class_docstr: Container module with an encoder, a recurrent module, and a decoder.
class_docstr_tok: ["Container","module","with","an","encoder",",","a","recurrent","module",",","and","a","decoder","."]
code: def get_representation(
      self,
      strings: List[str],
      start_marker: str,
      end_marker: str,
      chars_per_chunk: int = 512,
  ):
      len_longest_str: int = len(max(strings, key=len))
  
      # pad strings with whitespaces to longest sentence
      padded_strings: List[str] = []
  
      for string in strings:
          if not self.is_forward_lm:
              string = string[::-1]
  
          padded = f"{start_marker}{string}{end_marker}"
          padded_strings.append(padded)
  
      # cut up the input into chunks of max charlength = chunk_size
      chunks = []
      splice_begin = 0
      longest_padded_str: int = (
          len_longest_str + len(start_marker) + len(end_marker)
      )
      for splice_end in range(
          chars_per_chunk, longest_padded_str, chars_per_chunk
      ):
          chunks.append(
              [text[splice_begin:splice_end] for text in padded_strings]
          )
          splice_begin = splice_end
  
      chunks.append(
          [
              text[splice_begin:longest_padded_str]
              for text in padded_strings
          ]
      )
      hidden = self.init_hidden(len(chunks[0]))
  
      padding_char_index = self.dictionary.get_idx_for_item(" ")
  
      batches: List[torch.Tensor] = []
      # push each chunk through the RNN language model
      for chunk in chunks:
          len_longest_chunk: int = len(max(chunk, key=len))
          sequences_as_char_indices: List[List[int]] = []
          for string in chunk:
              char_indices = self.dictionary.get_idx_for_items(
                  list(string)
              )
              char_indices += [padding_char_index] * (
                  len_longest_chunk - len(string)
              )
  
              sequences_as_char_indices.append(char_indices)
          t = torch.tensor(
              sequences_as_char_indices, dtype=torch.long
          ).to(device=flair.device, non_blocking=True)
          batches.append(t)
  
      output_parts = []
      for batch in batches:
          batch = batch.transpose(0, 1)
          rnn_output, hidden = self.forward(batch, hidden, decode=False)
          output_parts.append(rnn_output)
  
      # concatenate all chunks to make final output
      output = torch.cat(output_parts)
  
      return output
  
imports: import math
  from pathlib import Path
  from typing import List, Optional, Tuple, Union
  import torch
  from torch import logsumexp, nn
  from torch.optim import Optimizer
  import flair
  from flair.data import Dictionary
  from flair.nn.recurrent import create_recurrent_layer
label_desc: Use image node_id 6 for calling the LanguageModel obj's underlying member method code with example usage: obj.get_representation(strings, start_marker, end_marker, chars_per_chunk) and returns: output