class_docstr: A pair of torch.utils.data.Datasets.
  
  Args:
      src (torch.utils.data.Dataset): source dataset to wrap
      src_sizes (List[int]): source sentence lengths
      src_dict (~fairseq.data.Dictionary): source vocabulary
      tgt (torch.utils.data.Dataset, optional): target dataset to wrap
      tgt_sizes (List[int], optional): target sentence lengths
      tgt_dict (~fairseq.data.Dictionary, optional): target vocabulary
      left_pad_source (bool, optional): pad source tensors on the left side
          (default: True).
      left_pad_target (bool, optional): pad target tensors on the left side
          (default: False).
      shuffle (bool, optional): shuffle dataset elements before batching
          (default: True).
      input_feeding (bool, optional): create a shifted version of the targets
          to be passed into the model for teacher forcing (default: True).
      remove_eos_from_source (bool, optional): if set, removes eos from end
          of source if it's present (default: False).
      append_eos_to_target (bool, optional): if set, appends eos to end of
          target if it's absent (default: False).
      align_dataset (torch.utils.data.Dataset, optional): dataset
          containing alignments.
      constraints (Tensor, optional): 2d tensor with a concatenated, zero-
          delimited list of constraints for each sentence.
      append_bos (bool, optional): if set, appends bos to the beginning of
          source/target sentence.
      num_buckets (int, optional): if set to a value greater than 0, then
          batches will be bucketed into the given number of batch shapes.
      src_lang_id (int, optional): source language ID, if set, the collated batch
          will contain a field 'src_lang_id' in 'net_input' which indicates the
          source language of the samples.
      tgt_lang_id (int, optional): target language ID, if set, the collated batch
          will contain a field 'tgt_lang_id' which indicates the target language
           of the samples.
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code: def get_batch_shapes(self):
      return self.buckets
  
file_path: fairseq/fairseq/data/language_pair_dataset.py
imports: import logging
  import numpy
  import torch
  from fairseq.data import FairseqDataset, data_utils
label_desc: Use image node_id 2 for calling the LanguagePairDataset obj's underlying member method code with example usage: obj.get_batch_shapes() and returns: self