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","."]
docstr: Args:
      df (DataFlow): the DataFlow to serialize.
      path (str): output npz file.
docstr_tok: ["Args",":","df","(","DataFlow",")",":","the","DataFlow","to","serialize",".","path","(","str",")",":","output","npz","file","."]
code: def save(df, path):
      """
      Args:
          df (DataFlow): the DataFlow to serialize.
          path (str): output npz file.
      """
      buffer = []
      size = _reset_df_and_get_size(df)
      with get_tqdm(total=size) as pbar:
          for dp in df:
              buffer.append(dp)
              pbar.update()
      np.savez_compressed(
          path, buffer=np.asarray(buffer, dtype=np.object)
      )
  
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 1 for calling the NumpySerializer obj's underlying member method code with example usage: obj.save(df, path) without return types