class_docstr: Loosely return list of the input data samples.
  There is no constraint of shape for any of the input samples, however, you will
  only be able to apply single batch operations since the output have different shapes.
  
  Examples
  --------
  >>> a = [1, 2, 3, 4]
  >>> b = [4, 5, 6]
  >>> c = [8, 2]
  >>> batchify.Append()([a, b, c])
  [
  [[1. 2. 3. 4.]]
  <NDArray 1x4 @cpu_shared(0)>,
  [[4. 5. 6.]]
  <NDArray 1x3 @cpu_shared(0)>,
  [[8. 2.]]
  <NDArray 1x2 @cpu_shared(0)>
  ]
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docstr: Batchify the input data.
  
  Parameters
  ----------
  data : list
      The input data samples
  Returns
  -------
  batch_data : NDArray
docstr_tok: ["Batchify","the","input","data",".","Parameters","--","--","--","--","--","data",":","list","The","input","data","samples","Returns","--","--","--","-","batch_data",":","NDArray"]
code: def __call__(self, data):
      """Batchify the input data.
  
      Parameters
      ----------
      data : list
          The input data samples
      Returns
      -------
      batch_data : NDArray
      """
      return _append_arrs(
          data,
          use_shared_mem=True,
          expand=self._expand,
          batch_axis=self._batch_axis,
      )
  
imports: import logging
  import numpy
  import mxnet
label_desc: Use image node_id 2 for calling the Append obj's underlying member method code with example usage: obj.__call__(data) and returns: _append_arrs