code: def tile_encoder_out(
      self, tile_size: int, encoder_out: Dict[str, Tensor]
  ) -> Dict[str, Tensor]:
      tiled_out = torch.jit.annotate(Dict[str, Tensor], {})
  
      x = encoder_out["encoder_out"]
      new_x = x.repeat(1, tile_size, 1)
      tiled_out["encoder_out"] = new_x
  
      if "encoder_mask" in encoder_out:
          new_encoder_mask = encoder_out["encoder_mask"].repeat(
              tile_size, 1
          )
          tiled_out["encoder_mask"] = new_encoder_mask
      if "src_tokens" in encoder_out:
          tiled_out["src_tokens"] = encoder_out["src_tokens"].repeat(
              tile_size, 1
          )
      if "src_lengths" in encoder_out:
          tiled_out["src_lengths"] = encoder_out["src_lengths"].repeat(
              tile_size, 1
          )
  
      return tiled_out
  
imports: import math
  from typing import Dict, List, Optional, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from pytext.config import ConfigBase
  from pytext.config.module_config import ModuleConfig
  from pytext.models.module import create_module
  from pytext.models.representations.transformer import PositionalEmbedding
  from pytext.models.seq_models.base import PlaceholderAttentionIdentity
  from pytext.models.seq_models.positional import PostionalEmbedCombine, PostionalEmbedType, SinusoidalPositionalEmbedding
  from torch import Tensor
  from torch.nn import LayerNorm
  from .attention import MultiheadAttention
  from .base import PlaceholderIdentity, PyTextSeq2SeqModule
  from .light_conv import LightweightConv
  from .nar_modules import NAREncoderUtility
  from .utils import Linear
label_desc: Use image node_id 9 for calling the LightConvEncoder obj's underlying member method code with example usage: obj.tile_encoder_out(tile_size, encoder_out) and returns: tiled_out