"//third_party/cloud_tpu/convergence_tools/sdc_monitoring/...",
        "//third_party/cloud_tpu/inference_converter/...",
        "//third_party/py/cloud_ml_autoflow/...",
        "//third_party/py/envlogger/...",
        "//third_party/py/gldm/...",
        "//third_party/py/guesslang/...",
        "//third_party/py/keras/...",
        "//third_party/py/tf_keras/...",

kubectl get thirdpartyresource --all-namespaces -o yaml > tprs.yaml
kubectl delete -f tprs.yaml

After upgrading to 1.3.0, re-register the third party resource objects at the root scope (using a 1.3 server and client):

kubectl create -f tprs.yaml

#### kubectl

  if is_windows():
    if int(environ_cp.get('TF_NEED_TENSORRT', False)):
      cuda_libraries.append('tensorrt')
      print('WARNING: TensorRT support on Windows is experimental\n')

  paths = glob.glob('**/third_party/gpus/find_cuda_config.py', recursive=True)
  if not paths:
    raise FileNotFoundError(
        "Can't find 'find_cuda_config.py' script inside working directory")
  proc = subprocess.Popen(

		t.Errorf("writeString: expected %q, got %q", writeString, w.writeString)
	}
}

func TestBufferFull(t *testing.T) {
	const longString = "And now, hello, world! It is the time for all good men to come to the aid of their party"
	buf := NewReaderSize(strings.NewReader(longString), minReadBufferSize)
	line, err := buf.ReadSlice('!')
	if string(line) != "And now, hello, " || err != ErrBufferFull {
		t.Errorf("first ReadSlice(,) = %q, %v", line, err)

#     dynamic_kernels:  Try to link all kernels dynamically (experimental).
#     dbg:              Build with debug info
#
# TF version options;
#     v2: Build TF v2
#
# Feature and Third party library support options:
#     xla:          Build TF with XLA
#     tpu:          Build TF with TPU support
#     cuda:         Build with CUDA support.
#     cuda_clang    Build with CUDA Clang support.