// 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]);

      }
      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.");
    }

    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();
}

	ctrl, err := newTestController(ctx, nil, nil, false)
	if err != nil {
		t.Fatalf("Construct PersistentVolume controller failed: %v", err)
	}
	retVal := ctrl.provisionClaim(ctx, nil)
	if retVal != nil {
		t.Errorf("Expected nil return but got %v", retVal)
	}

			if err != nil {
				return err
			}
			retval := inject.IntoResourceFile(injector, templs, vc, rev, meshConfig,
				reader, writer, func(warning string) {
					warnings = append(warnings, warning)
				})
			if len(warnings) > 0 {
				fmt.Fprintln(c.ErrOrStderr())
			}
			for _, warning := range warnings {
				fmt.Fprintln(c.ErrOrStderr(), warning)
			}
			return retval
		},

		if resTyp == nil {
			code := InvalidImag
			if id == _Real {
				code = InvalidReal
			}
			check.errorf(x, code, invalidArg+"argument has type %s, expected complex type", x.typ)
			return
		}

		// if the argument is a constant, the result is a constant
		if x.mode == constant_ {
			if id == _Real {
				x.val = constant.Real(x.val)
			} else {
				x.val = constant.Imag(x.val)

    return op.emitOpError(
        llvm::formatv(kBadArrayAttrLengthMsg, tensorflow::kOutputShardingAttr,
                      op.getNumResults(), output_shardings.size()));

  // Set retvals metadata in proto.
  for (auto output_sharding_and_idx : llvm::enumerate(output_shardings))
    if (failed(SetOpSharding(op, output_sharding_and_idx.value(),
                             tensorflow::kOutputShardingAttr,