Allow the output of tf.argmax as index type

This fix tries to fix the issue raised in tensorflow#8951 where
the following will raise a `TypeError`:
```
a = tf.constant([1, 2, 3], dtype=tf.float32)
b = tf.argmax(a)
tf.Session().run(a[b])

TypeError: Input 'strides' of 'StridedSlice' Op has type int32 that does not match type int64 of argument 'begin'.
```
The reason for the erorr is that, `strides` is added as `append(1)`
without type while `begin` is appended with type.

The mismatch of `strides` and `begin` causes the error.

This fix fixes the issue by cast the stride with the same type
as `begin` when needed.

This issue was raised in tensorflow#8951. It was also raised earlier in
tensorflow#206 (comment)

This fix fixes tensorflow#8951.

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

tensorflow/python/ops/array_ops.py

@@ -470,7 +470,10 @@ def _SliceHelper(tensor, slice_spec, var=None):
     else:
       begin.append(s)
       end.append(s + 1)
-      strides.append(1)
+      if isinstance(s, ops.Tensor):
+        strides.append(constant(1, s.dtype))
+      else:
+        strides.append(np.ones_like(s).dtype.type(1))
       shrink_axis_mask |= (1 << index)
     index += 1