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.

-     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;

                  .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());

  TF_Status* status = TF_NewStatus();

    absl::MutexLock l(&gcs_file->buffer_mutex);
    size_t buffer_end = gcs_file->buffer_start + gcs_file->buffer.size();
    size_t copy_size = 0;
    if (offset < buffer_end && gcs_file->buffer_start) {
      copy_size = (std::min)(n, static_cast<size_t>(buffer_end - offset));
      memcpy(buffer,
             gcs_file->buffer.data() + (offset - gcs_file->buffer_start),
             copy_size);
    }
    bool consumed_buffer_to_eof =

  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server)
                  .ok());
  ASSERT_TRUE(worker_server->Start().ok());

  TF_Status* status = TF_NewStatus();
  TFE_ContextOptions* opts = TFE_NewContextOptions();
  TFE_ContextOptionsSetAsync(opts, static_cast<unsigned char>(async));

    Start-Process -FilePath C:/TEMP/vs_buildtools.exe -ArgumentList "--installPath", "C:/VS", \
        "--quiet", "--wait", "--nocache", \
        "--add", "Microsoft.VisualStudio.Workload.VCTools", \
        "--add", "Microsoft.VisualStudio.Component.VC.Tools.x86.x64", \
        "--add", "Microsoft.VisualStudio.Component.Windows10SDK.19041" -Wait; \

# ==============================================================================
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

#   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:

    # 'Streaming build results to: ...' lines, one at the start, and one at the
    # end of the invocation.
    # If there's a failure message, it will be found in-between these two.

    if not current_url:
      url_lines['start'] = i
    elif current_url == url:
      url_lines['end'] = i
    else:
      result_store_links[current_url]['next_url'] = i
      url_lines['start'] = i
    current_url = url

  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server)
                  .ok());
  ASSERT_TRUE(worker_server->Start().ok());

  TF_Status* status = TF_NewStatus();
  TFE_ContextOptions* opts = TFE_NewContextOptions();
  TFE_ContextOptionsSetAsync(opts, static_cast<unsigned char>(async));
  TFE_ContextOptionsSetDevicePlacementPolicy(opts,