class_docstr: BatchSampler with constant batch-size.
  
  Any sorting is not done in this class,
  so no length information is required,
  This class is convenient for decoding mode,
  or not seq2seq learning e.g. classification.
  
  Args:
      batch_size:
      key_file:
class_docstr_tok: ["BatchSampler","with","constant","batch-size",".","Any","sorting","is","not","done","in","this","class",",","so","no","length","information","is","required",",","This","class","is","convenient","for","decoding","mode",",","or","not","seq2seq","learning","e.g",".","classification",".","Args",":","batch_size",":","key_file",":"]
code: def __init__(
      self,
      batch_size: int,
      key_file: str,
      drop_last: bool = False,
      utt2category_file: str = None,
  ):
      assert check_argument_types()
      assert batch_size > 0
      self.batch_size = batch_size
      self.key_file = key_file
      self.drop_last = drop_last
  
      # utt2shape:
      #    uttA <anything is o.k>
      #    uttB <anything is o.k>
      utt2any = read_2columns_text(key_file)
      if len(utt2any) == 0:
          logging.warning(f"{key_file} is empty")
      # In this case the, the first column in only used
      keys = list(utt2any)
      if len(keys) == 0:
          raise RuntimeError(f"0 lines found: {key_file}")
  
      category2utt = {}
      if utt2category_file is not None:
          utt2category = read_2columns_text(utt2category_file)
          if set(utt2category) != set(keys):
              raise RuntimeError(
                  f"keys are mismatched between {utt2category_file} != {key_file}"
              )
          for k, v in utt2category.items():
              category2utt.setdefault(v, []).append(k)
      else:
          category2utt["default_category"] = keys
  
      self.batch_list = []
      for d, v in category2utt.items():
          category_keys = v
          # Apply max(, 1) to avoid 0-batches
          N = max(len(category_keys) // batch_size, 1)
          if not self.drop_last:
              # Split keys evenly as possible as. Note that If N != 1,
              # the these batches always have size of batch_size at minimum.
              cur_batch_list = [
                  category_keys[
                      i * len(keys) // N : (i + 1) * len(keys) // N
                  ]
                  for i in range(N)
              ]
          else:
              cur_batch_list = [
                  tuple(
                      category_keys[
                          i * batch_size : (i + 1) * batch_size
                      ]
                  )
                  for i in range(N)
              ]
          self.batch_list.extend(cur_batch_list)
  
file_path: espnet/espnet2/samplers/unsorted_batch_sampler.py
imports: import logging
  from typing import Iterator, Tuple
  from typeguard import check_argument_types
  from espnet2.fileio.read_text import read_2columns_text
  from espnet2.samplers.abs_sampler import AbsSampler
label_desc: Use image node_id 1 to create a new UnsortedBatchSampler object from inherited base classes: AbsSampler with example: obj = UnsortedBatchSampler(batch_size, key_file, drop_last, utt2category_file)