class_docstr: Wrap multiple batchify functions together and apply it to merge inputs from a dict.
  
  The generated batch samples are stored as a dict with the same keywords.
  
  Each data sample should be a dict and the fn corresponds to `key` will be applied on the
  input with the keyword `key`.
  For example, each data sample is {'data': nd_data, 'label': nd_label}.
  You can merge the data and labels using
  `Dict({'data': DataBatchify, 'label': LabelBatchify})` to batchify the nd_data and nd_label.
  
  Parameters
  ----------
  fn_dict
      A dictionary that contains the key-->batchify function mapping.
  
  Examples
  --------
  >>> from gluonnlp.data.batchify import Dict, Pad, Stack
  >>> a = {'data': [1, 2, 3, 4], 'label': 0}
  >>> b = {'data': [5, 7], 'label': 1}
  >>> c = {'data': [1, 2, 3, 4, 5, 6, 7], 'label': 0}
  >>> batchify_fn = Dict({'data': Pad(), 'label': Stack()})
  >>> sample = batchify_fn([a, b, c])
  >>> sample['data']
  <BLANKLINE>
  [[1. 2. 3. 4. 0. 0. 0.]
   [5. 7. 0. 0. 0. 0. 0.]
   [1. 2. 3. 4. 5. 6. 7.]]
  <NDArray 3x7 @cpu_shared(0)>
  >>> sample['label']
  <BLANKLINE>
  [0 1 0]
  <NDArray 3 @cpu_shared(0)>
docstr: Parameters
  ----------
  data
      The samples to batchify. Each sample should be a dictionary
  
  Returns
  -------
  ret
      The resulting dictionary that stores the merged samples.
docstr_tok: ["Parameters","--","--","--","--","--","data","The","samples","to","batchify",".","Each","sample","should","be","a","dictionary","Returns","--","--","--","-","ret","The","resulting","dictionary","that","stores","the","merged","samples","."]
code: def __call__(self, data: t_List[t_Dict]) -> t_Dict:
      """
  
      Parameters
      ----------
      data
          The samples to batchify. Each sample should be a dictionary
  
      Returns
      -------
      ret
          The resulting dictionary that stores the merged samples.
      """
      ret = dict()
      for k, ele_fn in self._fn_dict.items():
          ret[k] = ele_fn([ele[k] for ele in data])
      return ret
  
imports: import warnings
  import math
  from typing import Dict, Callable, NamedTuple, List, Tuple, AnyStr, Union
  import numpy
  import mxnet
  from mxnet.util import is_np_array
label_desc: Use image node_id 2 for calling the Dict obj's underlying member method code with example usage: obj.__call__(data) and returns: ret