class_docstr: CNN based representation of a document.
class_docstr_tok: ["CNN","based","representation","of","a","document","."]
docstr: Maximum input length supported by the encoder.
docstr_tok: ["Maximum","input","length","supported","by","the","encoder","."]
code: def max_positions(self):
      """Maximum input length supported by the encoder."""
      if self.no_token_positional_embeddings:
          return self.max_source_positions
      return min(
          self.max_source_positions, self.embed_positions.max_positions
      )
  
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 7 for calling the LightConvRepresentation obj's underlying member method code with example usage: obj.max_positions() and returns: min, self