class_docstr: Stack the input data samples to construct the batch.
  The N input samples must have the same shape/length and will be stacked to construct a batch.
  Examples
  --------
  >>> from gluoncv.data import batchify
  >>> # Stack multiple lists
  >>> a = [1, 2, 3, 4]
  >>> b = [4, 5, 6, 8]
  >>> c = [8, 9, 1, 2]
  >>> batchify.Stack()([a, b, c])
  [[1. 2. 3. 4.]
   [4. 5. 6. 8.]
   [8. 9. 1. 2.]]
  <NDArray 3x4 @cpu(0)>
  >>> # Stack multiple numpy.ndarrays
  >>> import numpy as np
  >>> a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
  >>> b = np.array([[5, 6, 7, 8], [1, 2, 3, 4]])
  >>> batchify.Stack()([a, b])
  [[[1. 2. 3. 4.]
    [5. 6. 7. 8.]]
   [[5. 6. 7. 8.]
    [1. 2. 3. 4.]]]
  <NDArray 2x2x4 @cpu(0)>
  >>> # Stack multiple NDArrays
  >>> import mxnet as mx
  >>> a = mx.nd.array([[1, 2, 3, 4], [5, 6, 7, 8]])
  >>> b = mx.nd.array([[5, 6, 7, 8], [1, 2, 3, 4]])
  >>> batchify.Stack()([a, b])
  [[[1. 2. 3. 4.]
    [5. 6. 7. 8.]]
   [[5. 6. 7. 8.]
    [1. 2. 3. 4.]]]
  <NDArray 2x2x4 @cpu(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 _stack_arrs(data, True)
  
imports: import logging
  import numpy
  import mxnet
label_desc: Use image node_id 1 for calling the Stack obj's underlying member method code with example usage: obj.__call__(data) and returns: _stack_arrs