.ok());
  ASSERT_TRUE(worker_server1->Start().ok());

  server_def.set_task_index(2);
  std::unique_ptr<tensorflow::GrpcServer> worker_server2;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server2)
                  .ok());
  ASSERT_TRUE(worker_server2->Start().ok());

  TFE_ContextSetServerDef(context.get(), 0, serialized.data(),

-     symbol's address relative to the PC. */
-
-  ElfW(Addr) static_addr;
-  ElfW(Addr) dynamic_addr;
-
-  asm ("					\n\
-	adrp	%1, _dl_start;			\n\
-        add	%1, %1, #:lo12:_dl_start        \n\
-        ldr	%w0, 1f				\n\
-	b	2f				\n\
-1:	.word	_dl_start			\n\
-2:						\n\
-       " : "=r" (static_addr),  "=r" (dynamic_addr));
-  return dynamic_addr - static_addr;

# ==============================================================================
setup_file() {
    cd /tf/tensorflow
    bazel version  # Start the bazel server
}

# Do a bazel query specifically for the licenses checker. It searches for
# targets matching the provided query, which start with // or @ but not
# //tensorflow (so it looks for //third_party, //external, etc.), and then

                                    /*remote_func_outputs=*/true);
}
TEST(CAPI, RemoteExecuteSilentCopiesLocalAsyncFuncOrdering) {
  // A remote input may be not ready when we start running a function. Test that
  // the function execution should wait until the remote input is ready.
  TestRemoteExecuteSilentCopiesFunc(/*async=*/true, /*remote=*/false,

// Some TF ops need a step container to be set to limit the lifetime of some
// resources (mostly TensorArray and Stack, used in while loop gradients in
// graph mode). Calling this on a context tells it to start a step.
TF_CAPI_EXPORT extern void TFE_ContextStartStep(TFE_Context* ctx);

// Ends a step. When there is no active step (that is, every started step has

automatically on GitHub.

If you want to contribute, start working through the TensorFlow codebase,
navigate to the
[GitHub "issues" tab](https://github.com/tensorflow/tensorflow/issues) and start
looking through interesting issues. If you are not sure of where to start, then
start by trying one of the smaller/easier issues here i.e.

#if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)
  status->status = tensorflow::errors::Unimplemented(
      "Server functionality is not supported on mobile");
#else
  status->status = server->server->Start();
#endif  // defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)
}

void TF_ServerStop(TF_Server* server, TF_Status* status) {
#if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)

#   which mounts your TensorFlow directory into the container.
#
#   Important: because the container is persistent, you cannot change TFCI
#   variables in between script executions. To forcibly remove the
#   container and start fresh, run "docker rm -f tf". Removing the container
#   destroys some temporary bazel data and causes longer builds.
#
#   You will need the NVIDIA Container Toolkit for GPU testing:

  }
  // Calculate the block-aligned start and end of the read.
  size_t start = block_size_ * (offset / block_size_);
  size_t finish = block_size_ * ((offset + n) / block_size_);
  if (finish < offset + n) {
    finish += block_size_;
  }
  size_t total_bytes_transferred = 0;
  // Now iterate through the blocks, reading them one at a time.
  for (size_t pos = start; pos < finish; pos += block_size_) {

// If heavy_load_on_streaming_rpc is true, send some rpc requests before the one
// which creates a remote input, to simulate a scenario that the remote input
// is not ready when we start running an op or a function.
void TestRemoteExecuteSilentCopies(bool async, bool remote, bool func,
                                   bool heavy_load_on_streaming_rpc,