class_docstr: CNN based representation of a document.
class_docstr_tok: ["CNN","based","representation","of","a","document","."]
code: def _concat_pos_embed(self, x, src_tokens):
      pos_embed = self.embed_positions(src_tokens)
      return torch.cat([x, pos_embed], dim=2)
  
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 6 for calling the LightConvRepresentation obj's underlying member method code with example usage: obj._concat_pos_embed(x, src_tokens) and returns: torch