Doc styler examples (#14953)

* Fix bad examples

* Add black formatting to style_doc

* Use first nonempty line

* Put it at the right place

* Don't add spaces to empty lines

* Better templates

* Deal with triple quotes in docstrings

* Result of style_doc

* Enable mdx treatment and fix code examples in MDXs

* Result of doc styler on doc source files

* Last fixes

* Break copy from

docs/source/model_doc/canine.mdx

@@ -64,13 +64,13 @@ CANINE works on raw characters, so it can be used without a tokenizer:
 >>> from transformers import CanineModel
 >>> import torch
 
->>> model = CanineModel.from_pretrained('google/canine-c') # model pre-trained with autoregressive character loss
+>>> model = CanineModel.from_pretrained("google/canine-c")  # model pre-trained with autoregressive character loss
 
 >>> text = "hello world"
 >>> # use Python's built-in ord() function to turn each character into its unicode code point id
 >>> input_ids = torch.tensor([[ord(char) for char in text]])
 
->>> outputs = model(input_ids) # forward pass
+>>> outputs = model(input_ids)  # forward pass
 >>> pooled_output = outputs.pooler_output
 >>> sequence_output = outputs.last_hidden_state
 ```
@@ -81,13 +81,13 @@ sequences to the same length):
 ```python
 >>> from transformers import CanineTokenizer, CanineModel
 
->>> model = CanineModel.from_pretrained('google/canine-c')
->>> tokenizer = CanineTokenizer.from_pretrained('google/canine-c')
+>>> model = CanineModel.from_pretrained("google/canine-c")
+>>> tokenizer = CanineTokenizer.from_pretrained("google/canine-c")
 
 >>> inputs = ["Life is like a box of chocolates.", "You never know what you gonna get."]
 >>> encoding = tokenizer(inputs, padding="longest", truncation=True, return_tensors="pt")
 
->>> outputs = model(**encoding) # forward pass
+>>> outputs = model(**encoding)  # forward pass
 >>> pooled_output = outputs.pooler_output
 >>> sequence_output = outputs.last_hidden_state
 ```