assertEquals("FOO", UPPER_CAMEL.to(UPPER_UNDERSCORE, "Foo"));
    assertEquals("FOO_BAR", UPPER_CAMEL.to(UPPER_UNDERSCORE, "FooBar"));
    assertEquals("H_T_T_P", UPPER_CAMEL.to(UPPER_UNDERSCORE, "HTTP"));
    assertEquals("H__T__T__P", UPPER_CAMEL.to(UPPER_UNDERSCORE, "H_T_T_P"));
  }

  public void testUpperUnderscoreToLowerHyphen() {
    assertEquals("foo", UPPER_UNDERSCORE.to(LOWER_HYPHEN, "FOO"));

    assertEquals("FOO", UPPER_CAMEL.to(UPPER_UNDERSCORE, "Foo"));
    assertEquals("FOO_BAR", UPPER_CAMEL.to(UPPER_UNDERSCORE, "FooBar"));
    assertEquals("H_T_T_P", UPPER_CAMEL.to(UPPER_UNDERSCORE, "HTTP"));
    assertEquals("H__T__T__P", UPPER_CAMEL.to(UPPER_UNDERSCORE, "H_T_T_P"));
  }

  public void testUpperUnderscoreToLowerHyphen() {
    assertEquals("foo", UPPER_UNDERSCORE.to(LOWER_HYPHEN, "FOO"));

     */
    AbstractTensorHandle* upstream_grad = grad_outputs[0];

    // Get transpose attrs
    bool t_a;
    TF_RETURN_IF_ERROR(forward_attrs_.Get("transpose_a", &t_a));

    bool t_b;
    TF_RETURN_IF_ERROR(forward_attrs_.Get("transpose_b", &t_b));

    // Conj each input
    AbstractTensorHandle* conj_output;
    std::string name = "Conj_A_MatMul_Grad";
    TF_RETURN_IF_ERROR(

  TF_DeleteTensor(a);
  TF_DeleteTensor(b);
}

TEST(CAPI, TestFromProto) {
  Tensor t_cc(DT_FLOAT, TensorShape({2, 3}));
  t_cc.flat<float>().setConstant(1.0);
  tensorflow::TensorProto t_proto;
  t_cc.AsProtoField(&t_proto);

  TF_Buffer* t_buffer = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(t_proto, t_buffer));

  const int num_bytes = 6 * sizeof(float);
  float* values =

-#  endif
-# endif
-
-__STRING_INLINE size_t __strcspn_c1 (const char *__s, int __reject);
-__STRING_INLINE size_t
-__strcspn_c1 (const char *__s, int __reject)
-{
-  size_t __result = 0;
-  while (__s[__result] != '\0' && __s[__result] != __reject)
-    ++__result;
-  return __result;
-}
-
-__STRING_INLINE size_t __strcspn_c2 (const char *__s, int __reject1,
-				     int __reject2);

    }

    /**
     * @throws Exception
     */
    @Test
    public void testTrimPrefix() throws Exception {
        assertEquals("AAA", StringUtil.trimPrefix("T_AAA", "T_"));
    }

    /**
     * @throws Exception
     */
    @Test
    public void testIsBlank() throws Exception {
        assertEquals("1", true, StringUtil.isBlank(" "));

index 98c080b..843bd45 100644
--- a/locale/xlocale.h
+++ b/locale/xlocale.h
@@ -20,6 +20,9 @@
 #ifndef _XLOCALE_H
 #define _XLOCALE_H	1
 
+#ifndef _BITS_TYPES___LOCALE_T_H
+#define _BITS_TYPES___LOCALE_T_H 1
+
 /* Structure for reentrant locale using functions.  This is an
    (almost) opaque type for the user level programs.  The file and
    this data structure is not standardized.  Don't rely on it.  It can

  handle_methods.summarize = &ParallelTensorSummarize;
  return TensorHandlePtr(TFE_NewCustomDeviceTensorHandle(
      context, parallel_device_name.c_str(), t_released->dtype(), t_released,
      handle_methods, status));
}

// For TFE_CustomDevice::copy_tensor_to_device in the parallel device
// registration.
//
// Since this function is used to satisfy the TFE_CustomDevice C API,

    assertEquals("training", Optional.of("training").get());
  }

  @SuppressWarnings("OptionalOfRedundantMethod") // Unit tests for Optional
  public void testOr_T_present() {
    assertEquals("a", Optional.of("a").or("default"));
  }

  public void testOr_T_absent() {
    assertEquals("default", Optional.absent().or("default"));
  }

  @SuppressWarnings("OptionalOfRedundantMethod") // Unit tests for Optional

    assertEquals("training", Optional.of("training").get());
  }

  @SuppressWarnings("OptionalOfRedundantMethod") // Unit tests for Optional
  public void testOr_T_present() {
    assertEquals("a", Optional.of("a").or("default"));
  }

  public void testOr_T_absent() {
    assertEquals("default", Optional.absent().or("default"));
  }

  @SuppressWarnings("OptionalOfRedundantMethod") // Unit tests for Optional