference/generated/tensordict.MemoryMappedTensor.rst", "reference/generated/tensordict.NonTensorData.rst", "reference/generated/tensordict.NonTensorStack.rst", "reference/generated/tensordict.PersistentTensorDict.rst", "reference/generated/tensordict.TensorClass.rst", "reference/generated/tensordict.TensorDict.rst", "reference/generated/tensordict.TensorDictBase.rst", "reference/generated/tensordict.TensorDictParams.rst", "reference/generated/tensordict.cat.rst", "reference/generated/tensordict.dense_stack_tds.rst", "reference/generated/tensordict.from_any.rst", "reference/generated/tensordict.from_consolidated.rst", "reference/generated/tensordict.from_dataclass.rst", "reference/generated/tensordict.from_dict.rst", "reference/generated/tensordict.from_h5.rst", "reference/generated/tensordict.from_module.rst", "reference/generated/tensordict.from_modules.rst", "reference/generated/tensordict.from_namedtuple.rst", "reference/generated/tensordict.from_pytree.rst", "reference/generated/tensordict.from_struct_array.rst", "reference/generated/tensordict.from_tuple.rst", "reference/generated/tensordict.fromkeys.rst", "reference/generated/tensordict.get_defaults_to_none.rst", "reference/generated/tensordict.is_batchedtensor.rst", "reference/generated/tensordict.is_tensor_collection.rst", "reference/generated/tensordict.lazy_legacy.rst", "reference/generated/tensordict.lazy_stack.rst", "reference/generated/tensordict.load.rst", "reference/generated/tensordict.load_memmap.rst", "reference/generated/tensordict.make_tensordict.rst", "reference/generated/tensordict.maybe_dense_stack.rst", "reference/generated/tensordict.memmap.rst", "reference/generated/tensordict.merge_tensordicts.rst", "reference/generated/tensordict.nn.CudaGraphModule.rst", "reference/generated/tensordict.nn.EnsembleModule.rst", "reference/generated/tensordict.nn.ProbabilisticTensorDictModule.rst", "reference/generated/tensordict.nn.TensorDictModule.rst", "reference/generated/tensordict.nn.TensorDictModuleBase.rst", "reference/generated/tensordict.nn.TensorDictModuleWrapper.rst", "reference/generated/tensordict.nn.TensorDictSequential.rst", "reference/generated/tensordict.nn.WrapModule.rst", "reference/generated/tensordict.nn.biased_softplus.rst", "reference/generated/tensordict.nn.dispatch.rst", "reference/generated/tensordict.nn.distributions.AddStateIndependentNormalScale.rst", "reference/generated/tensordict.nn.distributions.CompositeDistribution.rst", "reference/generated/tensordict.nn.distributions.Delta.rst", "reference/generated/tensordict.nn.distributions.NormalParamExtractor.rst", "reference/generated/tensordict.nn.distributions.OneHotCategorical.rst", "reference/generated/tensordict.nn.distributions.TruncatedNormal.rst", "reference/generated/tensordict.nn.inv_softplus.rst", "reference/generated/tensordict.nn.make_tensordict.rst", "reference/generated/tensordict.nn.set_interaction_type.rst", "reference/generated/tensordict.nn.set_skip_existing.rst", "reference/generated/tensordict.nn.skip_existing.rst", "reference/generated/tensordict.pad.rst", "reference/generated/tensordict.pad_sequence.rst", "reference/generated/tensordict.parse_tensor_dict_string.rst", "reference/generated/tensordict.save.rst", "reference/generated/tensordict.set_lazy_legacy.rst", "reference/generated/tensordict.stack.rst", "reference/generated/tensordict.tensorclass.rst", "reference/generated/tensordict.utils.expand_as_right.rst", "reference/generated/tensordict.utils.expand_right.rst", "reference/generated/tensordict.utils.isin.rst", "reference/generated/tensordict.utils.remove_duplicates.rst", "reference/index.rst", "reference/nn.rst", "reference/tensorclass.rst", "reference/tensordict.rst", "saving.rst", "sg_execution_times.rst", "tutorials/data_fashion.rst", "tutorials/export.rst", "tutorials/index.rst", "tutorials/sg_execution_times.rst", "tutorials/streamed_tensordict.rst", "tutorials/tensorclass_fashion.rst", "tutorials/tensorclass_imagenet.rst", "tutorials/tensordict_keys.rst", "tutorials/tensordict_memory.rst", "tutorials/tensordict_module.rst", "tutorials/tensordict_preallocation.rst", "tutorials/tensordict_shapes.rst", "tutorials/tensordict_slicing.rst"], "titles": ["TensorDict in distributed settings", "Tracing TensorDictModule", "Welcome to the TensorDict Documentation!", "Overview", "LazyStackedTensorDict", "MemoryMappedTensor", "NonTensorData", "NonTensorStack", "PersistentTensorDict", "TensorClass", "TensorDict", "TensorDictBase", "TensorDictParams", "cat", "dense_stack_tds", "from_any", "from_consolidated", "from_dataclass", "from_dict", "from_h5", "from_module", "from_modules", "from_namedtuple", 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diff --git a/main/sg_execution_times.html b/main/sg_execution_times.html
index 569d5c6ea..8fd15b059 100644
--- a/main/sg_execution_times.html
+++ b/main/sg_execution_times.html
@@ -350,7 +350,7 @@
               
