class_docstr: RNN encoder.
class_docstr_tok: ["RNN","encoder","."]
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."""
      return int(1e5)
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.onnx.operators
  from fairseq import options, utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.modules import AdaptiveSoftmax
  from pytorch_translate import attention, rnn_cell, utils, vocab_reduction
  from pytorch_translate.common_layers import ContextEmbedding, DecoderWithOutputProjection, Embedding, Linear, RNNLayer, VariableTracker
  from pytorch_translate.data import data
  from pytorch_translate.multi_model import MultiDecoder, MultiEncoder
  from pytorch_translate.multilingual import MultilingualDecoder, MultilingualEncoder
  from pytorch_translate.ngram import NGramDecoder
  from pytorch_translate.semi_supervised import SemiSupervisedModel
  from pytorch_translate.utils import maybe_cat, maybe_cuda, torch_find
  from torch.nn.utils.rnn import pack_padded_sequence, PackedSequence, pad_packed_sequence
label_desc: Use image node_id 4 for calling the RNNEncoder obj's underlying member method code with example usage: obj.max_positions() and returns: int