class_docstr: Version of fairseq.data.LanguagePairDataset which represents source
  sentences as sequences of words, each represented as a sequence of
  characters (with numberized indices for both words and characters).
  Right-padded only.
code: def get_src_maybe_with_weights(self, i):
      example = {
          "id": i,
          "source_tokens": self.src.get_tokens(i).long(),
          "source_chars_list": self.src.get_chars_list(i),
      }
      if self.weights:
          """
          If weight for example is missing, use last seen weight. Sometimes we
          just want to assign a weight to the entire dataset with a single value
          but also maintain the IndexedInMemoryDataset convention of weights.
          This way, even if we don't care/know about dataset size, we can
          assign same weight to all examples.
          """
          if len(self.weights) <= i:
              example["weight"] = self.weights[-1]
          else:
              example["weight"] = self.weights[i]
      else:
          example["weight"] = 1.0
  
      return example
  
imports: from typing import Any, Dict
  import numpy
  import torch
  from fairseq import data, tokenizer
  from fvcore.common.file_io import PathManager
  from pytorch_translate import vocab_constants
  from pytorch_translate.data.dictionary import TAGS
label_desc: Use image node_id 2 for calling the LanguagePairSourceCharDataset obj's underlying member method code with example usage: obj.get_src_maybe_with_weights(i) and returns: example