docstr: Data loader that handles batches and shuffling.
  WARNING: this will alter the given data_map ordering, as dicts are mutable
  
  :param data_map: (dict) the input data, where every column is a key
  :param shuffle: (bool) Whether to shuffle or not the dataset
      Important: this should be disabled for recurrent policies
docstr_tok: ["Data","loader","that","handles","batches","and","shuffling",".","WARNING",":","this","will","alter","the","given","data_map","ordering",",","as","dicts","are","mutable",":","param","data_map",":","(","dict",")","the","input","data",",","where","every","column","is","a","key",":","param","shuffle",":","(","bool",")","Whether","to","shuffle","or","not","the","dataset","Important",":","this","should","be","disabled","for","recurrent","policies"]
code: def __init__(self, data_map, shuffle=True):
      """
      Data loader that handles batches and shuffling.
      WARNING: this will alter the given data_map ordering, as dicts are mutable
  
      :param data_map: (dict) the input data, where every column is a key
      :param shuffle: (bool) Whether to shuffle or not the dataset
          Important: this should be disabled for recurrent policies
      """
      self.data_map = data_map
      self.shuffle = shuffle
      self.n_samples = next(iter(data_map.values())).shape[0]
      self._next_id = 0
      if self.shuffle:
          self.shuffle_dataset()
  
file_path: stable-baselines/stable_baselines/common/dataset.py
imports: import numpy
label_desc: Use image node_id 1 to create a new Dataset object from inherited base classes: object with example: obj = Dataset(data_map, shuffle)