for _, tc := range testCases {
		actual := f([]byte(tc.in))
		if actual == nil && tc.out != nil {
			t.Errorf("%s(%q) = nil; want %q", funcName, tc.in, tc.out)
		}
		if actual != nil && tc.out == nil {
			t.Errorf("%s(%q) = %q; want nil", funcName, tc.in, actual)
		}
		if !Equal(actual, tc.out) {
			t.Errorf("%s(%q) = %q; want %q", funcName, tc.in, actual, tc.out)
		}
	}
}

    List<String> actual = Lists.newArrayList(filtered);
    assertEquals(expected, actual);
  }

  public void testFilterNoMatch() {
    Iterator<String> unfiltered = Lists.newArrayList("foo", "bar").iterator();
    Iterator<String> filtered = filter(unfiltered, Predicates.alwaysFalse());
    List<String> expected = emptyList();
    List<String> actual = Lists.newArrayList(filtered);

    List<String> actual = Lists.newArrayList(filtered);
    assertEquals(expected, actual);
  }

  public void testFilterNoMatch() {
    Iterator<String> unfiltered = Lists.newArrayList("foo", "bar").iterator();
    Iterator<String> filtered = filter(unfiltered, Predicates.alwaysFalse());
    List<String> expected = emptyList();
    List<String> actual = Lists.newArrayList(filtered);

alias(
    name = "is_cuda_enabled",
    actual = if_oss(
        "@local_config_cuda//:is_cuda_enabled",
        "@local_config_cuda//cuda:using_config_cuda",
    ),
)

# Config setting that is satisfied when CUDA device code should be compiled
# with clang. It does not imply that CUDA support has been enabled.
alias(
    name = "is_cuda_compiler_clang",
    actual = if_oss(

//
// - inherited from parent - this is the case for AssumeRole API, where the
// parent user is an actual real user with their own (permanent) credentials and
// policy association.
//
// - inherited from "virtual" parent - this is the case for AssumeRoleWithLDAP
// where the parent user is the DN of the actual LDAP user. The parent user
// itself cannot login, but the policy associated with them determines the base

				CalculatedSHA256: "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
			},
		},
		// Test case - 14.
		// Input with size more than the size of actual data inside the reader.
		{
			bucketName: bucket, objName: object, uploadID: uploadID, PartID: 1, inputReaderData: "abcd", inputMd5: "e2fc714c4727ee9395f324cd2e7f3335", inputDataSize: int64(len("abcd") + 1),

                       const std::unordered_set<string>& nodes) {
    ASSERT_EQ(nodes.size(), fdef.node_def_size())
        << "Got unexpected number of nodes. Expected: ["
        << absl::StrJoin(nodes, ", ")
        << "] Actual nodes in fdef: " << fdef.DebugString();
    for (const NodeDef& node_def : fdef.node_def()) {
      ASSERT_TRUE(nodes.find(node_def.name()) != nodes.end())
          << "Got unexpected node: " << node_def.name()

  private fun data(byteCount: Int): Buffer = Buffer().write(ByteArray(byteCount))

  private fun assertStreamData(
    expected: String?,
    source: Source?,
  ) {
    val actual = source!!.buffer().readUtf8()
    assertThat(actual).isEqualTo(expected)
  }

  /**
   * Returns true when all work currently in progress by the watchdog have completed. This method

   * list (counter-intuitive, but mathematically consistent).
   *
   * <p><i>Performance notes:</i> while the cartesian product of sets of size {@code m, n, p} is a
   * set of size {@code m x n x p}, its actual memory consumption is much smaller. When the
   * cartesian set is constructed, the input sets are merely copied. Only as the resulting set is

// unknown dimension is represented by `-1`.
//
// Returns an error into `status` if:
//   * `output` is not in `graph`.
//   * `num_dims` does not match the actual number of dimensions.
TF_CAPI_EXPORT extern void TF_GraphGetTensorShape(TF_Graph* graph,
                                                  TF_Output output,