class_docstr: CNN based representation of a document.
class_docstr_tok: ["CNN","based","representation","of","a","document","."]
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
  import torch
  import torch.nn
  from fairseq.modules import LayerNorm, PositionalEmbedding
  from pytext.models.module import create_module
  from pytext.models.seq_models.base import PlaceholderIdentity
  from pytext.models.seq_models.conv_encoder import ConvEncoderConfig, LightConvEncoderLayer
  from pytext.models.seq_models.positional import PostionalEmbedCombine, PostionalEmbedType, SinusoidalPositionalEmbedding
  from pytext.utils.usage import log_class_usage
  from torch import Tensor
  from torch.nn import Linear
  from .representation_base import RepresentationBase
label_desc: Use image node_id 8 for calling the LightConvRepresentation obj's underlying member method code with example usage: obj.tile_encoder_out(tile_size, encoder_out) and returns: tiled_out