// Note: this list must be updated if ever kubelet wants to allow mutations to other fields.
func pickFieldsToHash(container *v1.Container) map[string]string {
	retval := map[string]string{
		"name":  container.Name,
		"image": container.Image,
	}
	return retval
}

// envVarsToMap constructs a map of environment name to value from a slice
// of env vars.
func envVarsToMap(envs []EnvVar) map[string]string {

                             int* num_retvals) override {
    std::vector<TFE_TensorHandle*> outputs(*num_retvals);
    TF_Status status;
    device_.execute(tensorflow::wrap(op), num_retvals, outputs.data(), &status,
                    info_);
    if (status.status.ok()) {
      for (int i = 0; i < *num_retvals; ++i) {
        retvals[i] = tensorflow::unwrap(outputs[i]);

	// universe scope
	_Append builtinId = iota
	_Cap
	_Clear
	_Close
	_Complex
	_Copy
	_Delete
	_Imag
	_Len
	_Make
	_Max
	_Min
	_New
	_Panic
	_Print
	_Println
	_Real
	_Recover

	// package unsafe
	_Add
	_Alignof
	_Offsetof
	_Sizeof
	_Slice
	_SliceData
	_String
	_StringData

	// testing support
	_Assert
	_Trace
)

	// universe scope
	_Append builtinId = iota
	_Cap
	_Clear
	_Close
	_Complex
	_Copy
	_Delete
	_Imag
	_Len
	_Make
	_Max
	_Min
	_New
	_Panic
	_Print
	_Println
	_Real
	_Recover

	// package unsafe
	_Add
	_Alignof
	_Offsetof
	_Sizeof
	_Slice
	_SliceData
	_String
	_StringData

	// testing support
	_Assert
	_Trace
)

            tile_assignment_dimensions: 1
            tile_assignment_devices: 0
            tile_assignment_devices: 1
          }
          is_bounded_dynamic_dim: false
        }
        retvals { sharding {} }
        num_replicas: 1
        num_cores_per_replica: 2
        use_spmd_for_xla_partitioning: true
        compile_options {}
      )pb",
      &expected_compile_metadata));

      }
      tf_outputs[i] = t->output_;
    }
    TF_AddInputList(op_.get(), tf_outputs.data(), tf_outputs.size());
    return absl::OkStatus();
  }
  Status Execute(absl::Span<AbstractTensorHandle*> retvals,
                 int* num_retvals) override {
    auto* tf_opdesc = op_.release();
    if (tf_opdesc == nullptr) {
      return errors::InvalidArgument("AbstractOp is incomplete.");
    }

	}
	return strings.Join(matches, ", ")
}

func renderMatch(match *v1alpha3.HTTPMatchRequest) string {
	retval := "Match: "
	// TODO Are users interested in seeing Scheme, Method, Authority?
	if match.Uri != nil {
		retval += renderStringMatch(match.Uri)

		if match.IgnoreUriCase {
			retval += " uncased"
		}
	}

	if len(match.Headers) > 0 {
		headerConds := []string{}

    func.func @main(%arg0: tensor<3x3x10xbf16>, %arg1: tensor<3xi32>) -> tensor<1x?x4xbf16> attributes {allow_soft_placement = false, tf.entry_function = {control_outputs = "", inputs = "_arg0,_arg1,_arg2", outputs = "_retval0"}} {
      %cst = "tf.Const"() {value = dense<[1, -1, 4]> : tensor<3xi32>} : () -> tensor<3xi32>

  llvm::SmallVector<std::tuple<int64_t, int64_t, bool>, 8>
      output_buffer_to_size;
  for (auto retval : llvm::enumerate(old_terminator->getOperands())) {
    auto it = buffer_to_size.find(retval.value());
    if (it == buffer_to_size.end()) continue;
    output_buffer_to_size.emplace_back(retval.index(), new_outputs.size(),
                                       it->getSecond().fixed);

}

Status MlirAbstractOp::Execute(absl::Span<AbstractTensorHandle*> retvals,
                               int* num_retvals) {
  OperationState* state;
  TF_RETURN_IF_ERROR(Create(operands_, &state));
  Operation* op = function_context_->CreateOperationFromState(*state);
  *num_retvals = op->getNumResults();
  for (int i = 0; i < *num_retvals; i++)
    retvals[i] = new MlirTensor(op->getResult(i));
  return absl::OkStatus();
}