   <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>02:33.910</strong> total execution time for 11 files <strong>from all galleries</strong>:</p>
+<p><strong>02:29.308</strong> total execution time for 11 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" />
@@ -372,27 +372,27 @@
 </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>01:03.156</p></td>
+<td><p>01:00.787</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>00:57.776</p></td>
+<td><p>00:55.623</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>00:18.536</p></td>
+<td><p>00:18.477</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="tutorials/streamed_tensordict.html#sphx-glr-tutorials-streamed-tensordict-py"><span class="std std-ref">Building tensordicts from streams</span></a> (<code class="docutils literal notranslate"><span class="pre">reference/generated/tutorials/streamed_tensordict.py</span></code>)</p></td>
-<td><p>00:11.020</p></td>
+<td><p>00:11.021</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="tutorials/export.html#sphx-glr-tutorials-export-py"><span class="std std-ref">Exporting tensordict modules</span></a> (<code class="docutils literal notranslate"><span class="pre">reference/generated/tutorials/export.py</span></code>)</p></td>
-<td><p>00:01.749</p></td>
+<td><p>00:01.699</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>00:01.644</p></td>
+<td><p>00:01.673</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><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>
@@ -404,7 +404,7 @@
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="tutorials/tensordict_slicing.html#sphx-glr-tutorials-tensordict-slicing-py"><span class="std std-ref">Slicing, Indexing, and Masking</span></a> (<code class="docutils literal notranslate"><span class="pre">reference/generated/tutorials/tensordict_slicing.py</span></code>)</p></td>
-<td><p>00:00.005</p></td>
+<td><p>00:00.004</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><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>
diff --git a/main/tutorials/data_fashion.html b/main/tutorials/data_fashion.html
index 9324bfe7d..3d1e7c94a 100644
--- a/main/tutorials/data_fashion.html
+++ b/main/tutorials/data_fashion.html
@@ -630,159 +630,159 @@ <h2>Optimizing the parameters<a class="headerlink" href="#optimizing-the-paramet
     is_shared=False)
 Epoch 1
 -------------------------
-loss: 2.295148 [    0/60000]
-loss: 2.288687 [ 6400/60000]
-loss: 2.264276 [12800/60000]
-loss: 2.257121 [19200/60000]
-loss: 2.249283 [25600/60000]
-loss: 2.211193 [32000/60000]
-loss: 2.216204 [38400/60000]
-loss: 2.180449 [44800/60000]
-loss: 2.179368 [51200/60000]
-loss: 2.146407 [57600/60000]
+loss: 2.297034 [    0/60000]
+loss: 2.293723 [ 6400/60000]
+loss: 2.269874 [12800/60000]
+loss: 2.265041 [19200/60000]
+loss: 2.247218 [25600/60000]
+loss: 2.226938 [32000/60000]
+loss: 2.226363 [38400/60000]
+loss: 2.194768 [44800/60000]
+loss: 2.195367 [51200/60000]
+loss: 2.161224 [57600/60000]
 Test Error:
- Accuracy: 48.0%, Avg loss: 2.141438
+ Accuracy: 43.4%, Avg loss: 2.156612
 
 Epoch 2
 -------------------------
-loss: 2.148195 [    0/60000]
-loss: 2.141373 [ 6400/60000]
-loss: 2.079053 [12800/60000]
-loss: 2.093180 [19200/60000]
-loss: 2.041999 [25600/60000]
-loss: 1.976888 [32000/60000]
-loss: 1.994016 [38400/60000]
-loss: 1.914325 [44800/60000]
-loss: 1.923628 [51200/60000]
-loss: 1.836888 [57600/60000]
+loss: 2.160449 [    0/60000]
+loss: 2.160634 [ 6400/60000]
+loss: 2.090734 [12800/60000]
+loss: 2.112868 [19200/60000]
+loss: 2.055511 [25600/60000]
+loss: 2.001153 [32000/60000]
+loss: 2.027912 [38400/60000]
+loss: 1.945694 [44800/60000]
+loss: 1.961530 [51200/60000]
+loss: 1.883855 [57600/60000]
 Test Error:
- Accuracy: 57.2%, Avg loss: 1.842130
+ Accuracy: 53.6%, Avg loss: 1.881469
 
 Epoch 3
 -------------------------
-loss: 1.879584 [    0/60000]
-loss: 1.845570 [ 6400/60000]
-loss: 1.727267 [12800/60000]
-loss: 1.765438 [19200/60000]
-loss: 1.662461 [25600/60000]
-loss: 1.614136 [32000/60000]
-loss: 1.628378 [38400/60000]
-loss: 1.535969 [44800/60000]
-loss: 1.567617 [51200/60000]
-loss: 1.456037 [57600/60000]
+loss: 1.905408 [    0/60000]
+loss: 1.890264 [ 6400/60000]
+loss: 1.755859 [12800/60000]
+loss: 1.807360 [19200/60000]
+loss: 1.687582 [25600/60000]
+loss: 1.644098 [32000/60000]
+loss: 1.667204 [38400/60000]
+loss: 1.568138 [44800/60000]
+loss: 1.598023 [51200/60000]
+loss: 1.490839 [57600/60000]
 Test Error:
- Accuracy: 60.1%, Avg loss: 1.480593
+ Accuracy: 61.1%, Avg loss: 1.508179
 
