class_docstr: Serialize the entire dataflow to a npz dict.
  Note that this would have to store the entire dataflow in memory,
  and is also >10x slower than LMDB/TFRecord serializers.
class_docstr_tok: ["Serialize","the","entire","dataflow","to","a","npz","dict",".","Note","that","this","would","have","to","store","the","entire","dataflow","in","memory",",","and","is","also",">","10x","slower","than","LMDB\/TFRecord","serializers","."]
code: def load(path, shuffle=True):
      # allow_pickle defaults to False since numpy 1.16.3
      # (https://www.numpy.org/devdocs/release.html#unpickling-while-loading-requires-explicit-opt-in)
      buffer = np.load(path, allow_pickle=True)["buffer"]
      return DataFromList(buffer, shuffle=shuffle)
  
imports: import numpy
  import os
  import platform
  from collections import defaultdict
  from ..utils import logger
  from ..utils.serialize import dumps, loads
  from ..utils.develop import create_dummy_class
  from ..utils.utils import get_tqdm
  from .base import DataFlow
  from .common import FixedSizeData, MapData
  from .format import HDF5Data, LMDBData, DiskCacheData
  from .raw import DataFromGenerator, DataFromList
label_desc: Use image node_id 2 for calling the NumpySerializer obj's underlying member method code with example usage: obj.load(path, shuffle) and returns: DataFromList