Fix example of custom Trainer to reflect signature of compute_loss (#10537)

docs/source/main_classes/trainer.rst

@@ -23,14 +23,14 @@ customization during training.
 The API supports distributed training on multiple GPUs/TPUs, mixed precision through `NVIDIA Apex
 <https://github.com/NVIDIA/apex>`__ for PyTorch and :obj:`tf.keras.mixed_precision` for TensorFlow.
 
-Both :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` contain the basic training loop supporting the
-previous features. To inject custom behavior you can subclass them and override the following methods:
+Both :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` contain the basic training loop which supports
+the above features. To inject custom behavior you can subclass them and override the following methods:
 
 - **get_train_dataloader**/**get_train_tfdataset** -- Creates the training DataLoader (PyTorch) or TF Dataset.
 - **get_eval_dataloader**/**get_eval_tfdataset** -- Creates the evaluation DataLoader (PyTorch) or TF Dataset.
 - **get_test_dataloader**/**get_test_tfdataset** -- Creates the test DataLoader (PyTorch) or TF Dataset.
 - **log** -- Logs information on the various objects watching training.
-- **create_optimizer_and_scheduler** -- Setups the optimizer and learning rate scheduler if they were not passed at
+- **create_optimizer_and_scheduler** -- Sets up the optimizer and learning rate scheduler if they were not passed at
   init.
 - **compute_loss** - Computes the loss on a batch of training inputs.
 - **training_step** -- Performs a training step.
@@ -39,17 +39,23 @@ previous features. To inject custom behavior you can subclass them and override
 - **evaluate** -- Runs an evaluation loop and returns metrics.
 - **predict** -- Returns predictions (with metrics if labels are available) on a test set.
 
-Here is an example of how to customize :class:`~transformers.Trainer` using a custom loss function:
+Here is an example of how to customize :class:`~transformers.Trainer` using a custom loss function for multi-label
+classification:
 
 .. code-block:: python
 
+    import torch
     from transformers import Trainer
-    class MyTrainer(Trainer):
-        def compute_loss(self, model, inputs):
+
+    class MultilabelTrainer(Trainer):
+        def compute_loss(self, model, inputs, return_outputs=False):
             labels = inputs.pop("labels")
             outputs = model(**inputs)
-            logits = outputs[0]
-            return my_custom_loss(logits, labels)
+            logits = outputs.logits
+            loss_fct = torch.nn.BCEWithLogitsLoss()
+            loss = loss_fct(logits.view(-1, self.model.config.num_labels),
+                            labels.float().view(-1, self.model.config.num_labels))
+            return (loss, outputs) if return_outputs else loss
 
 Another way to customize the training loop behavior for the PyTorch :class:`~transformers.Trainer` is to use
 :doc:`callbacks <callback>` that can inspect the training loop state (for progress reporting, logging on TensorBoard or