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.
docstr: src : InMemoryNumpyWordCharDataset
  tgt : InMemoryIndexedDataset
  weights: Optional[IndexedInMemoryDataset]
docstr_tok: ["src",":","InMemoryNumpyWordCharDataset","tgt",":","InMemoryIndexedDataset","weights",":","Optional","[","IndexedInMemoryDataset","]"]
code: def __init__(
      self,
      src,
      src_sizes,
      src_dict,
      tgt=None,
      tgt_sizes=None,
      tgt_dict=None,
      weights=None,
  ):
      """
      src : InMemoryNumpyWordCharDataset
      tgt : InMemoryIndexedDataset
      weights: Optional[IndexedInMemoryDataset]
      """
      super().__init__(
          src,
          src_sizes,
          src_dict,
          tgt,
          tgt_sizes,
          tgt_dict,
          left_pad_source=False,
          left_pad_target=False,
      )
      self.pad_idx = src_dict.pad()
      self.eos_idx = src_dict.eos()
      self.weights = weights
  
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 1 to create a new LanguagePairSourceCharDataset object from inherited base classes: data with example: obj = LanguagePairSourceCharDataset(src, src_sizes, src_dict, tgt, tgt_sizes, tgt_dict, weights)