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, 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 8 for calling the LightConvEncoder obj's underlying member method code with example usage: obj.max_positions() and returns: min, self