class ImmediateExecutionOperation : public AbstractOperation {
 public:
  virtual void Clear() = 0;

  // Returns the inputs of this op.
  virtual absl::Span<ImmediateExecutionTensorHandle* const> GetInputs()
      const = 0;
  virtual Status SetInput(size_t index,
                          ImmediateExecutionTensorHandle* input) = 0;

  virtual ImmediateExecutionContext* GetContext() const = 0;

  if (use_region_args) {
    auto inputs = func.getFunctionType().getInputs();
    entry->addArguments(inputs, SmallVector<Location>(inputs.size(), loc));
    args = entry->getArguments();
  }
  llvm::SmallVector<Value, 4> casted_args;
  casted_args.reserve(func.getNumArguments());
  for (const auto& ArgAndType : zip(args, func.getFunctionType().getInputs())) {
    Value arg = std::get<0>(ArgAndType);

        }

        @Override
        public ModelReference<?> getSubject() {
            return targetReference;
        }

        @Override
        public List<? extends ModelReference<?>> getInputs() {
            return inputs;
        }

        @Override
        public void execute(ModelRuleInvoker<?> invoker, MutableModelNode subjectNode, List<ModelView<?>> inputs) {

void CreateRegionWithCall(func::FuncOp func, Region& region, Location loc) {
  OpBuilder builder(region);
  auto block = builder.createBlock(&region);
  SmallVector<Value, 4> new_operands;
  for (Type t : func.getFunctionType().getInputs())
    new_operands.push_back(block->addArgument(t, loc));
  auto call = builder.create<func::CallOp>(loc, func, new_operands);
  builder.create<YieldOp>(loc, call.getResults());

  if (!main_fn) {
    return absl::InternalError("Could not find main function in MLIR Module.");
  }

  mlir::FunctionType func_type = main_fn.getFunctionType();
  for (auto input_type : func_type.getInputs()) {
    tensorflow::TensorShape tensor_shape;
    xla::Shape xla_shape = xla::TypeToShape(input_type);
    TF_RETURN_IF_ERROR(tensorflow::TensorShape::BuildTensorShape(
        xla_shape.dimensions(), &tensor_shape));

  // If there are multiple platforms, the first argument is reserved for
  // passing the platform index.
  FunctionType function_type = func_op.getFunctionType();
  ArrayRef<Type> new_input_types =
      function_type.getInputs().take_back(func_op.getNumArguments() - 1);
  func_op.setFunctionType(
      FunctionType::get(ctx, new_input_types, function_type.getResults()));
  func_op.getBody().eraseArgument(0);
}

}  // namespace

    @Internal
    Optional<String> getReasonTaskIsIncompatibleWithConfigurationCache();

    @Internal
    StandardOutputCapture getStandardOutputCapture();

    @Override
    TaskInputsInternal getInputs();

    @Override
    TaskOutputsInternal getOutputs();

    @Override
    TaskStateInternal getState();

    @Internal
    boolean getImpliesSubProjects();

  // orig_op, except the op name.
  auto* new_op = new EagerOperation(&ctx);
  TF_RETURN_IF_ERROR(
      new_op->Reset(fname.c_str(), orig_op->DeviceName().c_str()));
  for (auto input : orig_op->GetInputs()) {
    TF_RETURN_IF_ERROR(new_op->AddInput(input));
  }
  new_op->MutableAttrs()->CopyAttributes(orig_op->Attrs());
  out_op->reset(new_op);

  LOG_FIRST_N(INFO, 1)

    return false;
  }
  return true;
}

bool ValidateReplicatedInput(TF::TPUReplicatedInputOp rep, int num_replicas,
                             StringAttr attr) {
  int arity = rep.getInputs().size();
  if (rep.getIsPacked() && arity != 1) {
    rep.emitOpError(
        "TF2XLA TPU bridge input check: packed with number of inputs not 1.")
        << " num_replicas=" << num_replicas << " no. of inputs=" << arity;

        }

        public void setBuild_time(List<Long> build_time) {
            this.build_time = build_time;
        }

        public Map<String, List> getInputs() {
            return inputs;
        }

        public void setInputs(Map<String, List> inputs) {
            this.inputs = inputs;
        }

        @Override
        public String toString() {
            return "SwiftDeps{" +