diff --git a/chapters/en/chapter2/3.mdx b/chapters/en/chapter2/3.mdx
index cf6309eb1..a11cdc8b9 100644
--- a/chapters/en/chapter2/3.mdx
+++ b/chapters/en/chapter2/3.mdx
@@ -277,21 +277,11 @@ encoded_sequences = [
 ]
 ```
 
-This is a list of encoded sequences: a list of lists. Tensors only accept rectangular shapes (think matrices). This "array" is already of rectangular shape, so converting it to a tensor is easy:
+This is a list of encoded sequences: a list of lists. As you can see, the lengths of the tokenized outputs are different. Tensors require a "rectangular" shape (like a matrix), so you won't be able to convert this list of lists directly to a tensor. To resolve this, you need to use padding, as we saw earlier.
 
-```py
-import torch
-
-model_inputs = torch.tensor(encoded_sequences)
-```
-
-### Using the tensors as inputs to the model[[using-the-tensors-as-inputs-to-the-model]]
+### Using the tensors as inputs to the model [[using-the-tensors-as-inputs-to-the-model]]
 
-Making use of the tensors with the model is extremely simple — we just call the model with the inputs:
-
-```py
-output = model(model_inputs)
-```
+Making use of the tensors with the model is extremely simple...
 
 While the model accepts a lot of different arguments, only the input IDs are necessary. We'll explain what the other arguments do and when they are required later, 
 but first we need to take a closer look at the tokenizers that build the inputs that a Transformer model can understand.