 Epoch 4
 -------------------------
-loss: 1.552394 [    0/60000]
-loss: 1.519605 [ 6400/60000]
-loss: 1.368929 [12800/60000]
-loss: 1.442735 [19200/60000]
-loss: 1.331060 [25600/60000]
-loss: 1.328798 [32000/60000]
-loss: 1.342121 [38400/60000]
-loss: 1.268073 [44800/60000]
-loss: 1.305307 [51200/60000]
-loss: 1.209298 [57600/60000]
+loss: 1.564770 [    0/60000]
+loss: 1.547288 [ 6400/60000]
+loss: 1.384363 [12800/60000]
+loss: 1.462441 [19200/60000]
+loss: 1.338688 [25600/60000]
+loss: 1.338367 [32000/60000]
+loss: 1.350146 [38400/60000]
+loss: 1.280336 [44800/60000]
+loss: 1.319443 [51200/60000]
+loss: 1.217629 [57600/60000]
 Test Error:
- Accuracy: 62.6%, Avg loss: 1.234078
+ Accuracy: 64.0%, Avg loss: 1.243834
 
 Epoch 5
 -------------------------
-loss: 1.312373 [    0/60000]
-loss: 1.298895 [ 6400/60000]
-loss: 1.127474 [12800/60000]
-loss: 1.239774 [19200/60000]
-loss: 1.117170 [25600/60000]
-loss: 1.143492 [32000/60000]
-loss: 1.166954 [38400/60000]
-loss: 1.100850 [44800/60000]
-loss: 1.139860 [51200/60000]
-loss: 1.060362 [57600/60000]
+loss: 1.309603 [    0/60000]
+loss: 1.309187 [ 6400/60000]
+loss: 1.132445 [12800/60000]
+loss: 1.242703 [19200/60000]
+loss: 1.116270 [25600/60000]
+loss: 1.142112 [32000/60000]
+loss: 1.159446 [38400/60000]
+loss: 1.102855 [44800/60000]
+loss: 1.148942 [51200/60000]
+loss: 1.061529 [57600/60000]
 Test Error:
- Accuracy: 64.5%, Avg loss: 1.079705
+ Accuracy: 65.6%, Avg loss: 1.082501
 
-TensorDict training done! time:  8.6095 s
+TensorDict training done! time:  8.6850 s
 Epoch 1
 -------------------------
-loss: 2.295774 [    0/60000]
-loss: 2.289083 [ 6400/60000]
-loss: 2.267943 [12800/60000]
-loss: 2.271904 [19200/60000]
-loss: 2.251286 [25600/60000]
-loss: 2.229369 [32000/60000]
-loss: 2.237860 [38400/60000]
-loss: 2.203740 [44800/60000]
-loss: 2.204681 [51200/60000]
-loss: 2.181303 [57600/60000]
+loss: 2.303568 [    0/60000]
+loss: 2.292461 [ 6400/60000]
+loss: 2.262461 [12800/60000]
+loss: 2.258972 [19200/60000]
+loss: 2.241117 [25600/60000]
+loss: 2.201943 [32000/60000]
+loss: 2.217089 [38400/60000]
+loss: 2.180319 [44800/60000]
+loss: 2.187558 [51200/60000]
+loss: 2.131742 [57600/60000]
 Test Error:
- Accuracy: 47.0%, Avg loss: 2.171147
+ Accuracy: 31.7%, Avg loss: 2.140259
 
 Epoch 2
 -------------------------
-loss: 2.176008 [    0/60000]
-loss: 2.166186 [ 6400/60000]
-loss: 2.107857 [12800/60000]
-loss: 2.134267 [19200/60000]
-loss: 2.080149 [25600/60000]
-loss: 2.030640 [32000/60000]
-loss: 2.056546 [38400/60000]
-loss: 1.976407 [44800/60000]
-loss: 1.991985 [51200/60000]
-loss: 1.931681 [57600/60000]
+loss: 2.164973 [    0/60000]
+loss: 2.150137 [ 6400/60000]
+loss: 2.077849 [12800/60000]
+loss: 2.093443 [19200/60000]
+loss: 2.038481 [25600/60000]
+loss: 1.973073 [32000/60000]
+loss: 1.998879 [38400/60000]
+loss: 1.921673 [44800/60000]
+loss: 1.933168 [51200/60000]
+loss: 1.831749 [57600/60000]
 Test Error:
- Accuracy: 53.7%, Avg loss: 1.918879
+ Accuracy: 48.7%, Avg loss: 1.849003
 
 Epoch 3
 -------------------------
-loss: 1.944126 [    0/60000]
-loss: 1.917355 [ 6400/60000]
-loss: 1.795971 [12800/60000]
-loss: 1.848915 [19200/60000]
-loss: 1.745302 [25600/60000]
-loss: 1.685439 [32000/60000]
-loss: 1.715304 [38400/60000]
-loss: 1.603045 [44800/60000]
-loss: 1.642661 [51200/60000]
-loss: 1.552419 [57600/60000]
+loss: 1.898297 [    0/60000]
+loss: 1.863893 [ 6400/60000]
+loss: 1.733816 [12800/60000]
+loss: 1.774636 [19200/60000]
+loss: 1.670297 [25600/60000]
+loss: 1.623713 [32000/60000]
+loss: 1.639363 [38400/60000]
+loss: 1.557734 [44800/60000]
+loss: 1.581697 [51200/60000]
+loss: 1.458045 [57600/60000]
 Test Error:
- Accuracy: 60.2%, Avg loss: 1.555570
+ Accuracy: 59.3%, Avg loss: 1.494883
 
 Epoch 4
 -------------------------
-loss: 1.612758 [    0/60000]
-loss: 1.585966 [ 6400/60000]
-loss: 1.427406 [12800/60000]
-loss: 1.507616 [19200/60000]
-loss: 1.402105 [25600/60000]
-loss: 1.379785 [32000/60000]
-loss: 1.396852 [38400/60000]
-loss: 1.307169 [44800/60000]
-loss: 1.354686 [51200/60000]
-loss: 1.271429 [57600/60000]
+loss: 1.569354 [    0/60000]
+loss: 1.539536 [ 6400/60000]
+loss: 1.385659 [12800/60000]
+loss: 1.456576 [19200/60000]
+loss: 1.343820 [25600/60000]
+loss: 1.342496 [32000/60000]
+loss: 1.348758 [38400/60000]
+loss: 1.292623 [44800/60000]
+loss: 1.323011 [51200/60000]
+loss: 1.209712 [57600/60000]
 Test Error:
- Accuracy: 62.9%, Avg loss: 1.284187
+ Accuracy: 63.5%, Avg loss: 1.249484
 
 Epoch 5
 -------------------------
-loss: 1.352535 [    0/60000]
-loss: 1.344217 [ 6400/60000]
-loss: 1.170571 [12800/60000]
-loss: 1.279448 [19200/60000]
-loss: 1.167800 [25600/60000]
-loss: 1.178811 [32000/60000]
-loss: 1.196409 [38400/60000]
-loss: 1.121528 [44800/60000]
-loss: 1.170856 [51200/60000]
-loss: 1.104489 [57600/60000]
+loss: 1.326457 [    0/60000]
+loss: 1.315519 [ 6400/60000]
+loss: 1.147514 [12800/60000]
+loss: 1.252299 [19200/60000]
+loss: 1.127516 [25600/60000]
+loss: 1.154889 [32000/60000]
+loss: 1.166634 [38400/60000]
+loss: 1.122377 [44800/60000]
+loss: 1.159742 [51200/60000]
+loss: 1.059098 [57600/60000]
 Test Error:
- Accuracy: 64.3%, Avg loss: 1.112184
+ Accuracy: 65.0%, Avg loss: 1.092335
 
-Training done! time:  36.0000 s
+Training done! time:  34.4519 s
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 57.776 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 55.623 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/main/tutorials/export.html b/main/tutorials/export.html
index 0a1bfb31a..f9872f157 100644
--- a/main/tutorials/export.html
+++ b/main/tutorials/export.html
@@ -430,7 +430,7 @@ <h3>Designing the model<a class="headerlink" href="#designing-the-model" title="
 <span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="o">=</span><a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">))</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(tensor([[0., 0., 0., 0.]], grad_fn=&lt;ReluBackward0&gt;), tensor([[-0.2587, -0.1564,  0.1451,  0.2811]], grad_fn=&lt;AddmmBackward0&gt;), tensor([[-0.2587, -0.1564]], grad_fn=&lt;SplitBackward0&gt;), tensor([[1.0936, 1.1856]], grad_fn=&lt;ClampMinBackward0&gt;), tensor([[-0.2587, -0.1564]], grad_fn=&lt;SplitBackward0&gt;))
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(tensor([[0.0000, 0.1837, 1.0734, 0.8747]], grad_fn=&lt;ReluBackward0&gt;), tensor([[-0.4912,  0.6789,  0.5227,  0.0275]], grad_fn=&lt;AddmmBackward0&gt;), tensor([[-0.4912,  0.6789]], grad_fn=&lt;SplitBackward0&gt;), tensor([[1.3587, 1.0174]], grad_fn=&lt;ClampMinBackward0&gt;), tensor([[-0.4912,  0.6789]], grad_fn=&lt;SplitBackward0&gt;))
 </pre></div>
 </div>
 <p>As expected, running the model with a tensor input returns as many tensors as the module’s output keys! For large
@@ -504,8 +504,8 @@ <h3>Using <code class="docutils literal notranslate"><span class="pre">torch.exp
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Time for exported module: </span><span class="si">{</span><span class="p">(</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><span class="w"> </span><span class="o">-</span><span class="w"> </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">t0</span></a><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mf">1e6</span><span class="si">:</span><span class="s2"> 4.2f</span><span class="si">}</span><span class="s2"> micro-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>Time for TDModule:  680.21 micro-seconds
-Time for exported module:  379.09 micro-seconds
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Time for TDModule:  677.82 micro-seconds
+Time for exported module:  369.31 micro-seconds
 </pre></div>
 </div>
 <p>and the FX graph:</p>
@@ -636,7 +636,7 @@ <h3>Selecting the outputs<a class="headerlink" href="#selecting-the-outputs" tit
 <span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="o">=</span><a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">))</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[-0.2587, -0.1564]], grad_fn=&lt;SplitBackward0&gt;)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[-0.4912,  0.6789]], grad_fn=&lt;SplitBackward0&gt;)
 </pre></div>
 </div>
 <p>We see that the output is now a single tensor, corresponding to the sample of the distribution.
@@ -798,7 +798,7 @@ <h3>Next steps and further reading<a class="headerlink" href="#next-steps-and-fu
 <li><p>For deployment of PyTorch code on servers without python environment, check the
 <a class="reference external" href="https://pytorch.org/docs/main/torch.compiler_aot_inductor.html">AOTInductor</a> documentation.</p></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.749 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.699 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorials-export-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/4e8ac58ef63f1e596d49d1b7366ef9bc/export.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">export.ipynb</span></code></a></p>
diff --git a/main/tutorials/sg_execution_times.html b/main/tutorials/sg_execution_times.html
index 370256260..8d3d59804 100644
--- a/main/tutorials/sg_execution_times.html
+++ b/main/tutorials/sg_execution_times.html
@@ -350,7 +350,7 @@
               
