class_docstr: Sequence to Sequence Dataset in memory.
class_docstr_tok: ["Sequence","to","Sequence","Dataset","in","memory","."]
code: def __init__(
      self,
      src_file_name: str,
      tgt_file_name: str,
      src_tokenizer: TokenizerSpec,
      tgt_tokenizer: TokenizerSpec,
      max_src_seq_length: int,
      max_tgt_seq_length: int,
      add_bos_to_input: bool = True,
      add_eos_to_input: bool = True,
      replace_bos_with_pad: bool = False,
  ):
      super().__init__()
      self.src_file_name = src_file_name
      self.tgt_file_name = tgt_file_name
      self.src_tokenizer = src_tokenizer
      self.tgt_tokenizer = tgt_tokenizer
      self.max_src_seq_length = max_src_seq_length
      self.max_tgt_seq_length = max_tgt_seq_length
      self.add_bos_to_input = add_bos_to_input
      self.add_eos_to_input = add_eos_to_input
      self.replace_bos_with_pad = replace_bos_with_pad
      assert self.max_src_seq_length > 0
      assert self.max_tgt_seq_length > 0
      self._check_files_exist()
      self._get_examples()
  
imports: import os
  import numpy
  import torch
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
  from nemo.collections.nlp.data.language_modeling.megatron.dataset_utils import get_indexed_dataset_, get_samples_mapping
  from nemo.collections.nlp.data.language_modeling.text_memmap_dataset import TextMemMapDataset
  from nemo.core.classes import Dataset
  from nemo.utils import logging
label_desc: Use image node_id 1 to create a new SequenceToSequenceDataset object from inherited base classes: Dataset with example: obj = SequenceToSequenceDataset(src_file_name, tgt_file_name, src_tokenizer, tgt_tokenizer, max_src_seq_length, max_tgt_seq_length, add_bos_to_input, add_eos_to_input, replace_bos_with_pad)