code: def prepare_for_nar_inference(
      self, length_beam_size: int, encoder_out: Dict[str, Tensor]
  ) -> Dict[str, Tensor]:
      """
      During masked NAR inference, multiple lengths are predicted for each
      item in the batch. Hence tiling has to be done in such a way that all new
      rows related to each item should be placed together. This is the assumption
      that we are following in the rest of the nar generation code.
      Eg:
      [row1, row2, row3] should be tiled as
      [row1, row1, row1, row2, row2, row2, row3, row3, row3]
      NOT [row1, row2, row3, row1, row2, row3, row1, row2, row3]
      """
      tiled_out = torch.jit.annotate(Dict[str, Tensor], {})
      bsz = encoder_out["encoder_out"].size(1)
      x = encoder_out["encoder_out"]
      new_x = (
          x.unsqueeze(2)
          .repeat(1, 1, length_beam_size, 1)
          .view(-1, bsz * length_beam_size, x.size(-1))
      )
      tiled_out["encoder_out"] = new_x
  
      if "encoder_mask" in encoder_out:
          new_encoder_mask = (
              encoder_out["encoder_mask"]
              .unsqueeze(1)
              .repeat(1, length_beam_size, 1)
              .view(bsz * length_beam_size, -1)
          )
          tiled_out["encoder_mask"] = new_encoder_mask
  
      if "src_tokens" in encoder_out:
          new_src_tokens = (
              encoder_out["src_tokens"]
              .unsqueeze(1)
              .repeat(1, length_beam_size, 1)
              .view(bsz * length_beam_size, -1)
          )
          tiled_out["src_tokens"] = new_src_tokens
  
      if "src_subword_begin_indices" in encoder_out:
          new_src_subword_begin_indices = (
              encoder_out["src_subword_begin_indices"]
              .unsqueeze(1)
              .repeat(1, length_beam_size, 1)
              .view(bsz * length_beam_size, -1)
          )
          tiled_out[
              "src_subword_begin_indices"
          ] = new_src_subword_begin_indices
  
      if "src_lengths" in encoder_out:
          new_src_lengths = (
              encoder_out["src_lengths"]
              .reshape(bsz, 1)
              .unsqueeze(0)
              .repeat(1, length_beam_size, 1)
              .view(bsz * length_beam_size, -1)
          )
          tiled_out["src_lengths"] = new_src_lengths
  
      if "src_index_tokens" in encoder_out:
          new_src_tokens = (
              encoder_out["src_index_tokens"]
              .unsqueeze(1)
              .repeat(1, length_beam_size, 1)
              .view(bsz * length_beam_size, -1)
          )
          tiled_out["src_index_tokens"] = new_src_tokens
  
      return tiled_out
  
imports: from typing import Dict
  import torch
  from torch import Tensor
label_desc: Use image node_id 1 for calling the NAREncoderUtility obj's underlying member method code with example usage: obj.prepare_for_nar_inference(length_beam_size, encoder_out) and returns: tiled_out