class_docstr: A dataset that provides helpers for batching.
class_docstr_tok: ["A","dataset","that","provides","helpers","for","batching","."]
docstr: Merge a list of samples to form a mini-batch.
  
  Args:
      samples (List[dict]): samples to collate
  
  Returns:
      dict: a mini-batch suitable for forwarding with a Model
docstr_tok: ["Merge","a","list","of","samples","to","form","a","mini-batch",".","Args",":","samples","(","List","[","dict","]",")",":","samples","to","collate","Returns",":","dict",":","a","mini-batch","suitable","for","forwarding","with","a","Model"]
code: def collater(self, samples):
      """Merge a list of samples to form a mini-batch.
  
      Args:
          samples (List[dict]): samples to collate
  
      Returns:
          dict: a mini-batch suitable for forwarding with a Model
      """
      raise NotImplementedError
  
imports: import logging
  import numpy
  import torch.utils.data
  from fairseq.data import data_utils
label_desc: Use image node_id 3 for calling the FairseqDataset obj's underlying member method code with example usage: obj.collater(samples) without return types