docstr: Maximum output length supported by the encoder.
docstr_tok: ["Maximum","output","length","supported","by","the","encoder","."]
code: def max_positions(self):
      """Maximum output length supported by the encoder."""
      return self.args.max_positions
  
imports: import math
  from dataclasses import dataclass, field
  from typing import List, Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.data.data_utils import compute_mask_indices
  from fairseq.dataclass import ChoiceEnum, FairseqDataclass
  from fairseq.distributed import fsdp_wrap
  from fairseq.models import BaseFairseqModel, register_model
  from fairseq.distributed.fully_sharded_data_parallel import FullyShardedDataParallel
  from fairseq.modules import Fp32GroupNorm, Fp32LayerNorm, GradMultiply, GumbelVectorQuantizer, LayerNorm, MultiheadAttention, RelPositionalEncoding, SamePad, TransposeLast
  from fairseq.modules.checkpoint_activations import checkpoint_wrapper
  from fairseq.modules.conformer_layer import ConformerWav2Vec2EncoderLayer
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
  from fairseq.utils import buffered_arange, index_put, is_xla_tensor
  from .utils import pad_to_multiple
label_desc: Use image node_id 5 for calling the TransformerEncoder obj's underlying member method code with example usage: obj.max_positions() and returns: self