Make consitent doc string for step method.

Author: jettify

torch_optimizer/accsgd.py

@@ -59,8 +59,7 @@ def step(self, closure: OptLossClosure = None) -> OptFloat:
         r"""Performs a single optimization step.
 
         Arguments:
-            closure: A closure that reevaluates the model
-                and returns the loss.
+            closure: A closure that reevaluates the model and returns the loss.
         """
         loss = None
         if closure is not None:

torch_optimizer/adabound.py

@@ -84,8 +84,7 @@ def step(self, closure: OptLossClosure = None) -> OptFloat:
         r"""Performs a single optimization step.
 
         Arguments:
-            closure (callable, optional): A closure that reevaluates the model
-                and returns the loss.
+            closure: A closure that reevaluates the model and returns the loss.
         """
         loss = None
         if closure is not None:

torch_optimizer/adamod.py

@@ -66,8 +66,7 @@ def step(self, closure: OptLossClosure = None) -> OptFloat:
         """Performs a single optimization step.
 
         Arguments:
-            closure (callable, optional): A closure that reevaluates the model
-                and returns the loss.
+            closure: A closure that reevaluates the model and returns the loss.
         """
         loss = None
         if closure is not None:

torch_optimizer/diffgrad.py

@@ -60,8 +60,7 @@ def step(self, closure: OptLossClosure = None) -> OptFloat:
         r"""Performs a single optimization step.
 
         Arguments:
-            closure (callable, optional): A closure that reevaluates the model
-                and returns the loss.
+            closure: A closure that reevaluates the model and returns the loss.
         """
         loss = None
         if closure is not None:

torch_optimizer/lamb.py

@@ -63,8 +63,7 @@ def step(self, closure: OptLossClosure = None) -> OptFloat:
         r"""Performs a single optimization step.
 
         Arguments:
-            closure (callable, optional): A closure that reevaluates the model
-                and returns the loss.
+            closure: A closure that reevaluates the model and returns the loss.
         """
         loss = None
         if closure is not None:

torch_optimizer/lookahead.py

@@ -63,8 +63,7 @@ def step(self, closure: OptLossClosure = None) -> OptFloat:
         r"""Performs a single optimization step.
 
         Arguments:
-            closure (callable, optional): A closure that reevaluates the model
-                and returns the loss.
+            closure: A closure that reevaluates the model and returns the loss.
         """
         loss = self.optimizer.step(closure=closure)
         for group in self.param_groups:

torch_optimizer/novograd.py

@@ -79,8 +79,7 @@ def step(self, closure: OptLossClosure = None) -> OptFloat:
         r"""Performs a single optimization step.
 
         Arguments:
-            closure (callable, optional): A closure that reevaluates the model
-                and returns the loss.
+            closure: A closure that reevaluates the model and returns the loss.
         """
         loss = None
         if closure is not None:

torch_optimizer/radam.py

@@ -61,8 +61,7 @@ def step(self, closure: OptLossClosure = None) -> OptFloat:
         r"""Performs a single optimization step.
 
         Arguments:
-            closure (callable, optional): A closure that reevaluates the model
-                and returns the loss.
+            closure: A closure that reevaluates the model and returns the loss.
         """
 
         loss = None

torch_optimizer/sgdw.py

@@ -69,8 +69,7 @@ def step(self, closure: OptLossClosure = None) -> OptFloat:
         """Performs a single optimization step.
 
         Arguments:
-            closure (callable, optional): A closure that reevaluates the model
-                and returns the loss.
+            closure: A closure that reevaluates the model and returns the loss.
         """
         loss = None
         if closure is not None:

torch_optimizer/yogi.py

@@ -59,8 +59,7 @@ def step(self, closure: OptLossClosure = None) -> OptFloat:
         r"""Performs a single optimization step.
 
         Arguments:
-            closure (callable, optional): A closure that reevaluates the model
-                and returns the loss.
+            closure: A closure that reevaluates the model and returns the loss.
         """
         loss = None
         if closure is not None: