// Validate NodeDef is what we expect.
  EXPECT_TRUE(IsNeg(node_def, "add"));

  // Serialize to GraphDef.
  GraphDef graph_def2;
  ASSERT_TRUE(GetGraphDef(graph, &graph_def2));

  // Compare with first GraphDef + added NodeDef.
  NodeDef* added_node = graph_def.add_node();
  *added_node = node_def;
  EXPECT_EQ(graph_def.DebugString(), graph_def2.DebugString());

  // Look up some nodes by name.

  FileSystem* fs = nullptr;
  Status s = env_->GetFileSystemForFile(generic_path, &fs);
  if (fs == nullptr || !s.ok())
    GTEST_SKIP() << "No filesystem registered: " << s;

  // First, test some interesting corner cases concerning empty URIs
  if (GetParam().empty()) {
    EXPECT_EQ(fs->TranslateName(""), "");
    EXPECT_EQ(fs->TranslateName("/"), "/");
    EXPECT_EQ(fs->TranslateName("//"), "/");

  job_def2->set_name("worker");

  // Copy over `host:port` at `task_index`
  for (auto task : cluster_server_def.cluster().job(0).tasks()) {
    if (task.first == task_index) {
      job_def2->mutable_tasks()->insert({task.first, task.second});
    }
  }

  return single_host_server_def;
}

tensorflow::ServerDef GetClusterServerDef(const string& worker_job_name,

  Define(-1, {}, {feed1, feed2}, {feed2, feed1}, {"first", "second"});

  // Use, run, and verify
  TF_Operation* two = ScalarConst(2, host_graph_, s_);
  TF_Operation* func_feed = Placeholder(host_graph_, s_);
  TF_Operation* func_op = Use({two, func_feed});
  Run({{func_feed, Int32Tensor(3)}}, {{func_op, 0}, {func_op, 1}}, {3, 2});
  VerifyFDef(empty_, M({{"feed1"}, {"feed2"}}), M({{"first"}, {"second"}}),

/// `ReadOnlyMemoryRegion`) and one for all the operations a `Filesystem`
/// implements. Each of them is in a 1-to-1 correspondence with the wrapper
/// structures from the first section: these tables only contain function
/// pointers that operate on the corresponding data. Thus, the first argument of
/// each of these functions is a pointer to the paired struct and this argument
/// can be used to track state in between calls (from an object oriented point

def get_ndk_api_level(environ_cp, android_ndk_home_path):
  """Gets the appropriate NDK API level to use for the provided Android NDK path.
  """

  # First check to see if we're using a blessed version of the NDK.
  properties_path = '%s/source.properties' % android_ndk_home_path
  if is_windows() or is_cygwin():
    properties_path = cygpath(properties_path)

#     release_cpu_macos:               Toolchain and CUDA options for MacOS CPU builds.
#     release_cpu_windows:             Toolchain and CUDA options for Windows CPU builds.

# Default build options. These are applied first and unconditionally.

# For projects which use TensorFlow as part of a Bazel build process, putting
# nothing in a bazelrc will default to a monolithic build. The following line

//       .Output("output: T")
//       .Attr("N: int >= 2")
//       .Attr("T: type");
// that defines two inputs, "concat_dim" and "values" (in that order).
// You must use TF_AddInput() for the first input (since it takes a
// single tensor), and TF_AddInputList() for the second input (since
// it takes a list, even if you were to pass a list with a single
// tensor), as in: