class_docstr: A language pair dataset with multiple source sentences for
  each target sentence.
class_docstr_tok: ["A","language","pair","dataset","with","multiple","source","sentences","for","each","target","sentence","."]
code: def collate(
      samples,
      pad_idx,
      eos_idx,
      has_target=True,
      left_pad_source=True,
      left_pad_target=False,
  ):
      if len(samples) == 0:
          return {}
  
      n_sources = len(samples[0]["source"])
      assert all(
          len(sample["source"]) == n_sources for sample in samples
      ), "All samples in a batch must have the same number of source sentences."
  
      def merge(
          key, left_pad, source=False, move_eos_to_beginning=False
      ):
          if source:
              # Collate source sentences all source sentences together. Each
              return data.data_utils.collate_tokens(
                  [
                      s[key][src_id]
                      for s in samples
                      for src_id in range(n_sources)
                  ],
                  pad_idx,
                  eos_idx,
                  left_pad,
                  move_eos_to_beginning,
              )
          else:
              return data.data_utils.collate_tokens(
                  [s[key] for s in samples],
                  pad_idx,
                  eos_idx,
                  left_pad,
                  move_eos_to_beginning,
              )
  
      id = torch.LongTensor([s["id"] for s in samples])
      src_tokens = merge(
          "source", left_pad=left_pad_source, source=True
      )
      # We sort all source sentences from each batch element by length
      src_lengths = torch.LongTensor(
          [
              s["source"][src_id].numel()
              for s in samples
              for src_id in range(n_sources)
          ]
      )
      src_lengths, sort_order = src_lengths.sort(descending=True)
      # id = id.index_select(0, sort_order)
      src_tokens = src_tokens.index_select(0, sort_order)
      # Record which sentence corresponds to which source and sample
      _, rev_order = sort_order.sort()
      # srcs_ids[k] contains the indices of kth source sentences of each
      # sample in src_tokens
      srcs_ids = [rev_order[k::n_sources] for k in range(n_sources)]
  
      prev_output_tokens = None
      target = None
      ntokens = None
      if has_target:
          target = merge("target", left_pad=left_pad_target)
          # we create a shifted version of targets for feeding the
          # previous output token(s) into the next decoder step
          prev_output_tokens = merge(
              "target",
              left_pad=left_pad_target,
              move_eos_to_beginning=True,
          )
          ntokens = sum(len(s["target"]) for s in samples)
  
      return {
          "id": id,
          "ntokens": ntokens,
          "net_input": {
              "src_tokens": src_tokens,
              "src_lengths": src_lengths,
              "src_ids": srcs_ids,
              "prev_output_tokens": prev_output_tokens,
          },
          "target": target,
      }
  
file_path: translate/pytorch_translate/research/multisource/multisource_data.py
imports: import numpy
  import torch
  from fairseq import data
label_desc: Use image node_id 3 for calling the MultisourceLanguagePairDataset obj's underlying member method code with example usage: obj.collate(samples, pad_idx, eos_idx, has_target, left_pad_source, left_pad_target) and returns: dict, dict, data, data