Translate en/tasks folder docs to Japanese 🇯🇵 (#27098)

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docs/source/en/tasks/idefics.md

@@ -109,7 +109,6 @@ on the fly while loading.
 Now that you have the model loaded in one of the suggested ways, let's move on to exploring tasks that you can use IDEFICS for.
 
 ## Image captioning
-
 Image captioning is the task of predicting a caption for a given image. A common application is to aid visually impaired 
 people navigate through different situations, for instance, explore image content online. 
 
@@ -229,7 +228,7 @@ Let's get a new image for this task:
      <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/idefics-vqa.jpg" alt="Image of a couple having a picnic"/>
 </div>
 
-Photo by [Jarritos Mexican Soda](https://unsplash.com/@jarritos).
+Photo by [Jarritos Mexican Soda](https://unsplash.com/@jarritos). 
 
 You can steer the model from image captioning to visual question answering by prompting it with appropriate instructions: 
 

docs/source/en/tasks/summarization.md

@@ -126,6 +126,7 @@ Now create a batch of examples using [`DataCollatorForSeq2Seq`]. It's more effic
 
 <frameworkcontent>
 <pt>
+
 ```py
 >>> from transformers import DataCollatorForSeq2Seq
 
@@ -133,6 +134,7 @@ Now create a batch of examples using [`DataCollatorForSeq2Seq`]. It's more effic
 ```
 </pt>
 <tf>
+
 ```py
 >>> from transformers import DataCollatorForSeq2Seq
 

docs/source/en/tasks/translation.md

@@ -232,7 +232,7 @@ At this point, only three steps remain:
 ... )
 
 >>> trainer.train()
-````
+```
 
 Once training is completed, share your model to the Hub with the [`~transformers.Trainer.push_to_hub`] method so everyone can use your model: