From e668ae1e0a757cf8217e926be9ae228676fbe17b Mon Sep 17 00:00:00 2001
From: Alby M <1473644+Linux-cpp-lisp@users.noreply.github.com>
Date: Wed, 21 Apr 2021 14:04:10 -0600
Subject: Indents

---
 torch_ema/ema.py | 31 ++++++++++++++++---------------
 1 file changed, 16 insertions(+), 15 deletions(-)

diff --git a/torch_ema/ema.py b/torch_ema/ema.py
index 2aa3004..3bcb465 100644
--- a/torch_ema/ema.py
+++ b/torch_ema/ema.py
@@ -4,6 +4,7 @@ from __future__ import unicode_literals
 from typing import Iterable, Optional
 import weakref
 import copy
+import contextlib
 
 import torch
 
@@ -79,10 +80,10 @@ class ExponentialMovingAverage:
         the `optimizer.step()` call.
 
         Args:
-          parameters: Iterable of `torch.nn.Parameter`; usually the same set of
-            parameters used to initialize this object. If `None`, the
-            parameters with which this `ExponentialMovingAverage` was
-            initialized will be used.
+            parameters: Iterable of `torch.nn.Parameter`; usually the same set of
+                parameters used to initialize this object. If `None`, the
+                parameters with which this `ExponentialMovingAverage` was
+                initialized will be used.
         """
         parameters = self._get_parameters(parameters)
         decay = self.decay
@@ -109,10 +110,10 @@ class ExponentialMovingAverage:
         Copy current averaged parameters into given collection of parameters.
 
         Args:
-          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            updated with the stored moving averages. If `None`, the
-            parameters with which this `ExponentialMovingAverage` was
-            initialized will be used.
+            parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+                updated with the stored moving averages. If `None`, the
+                parameters with which this `ExponentialMovingAverage` was
+                initialized will be used.
         """
         parameters = self._get_parameters(parameters)
         for s_param, param in zip(self.shadow_params, parameters):
@@ -127,9 +128,9 @@ class ExponentialMovingAverage:
         Save the current parameters for restoring later.
 
         Args:
-          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            temporarily stored. If `None`, the parameters of with which this
-            `ExponentialMovingAverage` was initialized will be used.
+            parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+                temporarily stored. If `None`, the parameters of with which this
+                `ExponentialMovingAverage` was initialized will be used.
         """
         parameters = self._get_parameters(parameters)
         self.collected_params = [
@@ -150,10 +151,10 @@ class ExponentialMovingAverage:
         restore the former parameters.
 
         Args:
-          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            updated with the stored parameters. If `None`, the
-            parameters with which this `ExponentialMovingAverage` was
-            initialized will be used.
+            parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+                updated with the stored parameters. If `None`, the
+                parameters with which this `ExponentialMovingAverage` was
+                initialized will be used.
         """
         if self.collected_params is None:
             raise RuntimeError(
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