   <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>02:33.910</strong> total execution time for 11 files <strong>from tutorials</strong>:</p>
+<p><strong>02:29.308</strong> total execution time for 11 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" />
@@ -372,27 +372,27 @@
 </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>01:03.156</p></td>
+<td><p>01:00.787</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>00:57.776</p></td>
+<td><p>00:55.623</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>00:18.536</p></td>
+<td><p>00:18.477</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="streamed_tensordict.html#sphx-glr-tutorials-streamed-tensordict-py"><span class="std std-ref">Building tensordicts from streams</span></a> (<code class="docutils literal notranslate"><span class="pre">streamed_tensordict.py</span></code>)</p></td>
-<td><p>00:11.020</p></td>
+<td><p>00:11.021</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="export.html#sphx-glr-tutorials-export-py"><span class="std std-ref">Exporting tensordict modules</span></a> (<code class="docutils literal notranslate"><span class="pre">export.py</span></code>)</p></td>
-<td><p>00:01.749</p></td>
+<td><p>00:01.699</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>00:01.644</p></td>
+<td><p>00:01.673</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><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>
@@ -404,7 +404,7 @@
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="tensordict_slicing.html#sphx-glr-tutorials-tensordict-slicing-py"><span class="std std-ref">Slicing, Indexing, and Masking</span></a> (<code class="docutils literal notranslate"><span class="pre">tensordict_slicing.py</span></code>)</p></td>
-<td><p>00:00.005</p></td>
+<td><p>00:00.004</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><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>
diff --git a/main/tutorials/streamed_tensordict.html b/main/tutorials/streamed_tensordict.html
index 9f7717552..b1d9c3b06 100644
--- a/main/tutorials/streamed_tensordict.html
+++ b/main/tutorials/streamed_tensordict.html
@@ -594,7 +594,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 By leveraging the power of TensorDict and asyncio, you can build scalable and efficient data processing pipelines
 that can handle even the most demanding real-world applications.</p>
 <p>Thanks for following along with this tutorial! We hope you’ve found it helpful and informative.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 11.020 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 11.021 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorials-streamed-tensordict-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/ea350b8ef859f0797dd9ceffacb742e6/streamed_tensordict.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">streamed_tensordict.ipynb</span></code></a></p>
diff --git a/main/tutorials/tensorclass_fashion.html b/main/tutorials/tensorclass_fashion.html
index bf94802cf..b36a90a1e 100644
--- a/main/tutorials/tensorclass_fashion.html
+++ b/main/tutorials/tensorclass_fashion.html
@@ -397,29 +397,30 @@
 </pre></div>
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 </pre></div>
 </div>
 <p>Tensorclasses are dataclasses that expose dedicated tensor methods over
@@ -657,159 +658,159 @@ <h2>Optimizing the parameters<a class="headerlink" href="#optimizing-the-paramet
     is_shared=False)
 Epoch 1
 -------------------------
-loss: 2.307725 [    0/60000]
-loss: 2.293911 [ 6400/60000]
-loss: 2.270989 [12800/60000]
-loss: 2.266152 [19200/60000]
-loss: 2.249918 [25600/60000]
-loss: 2.222015 [32000/60000]
-loss: 2.243351 [38400/60000]
-loss: 2.207049 [44800/60000]
-loss: 2.203401 [51200/60000]
-loss: 2.178670 [57600/60000]
+loss: 2.296910 [    0/60000]
+loss: 2.290153 [ 6400/60000]
+loss: 2.267184 [12800/60000]
+loss: 2.267986 [19200/60000]
+loss: 2.241964 [25600/60000]
+loss: 2.218651 [32000/60000]
+loss: 2.225486 [38400/60000]
+loss: 2.190040 [44800/60000]
+loss: 2.199885 [51200/60000]
+loss: 2.154611 [57600/60000]
 Test Error:
- Accuracy: 42.7%, Avg loss: 2.167375
+ Accuracy: 42.6%, Avg loss: 2.150428
 
