docs: Add empty lines in docstring [ci skip] (#4232)

* Add empty lines in docstring for proper docs

* Remove Returns:

* Remove unnecessary Returns:

* Update pytorch_lightning/accelerators/ddp2_accelerator.py

Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com>

* fix returns

Co-authored-by: William Falcon <waf2107@columbia.edu>
Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com>

pytorch_lightning/accelerators/ddp2_accelerator.py

@@ -122,11 +122,12 @@ class DDP2Accelerator(Accelerator):
         Entry point for ddp
 
         Args:
-            process_idx:
+            process_idx: current process rank
             mp_queue: multiprocessing queue
-            model:
+            model: pointer to current :class:`LightningModule`
 
         Returns:
+            Dict with evaluation results
 
         """
         # show progressbar only on progress_rank 0

pytorch_lightning/accelerators/ddp_accelerator.py

@@ -212,6 +212,7 @@ class DDPAccelerator(Accelerator):
             model:
 
         Returns:
+            Dict with evaluation results
 
         """
         seed = os.environ.get("PL_GLOBAL_SEED")

pytorch_lightning/accelerators/ddp_cpu_slurm_accelerator.py

@@ -111,6 +111,7 @@ class DDPCPUSLURMAccelerator(Accelerator):
             model:
 
         Returns:
+            Dict with evaluation results
 
         """
         # determine which process we are and world size

pytorch_lightning/accelerators/ddp_cpu_spawn_accelerator.py

@@ -73,11 +73,11 @@ class DDPCPUSpawnAccelerator(Accelerator):
     def ddp_train(self, process_idx, mp_queue, model):
         """
         Entry point for ddp
+
         Args:
             process_idx:
             mp_queue: multiprocessing queue
             model:
-        Returns:
         """
         # show progressbar only on progress_rank 0
         if (self.trainer.node_rank != 0 or process_idx != 0) and self.trainer.progress_bar_callback is not None:

pytorch_lightning/accelerators/ddp_cpu_torchelastic_accelerator.py

@@ -110,6 +110,7 @@ class DDPCPUTorchElasticAccelerator(Accelerator):
             model:
 
         Returns:
+            Dict with evaluation results
 
         """
         # determine which process we are and world size

pytorch_lightning/accelerators/ddp_slurm_accelerator.py

@@ -113,6 +113,7 @@ class DDPSLURMAccelerator(Accelerator):
             model:
 
         Returns:
+            Dict with evaluation results
 
         """
         seed = os.environ.get("PL_GLOBAL_SEED")

pytorch_lightning/accelerators/ddp_spawn_accelerator.py

@@ -81,9 +81,6 @@ class DDPSpawnAccelerator(Accelerator):
             process_idx:
             mp_queue: multiprocessing queue
             model:
-
-        Returns:
-
         """
         seed = os.environ.get("PL_GLOBAL_SEED")
         if seed is not None:

pytorch_lightning/accelerators/ddp_torchelastic_accelerator.py

@@ -113,6 +113,7 @@ class DDPTorchElasticAccelerator(Accelerator):
             model:
 
         Returns:
+            Dict with evaluation results
 
         """
         # determine which process we are and world size

pytorch_lightning/metrics/functional/classification.py

@@ -85,13 +85,13 @@ def get_num_classes(
     """
     Calculates the number of classes for a given prediction and target tensor.
 
-        Args:
-            pred: predicted values
-            target: true labels
-            num_classes: number of classes if known
+    Args:
+        pred: predicted values
+        target: true labels
+        num_classes: number of classes if known
 
-        Return:
-            An integer that represents the number of classes.
+    Return:
+        An integer that represents the number of classes.
     """
     num_target_classes = int(target.max().detach().item() + 1)
     num_pred_classes = int(pred.max().detach().item() + 1)
@@ -272,6 +272,7 @@ def accuracy(
             - ``'none'``: returns calculated metric per class
         return_state: returns a internal state that can be ddp reduced
             before doing the final calculation
+
     Return:
          A Tensor with the accuracy score.
 

pytorch_lightning/utilities/xla_device_utils.py

@@ -55,8 +55,10 @@ class XLADeviceUtils:
     def _fetch_xla_device_type(device: torch.device) -> str:
         """
         Returns XLA device type
+
         Args:
             device: (:class:`~torch.device`): Accepts a torch.device type with a XLA device format i.e xla:0
+
         Return:
             Returns a str of the device hardware type. i.e TPU
         """
@@ -67,6 +69,7 @@ class XLADeviceUtils:
     def _is_device_tpu() -> bool:
         """
         Check if device is TPU
+
         Return:
             A boolean value indicating if the xla device is a TPU device or not
         """
@@ -79,6 +82,7 @@ class XLADeviceUtils:
     def tpu_device_exists() -> bool:
         """
         Public method to check if TPU is available
+
         Return:
             A boolean value indicating if a TPU device exists on the system
         """