class_docstr: CNN based representation of a document.
class_docstr_tok: ["CNN","based","representation","of","a","document","."]
code: def extra_repr(self):
      s = "dropout={}, embed_scale={}, normalize={}".format(
          self.dropout, self.embed_scale, self.normalize
      )
      return s
  
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 9 for calling the LightConvRepresentation obj's underlying member method code with example usage: obj.extra_repr() and returns: s