 Epoch 2
 -------------------------
-loss: 2.182459 [    0/60000]
-loss: 2.169180 [ 6400/60000]
-loss: 2.116737 [12800/60000]
-loss: 2.130716 [19200/60000]
-loss: 2.082952 [25600/60000]
-loss: 2.029855 [32000/60000]
-loss: 2.066772 [38400/60000]
-loss: 1.989665 [44800/60000]
-loss: 1.990337 [51200/60000]
-loss: 1.924362 [57600/60000]
+loss: 2.158208 [    0/60000]
+loss: 2.149079 [ 6400/60000]
+loss: 2.084302 [12800/60000]
+loss: 2.108220 [19200/60000]
+loss: 2.045984 [25600/60000]
+loss: 1.987296 [32000/60000]
+loss: 2.015088 [38400/60000]
+loss: 1.929641 [44800/60000]
+loss: 1.958254 [51200/60000]
+loss: 1.866098 [57600/60000]
 Test Error:
- Accuracy: 49.6%, Avg loss: 1.917381
+ Accuracy: 53.5%, Avg loss: 1.865167
 
 Epoch 3
 -------------------------
-loss: 1.949793 [    0/60000]
-loss: 1.919466 [ 6400/60000]
-loss: 1.812307 [12800/60000]
-loss: 1.849123 [19200/60000]
-loss: 1.738953 [25600/60000]
-loss: 1.688647 [32000/60000]
-loss: 1.719033 [38400/60000]
-loss: 1.611804 [44800/60000]
-loss: 1.635073 [51200/60000]
-loss: 1.534028 [57600/60000]
+loss: 1.897520 [    0/60000]
+loss: 1.866758 [ 6400/60000]
+loss: 1.742258 [12800/60000]
+loss: 1.795041 [19200/60000]
+loss: 1.682887 [25600/60000]
+loss: 1.626283 [32000/60000]
+loss: 1.653406 [38400/60000]
+loss: 1.553119 [44800/60000]
+loss: 1.593457 [51200/60000]
+loss: 1.478362 [57600/60000]
 Test Error:
- Accuracy: 58.3%, Avg loss: 1.543767
+ Accuracy: 61.4%, Avg loss: 1.497962
 
 Epoch 4
 -------------------------
-loss: 1.605731 [    0/60000]
-loss: 1.569755 [ 6400/60000]
-loss: 1.430758 [12800/60000]
-loss: 1.500959 [19200/60000]
-loss: 1.378377 [25600/60000]
-loss: 1.372691 [32000/60000]
-loss: 1.397103 [38400/60000]
-loss: 1.307562 [44800/60000]
-loss: 1.344959 [51200/60000]
-loss: 1.256637 [57600/60000]
+loss: 1.561284 [    0/60000]
+loss: 1.529328 [ 6400/60000]
+loss: 1.378711 [12800/60000]
+loss: 1.458048 [19200/60000]
+loss: 1.342770 [25600/60000]
+loss: 1.329875 [32000/60000]
+loss: 1.346497 [38400/60000]
+loss: 1.273600 [44800/60000]
+loss: 1.314018 [51200/60000]
+loss: 1.209880 [57600/60000]
 Test Error:
- Accuracy: 62.9%, Avg loss: 1.269797
+ Accuracy: 63.7%, Avg loss: 1.237870
 
 Epoch 5
 -------------------------
-loss: 1.341925 [    0/60000]
-loss: 1.322621 [ 6400/60000]
-loss: 1.170070 [12800/60000]
-loss: 1.274381 [19200/60000]
-loss: 1.146528 [25600/60000]
-loss: 1.172810 [32000/60000]
-loss: 1.204830 [38400/60000]
-loss: 1.124031 [44800/60000]
-loss: 1.165264 [51200/60000]
-loss: 1.097989 [57600/60000]
+loss: 1.307323 [    0/60000]
+loss: 1.295308 [ 6400/60000]
+loss: 1.129464 [12800/60000]
+loss: 1.241139 [19200/60000]
+loss: 1.117206 [25600/60000]
+loss: 1.134774 [32000/60000]
+loss: 1.158498 [38400/60000]
+loss: 1.098527 [44800/60000]
+loss: 1.139965 [51200/60000]
+loss: 1.053264 [57600/60000]
 Test Error:
- Accuracy: 65.0%, Avg loss: 1.102238
+ Accuracy: 65.0%, Avg loss: 1.075727
 
-Tensorclass training done! time:  8.6376 s
+Tensorclass training done! time:  8.7627 s
 Epoch 1
 -------------------------
-loss: 2.308171 [    0/60000]
-loss: 2.286401 [ 6400/60000]
-loss: 2.275298 [12800/60000]
-loss: 2.268542 [19200/60000]
-loss: 2.239279 [25600/60000]
-loss: 2.218736 [32000/60000]
-loss: 2.222399 [38400/60000]
-loss: 2.188224 [44800/60000]
-loss: 2.189977 [51200/60000]
-loss: 2.148144 [57600/60000]
+loss: 2.323289 [    0/60000]
+loss: 2.302869 [ 6400/60000]
+loss: 2.289611 [12800/60000]
+loss: 2.275158 [19200/60000]
+loss: 2.250096 [25600/60000]
+loss: 2.233657 [32000/60000]
+loss: 2.245745 [38400/60000]
+loss: 2.217353 [44800/60000]
+loss: 2.219387 [51200/60000]
+loss: 2.178802 [57600/60000]
 Test Error:
- Accuracy: 49.7%, Avg loss: 2.143384
+ Accuracy: 36.7%, Avg loss: 2.174884
 
