From c6c0e8dc67589ed51015d7398725ba8171ca97b0 Mon Sep 17 00:00:00 2001
From: Brendan Shillingford <brendan2609@gmail.com>
Date: Fri, 14 Oct 2016 00:25:38 +0800
Subject: [PATCH] Formatting and indentation fixes

---
 Creating Extensions using FFI.md | 59 +++++++++++++++-----------------
 1 file changed, 28 insertions(+), 31 deletions(-)

diff --git a/Creating Extensions using FFI.md b/Creating Extensions using FFI.md
index a6ee63f120f..185ce4c86a9 100644
--- a/Creating Extensions using FFI.md	
+++ b/Creating Extensions using FFI.md	
@@ -6,7 +6,7 @@ First, you have to write your C functions.
 
 Below you can find an example implementation of forward and backward functions of a module that adds its both inputs.
 
-In your .c files you can include TH using an #include <TH/TH.h> directive, and THC using #include <THC/THC.h>.
+In your `.c` files you can include TH using an `#include <TH/TH.h>` directive, and THC using `#include <THC/THC.h>`.
 
 ffi utils will make sure a compiler can find them during the build.
 
@@ -17,18 +17,18 @@ ffi utils will make sure a compiler can find them during the build.
 int my_lib_add_forward(THFloatTensor *input1, THFloatTensor *input2,
 THFloatTensor *output)
 {
-if (!THFloatTensor_isSameSizeAs(input1, input2))
-return 0;
-THFloatTensor_resizeAs(output, input1);
-THFloatTensor_add(output, input1, input2);
-return 1;
+    if (!THFloatTensor_isSameSizeAs(input1, input2))
+        return 0;
+    THFloatTensor_resizeAs(output, input1);
+    THFloatTensor_add(output, input1, input2);
+    return 1;
 }
 
 int my_lib_add_backward(THFloatTensor *grad_output, THFloatTensor *grad_input)
 {
-THFloatTensor_resizeAs(grad_input, grad_output);
-THFloatTensor_fill(grad_input, 1);
-return 1;
+    THFloatTensor_resizeAs(grad_input, grad_output);
+    THFloatTensor_fill(grad_input, 1);
+    return 1;
 }
 ```
 
@@ -39,8 +39,7 @@ It will be used by the ffi utils to generate appropriate wrappers.
 
 ```C
 /* src/my_lib.h */
-int my_lib_add_forward(THFloatTensor *input1, THFloatTensor *input2,
-THFloatTensor *output);
+int my_lib_add_forward(THFloatTensor *input1, THFloatTensor *input2, THFloatTensor *output);
 int my_lib_add_backward(THFloatTensor *grad_output, THFloatTensor *grad_input);
 ```
 
@@ -59,7 +58,7 @@ with_cuda=False
 
 ## Step 2: Include it in your Python code
 
-After you run it, pytorch will create an _ext directory and put my_lib inside.
+After you run it, pytorch will create an `_ext` directory and put `my_lib` inside.
 
 Package name can have an arbitrary number of packages preceding the final module name (including none).
 If the build succeeded you can import your extension just like a regular python file.
@@ -72,16 +71,15 @@ from _ext import my_lib
 
 
 class MyAddFunction(Function):
-
-def forward(self, input1, input2):
-output = torch.FloatTensor()
-my_lib.my_lib_add_forward(input1, input2, output)
-return output
-
-def backward(self, grad_output):
-grad_input = torch.FloatTensor()
-my_lib.my_lib_add_backward(grad_output, grad_input)
-return grad_input
+    def forward(self, input1, input2):
+        output = torch.FloatTensor()
+        my_lib.my_lib_add_forward(input1, input2, output)
+        return output
+
+    def backward(self, grad_output):
+        grad_input = torch.FloatTensor()
+        my_lib.my_lib_add_backward(grad_output, grad_input)
+        return grad_input
 ```
 
 ```python
@@ -90,9 +88,8 @@ from torch.nn import Module
 from functions.add import MyAddFunction
 
 class MyAddModule(Module):
-
-def forward(self, input1, input2):
-return MyAddFunction()(input1, input2)
+    def forward(self, input1, input2):
+        return MyAddFunction()(input1, input2)
 ```
 
 ```python
@@ -102,13 +99,13 @@ from torch.autograd import Variable
 from modules.add import MyAddModule
 
 class MyNetwork(nn.Container):
-def __init__(self):
-super(MyNetwork, self).__init__(
-add=MyAddModule(),
-)
+    def __init__(self):
+        super(MyNetwork, self).__init__(
+            add=MyAddModule(),
+        )
 
-def forward(self, input1, input2):
-return self.add(input1, input2)
+    def forward(self, input1, input2):
+        return self.add(input1, input2)
 
 model = MyNetwork()
 input1, input2 = Variable(torch.randn(5, 5)), Variable(torch.randn(5, 5))