diff --git a/docs/source/en/tutorials/basic_training.mdx b/docs/source/en/tutorials/basic_training.mdx
index 1e91f81429..435de38d83 100644
--- a/docs/source/en/tutorials/basic_training.mdx
+++ b/docs/source/en/tutorials/basic_training.mdx
@@ -252,6 +252,7 @@ Then, you'll need a way to evaluate the model. For evaluation, you can use the [
 ```py
 >>> from diffusers import DDPMPipeline
 >>> import math
+>>> import os
 
 
 >>> def make_grid(images, rows, cols):
@@ -411,4 +412,4 @@ Unconditional image generation is one example of a task that can be trained. You
 * [Textual Inversion](./training/text_inversion), an algorithm that teaches a model a specific visual concept and integrates it into the generated image.
 * [DreamBooth](./training/dreambooth), a technique for generating personalized images of a subject given several input images of the subject.
 * [Guide](./training/text2image) to finetuning a Stable Diffusion model on your own dataset.
-* [Guide](./training/lora) to using LoRA, a memory-efficient technique for finetuning really large models faster.
\ No newline at end of file
+* [Guide](./training/lora) to using LoRA, a memory-efficient technique for finetuning really large models faster.