 Epoch 2
 -------------------------
-loss: 2.157794 [    0/60000]
-loss: 2.140212 [ 6400/60000]
-loss: 2.083794 [12800/60000]
-loss: 2.101565 [19200/60000]
-loss: 2.035136 [25600/60000]
-loss: 1.974287 [32000/60000]
-loss: 2.003978 [38400/60000]
-loss: 1.920177 [44800/60000]
-loss: 1.925001 [51200/60000]
-loss: 1.841307 [57600/60000]
+loss: 2.194728 [    0/60000]
+loss: 2.183252 [ 6400/60000]
+loss: 2.136127 [12800/60000]
+loss: 2.144600 [19200/60000]
+loss: 2.085006 [25600/60000]
+loss: 2.040224 [32000/60000]
+loss: 2.076125 [38400/60000]
+loss: 2.007898 [44800/60000]
+loss: 2.011441 [51200/60000]
+loss: 1.941871 [57600/60000]
 Test Error:
- Accuracy: 59.0%, Avg loss: 1.844654
+ Accuracy: 52.2%, Avg loss: 1.933784
 
 Epoch 3
 -------------------------
-loss: 1.880996 [    0/60000]
-loss: 1.844965 [ 6400/60000]
-loss: 1.730476 [12800/60000]
-loss: 1.775033 [19200/60000]
-loss: 1.649532 [25600/60000]
-loss: 1.606074 [32000/60000]
-loss: 1.628593 [38400/60000]
-loss: 1.536964 [44800/60000]
-loss: 1.557712 [51200/60000]
-loss: 1.443908 [57600/60000]
+loss: 1.969968 [    0/60000]
+loss: 1.941942 [ 6400/60000]
+loss: 1.840044 [12800/60000]
+loss: 1.873434 [19200/60000]
+loss: 1.745236 [25600/60000]
+loss: 1.710266 [32000/60000]
+loss: 1.741888 [38400/60000]
+loss: 1.647687 [44800/60000]
+loss: 1.656758 [51200/60000]
+loss: 1.560415 [57600/60000]
 Test Error:
- Accuracy: 61.1%, Avg loss: 1.473452
+ Accuracy: 60.3%, Avg loss: 1.564881
 
 Epoch 4
 -------------------------
-loss: 1.543633 [    0/60000]
-loss: 1.506534 [ 6400/60000]
-loss: 1.365190 [12800/60000]
-loss: 1.443080 [19200/60000]
-loss: 1.310368 [25600/60000]
-loss: 1.313909 [32000/60000]
-loss: 1.327447 [38400/60000]
-loss: 1.263996 [44800/60000]
-loss: 1.290329 [51200/60000]
-loss: 1.189054 [57600/60000]
+loss: 1.630355 [    0/60000]
+loss: 1.595403 [ 6400/60000]
+loss: 1.451340 [12800/60000]
+loss: 1.516528 [19200/60000]
+loss: 1.379949 [25600/60000]
+loss: 1.382664 [32000/60000]
+loss: 1.401636 [38400/60000]
+loss: 1.334157 [44800/60000]
+loss: 1.352804 [51200/60000]
+loss: 1.255776 [57600/60000]
 Test Error:
- Accuracy: 63.1%, Avg loss: 1.221911
+ Accuracy: 63.3%, Avg loss: 1.275988
 
 Epoch 5
 -------------------------
-loss: 1.300781 [    0/60000]
-loss: 1.280785 [ 6400/60000]
-loss: 1.122259 [12800/60000]
-loss: 1.236478 [19200/60000]
-loss: 1.098779 [25600/60000]
-loss: 1.127348 [32000/60000]
-loss: 1.148480 [38400/60000]
-loss: 1.097973 [44800/60000]
-loss: 1.125805 [51200/60000]
-loss: 1.042096 [57600/60000]
+loss: 1.354131 [    0/60000]
+loss: 1.337473 [ 6400/60000]
+loss: 1.176684 [12800/60000]
+loss: 1.273267 [19200/60000]
+loss: 1.142443 [25600/60000]
+loss: 1.167726 [32000/60000]
+loss: 1.190811 [38400/60000]
+loss: 1.141034 [44800/60000]
+loss: 1.166486 [51200/60000]
+loss: 1.081528 [57600/60000]
 Test Error:
- Accuracy: 64.6%, Avg loss: 1.067900
+ Accuracy: 64.8%, Avg loss: 1.100232
 
-Training done! time:  36.0137 s
+Training done! time:  34.3544 s
 </pre></div>
 </div>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 0.787 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/main/tutorials/tensorclass_imagenet.html b/main/tutorials/tensorclass_imagenet.html
index 2d2304a45..fe9e15fb4 100644
--- a/main/tutorials/tensorclass_imagenet.html
+++ b/main/tutorials/tensorclass_imagenet.html
@@ -739,7 +739,7 @@
 <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, 234.58it/s]ImageNetData(
+ 26%|██▌       | 64/244 [00:00&lt;00:00, 231.24it/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]),
@@ -788,10 +788,10 @@
     device=None,
     is_shared=False)
 
