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 mxnet.gluon.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 = nd.array([[1, 2, 3, 4], [5, 6, 7, 8]])
  >>> b = 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)>
code: def __mx_handle__(self):
      from ._internal import StackBatchify
  
      return StackBatchify()
  
imports: import math
  import warnings
  import numpy
  from ...device import Device, cpu
  from ...None import ndarray
  from ...None import numpy
  from ...util import is_np_array
label_desc: Use image node_id 3 for calling the Stack obj's underlying member method code with example usage: obj.__mx_handle__() and returns: StackBatchify