*   [Python license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/nn.py#L1)
*   [Java license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/java/src/main/java/org/tensorflow/Graph.java#L1)
*   [Go license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/go/operation.go#L1)

-		      }							      \
-		    __result; }))
-
-# define __strcmp_cg(s1, s2, l1) \
-  (__extension__ ({ const unsigned char *__s2 =				      \
-		      (const unsigned char *) (const char *) (s2);	      \
-		    int __result =					      \
-		      (((const unsigned char *) (const char *) (s1))[0]	      \
-		       - __s2[0]);					      \
-		    if (l1 > 0 && __result == 0)			      \
-		      {							      \

	// limit (8KB), we stop growing the source string once the limit
	// is reached and keep reusing the same source string - that
	// should therefore be always resident in the L1 cache - until we
	// have completed the construction of the result.
	// This yields significant speedups (up to +100%) in cases where
	// the result length is large (roughly, over L2 cache size).
	const chunkLimit = 8 * 1024

For instance, this example:
</p>

<pre>
if n%2 == 1 {
	goto L1
}
for n &gt; 0 {
	f()
	n--
L1:
	f()
	n--
}
</pre>

<p>
is erroneous because the label <code>L1</code> is inside
the "for" statement's block but the <code>goto</code> is not.
</p>

For instance, this example:
</p>

<pre>
if n%2 == 1 {
	goto L1
}
for n &gt; 0 {
	f()
	n--
L1:
	f()
	n--
}
</pre>

<p>
is erroneous because the label <code>L1</code> is inside
the "for" statement's block but the <code>goto</code> is not.
</p>

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