update colout link in custom speedup notebook

examples/custom_speedup_methods.ipynb

@@ -193,7 +193,7 @@
    "source": [
     "\n",
     "\n",
-    "For this tutorial, we'll look at how to implement one of the simpler speedup methods currently in our composer library: [ColOut](https://docs.mosaicml.com/en/stable/method_cards/col_out.html). This method works on image data by dropping random rows and columns from the training images. This reduces the size of the training images, which reduces the time per training iteration and hopefully does not alter the semantic content of the image too much. Additionally, dropping a small fraction of random rows and columns can also slightly distort objects and perhaps provide a data augmentation effect.\n",
+    "For this tutorial, we'll look at how to implement one of the simpler speedup methods currently in our composer library: [ColOut](https://docs.mosaicml.com/en/stable/method_cards/colout.html). This method works on image data by dropping random rows and columns from the training images. This reduces the size of the training images, which reduces the time per training iteration and hopefully does not alter the semantic content of the image too much. Additionally, dropping a small fraction of random rows and columns can also slightly distort objects and perhaps provide a data augmentation effect.\n",
     "\n",
     "To start our implementation, we'll write a function to drop random rows and columns from a batch of input images. We'll assume that these are torch tensors and operate on a batch, rather than individual images, for simplicity here.\n",
     "\n"