Fix task guide formatting (#21409)

fix formatting

docs/source/en/tasks/audio_classification.mdx

@@ -193,6 +193,7 @@ Your `compute_metrics` function is ready to go now, and you'll return to it when
 If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#train-with-pytorch-trainer)!
 
 </Tip>
+
 You're ready to start training your model now! Load Wav2Vec2 with [`AutoModelForAudioClassification`] along with the number of expected labels, and the label mappings:
 
 ```py

docs/source/en/tasks/language_modeling.mdx

@@ -209,6 +209,7 @@ Use the end-of-sequence token as the padding token and set `mlm=False`. This wil
 If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the [basic tutorial](../training#train-with-pytorch-trainer)!
 
 </Tip>
+
 You're ready to start training your model now! Load DistilGPT2 with [`AutoModelForCausalLM`]:
 
 ```py

docs/source/en/tasks/masked_language_modeling.mdx

@@ -203,6 +203,7 @@ Use the end-of-sequence token as the padding token and specify `mlm_probability`
 If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#train-with-pytorch-trainer)!
 
 </Tip>
+
 You're ready to start training your model now! Load DistilRoBERTa with [`AutoModelForMaskedLM`]:
 
 ```py

docs/source/en/tasks/multiple_choice.mdx

@@ -241,6 +241,7 @@ Your `compute_metrics` function is ready to go now, and you'll return to it when
 If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#train-with-pytorch-trainer)!
 
 </Tip>
+
 You're ready to start training your model now! Load BERT with [`AutoModelForMultipleChoice`]:
 
 ```py

docs/source/en/tasks/question_answering.mdx

@@ -196,6 +196,7 @@ Now create a batch of examples using [`DefaultDataCollator`]. Unlike other data
 If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#train-with-pytorch-trainer)!
 
 </Tip>
+
 You're ready to start training your model now! Load DistilBERT with [`AutoModelForQuestionAnswering`]:
 
 ```py

docs/source/en/tasks/sequence_classification.mdx

@@ -155,6 +155,7 @@ Before you start training your model, create a map of the expected ids to their
 If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#train-with-pytorch-trainer)!
 
 </Tip>
+
 You're ready to start training your model now! Load DistilBERT with [`AutoModelForSequenceClassification`] along with the number of expected labels, and the label mappings:
 
 ```py

docs/source/en/tasks/summarization.mdx

@@ -176,6 +176,7 @@ Your `compute_metrics` function is ready to go now, and you'll return to it when
 If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#train-with-pytorch-trainer)!
 
 </Tip>
+
 You're ready to start training your model now! Load T5 with [`AutoModelForSeq2SeqLM`]:
 
 ```py

docs/source/en/tasks/token_classification.mdx

@@ -261,6 +261,7 @@ Before you start training your model, create a map of the expected ids to their
 If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#train-with-pytorch-trainer)!
 
 </Tip>
+
 You're ready to start training your model now! Load DistilBERT with [`AutoModelForTokenClassification`] along with the number of expected labels, and the label mappings:
 
 ```py

docs/source/en/tasks/translation.mdx

@@ -185,6 +185,7 @@ Your `compute_metrics` function is ready to go now, and you'll return to it when
 If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#train-with-pytorch-trainer)!
 
 </Tip>
+
 You're ready to start training your model now! Load T5 with [`AutoModelForSeq2SeqLM`]:
 
 ```py