// Verify that FunctionDef ArgDef has attributes.
  ASSERT_EQ(func_->record->fdef().arg_attr_size(), 1);
  auto arg_attrs = func_->record->fdef().arg_attr().find(0);
  ASSERT_NE(arg_attrs, func_->record->fdef().arg_attr().end());
  auto iter = arg_attrs->second.attr().find("_test_attr");
  ASSERT_NE(iter, arg_attrs->second.attr().end());
  EXPECT_EQ(iter->second.s(), "value");
}

			fmt.Fprintf(&sb, "var _cgo%d %s = %s; ", i,
				gofmtLine(ptype), gofmtPos(arg, origArg.Pos()))
			continue
		}

		// Check for &a[i].
		if p.checkIndex(&sb, &sbCheck, arg, i) {
			continue
		}

		// Check for &x.
		if p.checkAddr(&sb, &sbCheck, arg, i) {
			continue
		}

		// Check for a[:].
		if p.checkSlice(&sb, &sbCheck, arg, i) {
			continue
		}

    ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

    auto cpu_arg =
        tensorflow::TensorHandleFromInterface(tensorflow::unwrap(hcpu));
    auto gpu_arg =
        tensorflow::TensorHandleFromInterface(tensorflow::unwrap(hgpu));
    auto gpu_device = gpu_arg->device();
    ASSERT_FALSE(cpu_arg->HasLocalMirror(gpu_device));

    TFE_Op* matmul = MatMulOp(ctx, hcpu, hgpu);
    if (cpu_op) {

		if f == nil && fb == nil {
			continue
		}
		var actual string
		if f != nil {
			actual = string(f([]byte(tc.in), tc.arg))
		} else {
			actual = string(fb([]byte(tc.in), []byte(tc.arg)))
		}
		if actual != tc.out {
			t.Errorf("%s(%q, %q) = %q; want %q", name, tc.in, tc.arg, actual, tc.out)
		}
	}

	for _, tc := range trimNilTests {
		name := tc.f
		f, fb := toFn(name)

		if !(strings.Contains(poolsArgs, "{") && strings.Contains(poolsArgs, "}")) {
			// No ellipses pattern. Anonymize host name from every pool arg
			pools := strings.Fields(poolsArgs)
			anonPools = make([]string, len(pools))
			for index, arg := range pools {
				anonPools[index] = anonAddr(arg)
			}
			return cmdLineWithoutPools + strings.Join(anonPools, " ")
		}

		// Ellipses pattern in pool args. Regex groups:

//   are not optimizing for convenience.
// * We make assumption that one session has one graph.  This should be
//   fine since we have the ability to run sub-graphs.
// * We could allow NULL for some arguments (e.g., NULL options arg).
//   However since convenience is not a primary goal, we don't do this.
// * Devices are not in this API.  Instead, they are created/used internally
//   and the API just provides high level controls over the number of

    assertEquals(reserialized, substitute(reserialized, String.class));
  }

  private static <T, X> TypeToken<T> substitute(TypeToken<T> type, Class<X> arg) {
    return type.where(new TypeParameter<X>() {}, arg);
  }

  private abstract static class ToReproduceGenericSignatureFormatError<V> {
    private abstract class BaseOuter {
      abstract class BaseInner {}
    }

    assertEquals(reserialized, substitute(reserialized, String.class));
  }

  private static <T, X> TypeToken<T> substitute(TypeToken<T> type, Class<X> arg) {
    return type.where(new TypeParameter<X>() {}, arg);
  }

  private abstract static class ToReproduceGenericSignatureFormatError<V> {
    private abstract class BaseOuter {
      abstract class BaseInner {}
    }

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  EXPECT_EQ(TF_CANCELLED, TF_GetCode(s));
  EXPECT_EQ(string("cancel"), TF_Message(s));
  TF_DeleteStatus(s);
}

void Deallocator(void* data, size_t, void* arg) {
  tensorflow::cpu_allocator()->DeallocateRaw(data);
  *reinterpret_cast<bool*>(arg) = true;
}

TEST(CAPI, Tensor) {
  const int num_bytes = 6 * sizeof(float);
  float* values =
      reinterpret_cast<float*>(tensorflow::cpu_allocator()->AllocateRaw(