Automated tutorials push

Author: pytorchbot

_downloads/57114670a041b4c96ed6eb9fc17a6b3f/transformer_building_blocks.py

@@ -3,8 +3,6 @@
 =============================================================================================================
 **Author:** `Mikayla Gawarecki <https://github.com/mikaylagawarecki>`_
 
-.. note::
-    This tutorial currently requires you to use the PyTorch nightly build.
 
 .. grid:: 2
 

_downloads/e201125c39959609ca168c306995205c/transformer_building_blocks.ipynb

@@ -22,14 +22,6 @@
     "\n",
     "**Author:** [Mikayla Gawarecki](https://github.com/mikaylagawarecki)\n",
     "\n",
-    "<div style=\"background-color: #54c7ec; color: #fff; font-weight: 700; padding-left: 10px; padding-top: 5px; padding-bottom: 5px\"><strong>NOTE:</strong></div>\n",
-    "\n",
-    "<div style=\"background-color: #f3f4f7; padding-left: 10px; padding-top: 10px; padding-bottom: 10px; padding-right: 10px\">\n",
-    "\n",
-    "<p>This tutorial currently requires you to use the PyTorch nightly build.</p>\n",
-    "\n",
-    "</div>\n",
-    "\n",
     "<div style=\"width: 45%; float: left; padding: 20px;\"><h2> What you will learn</h2><ul><li>Learn about the low-level building blocks PyTorch provides to build custom transformer layers (nested tensors, <code>scaled_dot_product_attention</code>, <code>torch.compile()</code>, and <code>FlexAttention</code>)</li><li>Discover how the above improve memory usage and performance using MultiHeadAttention as an example</li><li>Explore advanced customizations using the aforementioned building blocks</li></ul></div><div style=\"width: 45%; float: right; padding: 20px;\"><h2> Prerequisites</h2><ul><li>PyTorch v.2.6.0 or later</li></ul></div>\n",
     "\n",
     "Over the past few years, the PyTorch team has developed various lower\n",