-100%|██████████| 244/244 [00:00&lt;00:00, 723.10it/s]
+100%|██████████| 244/244 [00:00&lt;00:00, 711.98it/s]
 
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+ 42%|████▏     | 64/153 [00:00&lt;00:00, 186.72it/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]),
@@ -828,14 +828,14 @@
     device=None,
     is_shared=False)
 
-100%|██████████| 153/153 [00:00&lt;00:00, 326.50it/s]
-One iteration over dataloader done! Rate: 878.3548 fps, time:  0.2505s
-One iteration over tensorclass dataloader done! Rate: 1869.2406 fps, time:  0.1177s
-One iteration over val data done! Rate: 547.5260 fps, time:  0.2356s
-One iteration over tensorclass val data done! Rate: 19560.5805 fps, time:  0.0066s
+100%|██████████| 153/153 [00:00&lt;00:00, 326.40it/s]
+One iteration over dataloader done! Rate: 879.3836 fps, time:  0.2502s
+One iteration over tensorclass dataloader done! Rate: 1681.0041 fps, time:  0.1309s
+One iteration over val data done! Rate: 539.4555 fps, time:  0.2391s
+One iteration over tensorclass val data done! Rate: 18891.9419 fps, time:  0.0068s
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.644 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.673 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/main/tutorials/tensordict_module.html b/main/tutorials/tensordict_module.html
index 5f042a0ac..a70ddde60 100644
--- a/main/tutorials/tensordict_module.html
+++ b/main/tutorials/tensordict_module.html
@@ -560,13 +560,13 @@ <h2>Runtime<a class="headerlink" href="#runtime" title="Permalink to this headin
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Without compile
-Regular:  220.6026 us
-TDM:  277.7298 us
-Sequential:  481.1476 us
+Regular:  218.3131 us
+TDM:  275.8440 us
+Sequential:  479.7458 us
 Compiled versions
-Compiled regular:  342.2225 us
-Compiled TDM:  350.3220 us
-Compiled sequential:  363.3525 us
+Compiled regular:  303.8840 us
+Compiled TDM:  343.8845 us
+Compiled sequential:  356.0950 us
 </pre></div>
 </div>
 <p>As one can see, the onverhead introduced by <a class="reference internal" href="../reference/generated/tensordict.nn.TensorDictSequential.html#tensordict.nn.TensorDictSequential" title="tensordict.nn.TensorDictSequential"><code class="xref py py-class docutils literal notranslate"><span class="pre">TensorDictSequential</span></code></a> has been completely resolved.</p>
@@ -666,7 +666,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 introduced by <a class="reference internal" href="../reference/generated/tensordict.nn.TensorDictSequential.html#tensordict.nn.TensorDictSequential" title="tensordict.nn.TensorDictSequential"><code class="xref py py-class docutils literal notranslate"><span class="pre">tensordict.nn.TensorDictSequential</span></code></a> can be completely removed, leaving users with a neat,
 tensordict-free version of their module.</p>
 <p>In the next tutorial, we will be seeing how <code class="docutils literal notranslate"><span class="pre">torch.export</span></code> can be used to isolate a module and export it.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 18.536 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 18.477 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/main/tutorials/tensordict_shapes.html b/main/tutorials/tensordict_shapes.html
index 22b2f1c71..9bba59ead 100644
--- a/main/tutorials/tensordict_shapes.html
+++ b/main/tutorials/tensordict_shapes.html
@@ -672,19 +672,19 @@ <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([[2, 2, 0, 1],
-        [1, 0, 3, 2],
-        [2, 0, 1, 2]])
+ tensor([[2, 3, 2, 2],
+        [3, 0, 0, 2],
+        [3, 2, 2, 3]])
 
 tensordict[&#39;a&#39;]:
- tensor([[0.7156, 0.9267, 0.7474, 0.7576],
-        [0.9559, 0.2976, 0.4468, 0.1741],
-        [0.6645, 0.0389, 0.9629, 0.7605]])
+ tensor([[0.2576, 0.1782, 0.4756, 0.7252],
+        [0.8398, 0.6909, 0.8339, 0.9424],
+        [0.8191, 0.7667, 0.1551, 0.6584]])
 
 gathered_tensordict[&#39;a&#39;]:
- tensor([[0.7474, 0.7474, 0.7156, 0.9267],
-        [0.2976, 0.9559, 0.1741, 0.4468],
-        [0.9629, 0.6645, 0.0389, 0.9629]])
+ tensor([[0.4756, 0.7252, 0.4756, 0.4756],
+        [0.9424, 0.8398, 0.8398, 0.8339],
+        [0.6584, 0.1551, 0.1551, 0.6584]])
 </pre></div>
 </div>
 <p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.008 seconds)</p>
diff --git a/main/tutorials/tensordict_slicing.html b/main/tutorials/tensordict_slicing.html
index 9c2acabc4..78d64f21c 100644
--- a/main/tutorials/tensordict_slicing.html
+++ b/main/tutorials/tensordict_slicing.html
@@ -479,7 +479,7 @@ <h2>Masking<a class="headerlink" href="#masking" title="Permalink to this headin
     is_shared=False)
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.005 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.004 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorials-tensordict-slicing-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/245c7aad3c5052fda391900ac51962d9/tensordict_slicing.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_slicing.ipynb</span></code></a></p>