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.
  
  Parameters
  ----------
  dtype : str or numpy.dtype, default None
      The value type of the output. If it is set to None, the input data type is used.
  
  Examples
  --------
  >>> import gluonnlp.data.batchify as bf
  >>> # Stack multiple lists
  >>> a = [1, 2, 3, 4]
  >>> b = [4, 5, 6, 8]
  >>> c = [8, 9, 1, 2]
  >>> bf.Stack()([a, b, c])
  <BLANKLINE>
  [[1 2 3 4]
   [4 5 6 8]
   [8 9 1 2]]
  <NDArray 3x4 @cpu_shared(0)>
  >>> # Stack multiple numpy.ndarrays
  >>> a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
  >>> b = np.array([[5, 6, 7, 8], [1, 2, 3, 4]])
  >>> bf.Stack()([a, b])
  <BLANKLINE>
  [[[1 2 3 4]
    [5 6 7 8]]
  <BLANKLINE>
   [[5 6 7 8]
    [1 2 3 4]]]
  <NDArray 2x2x4 @cpu_shared(0)>
  >>> # Stack multiple NDArrays
  >>> a = mx.nd.array([[1, 2, 3, 4], [5, 6, 7, 8]])
  >>> b = mx.nd.array([[5, 6, 7, 8], [1, 2, 3, 4]])
  >>> bf.Stack()([a, b])
  <BLANKLINE>
  [[[1. 2. 3. 4.]
    [5. 6. 7. 8.]]
  <BLANKLINE>
   [[5. 6. 7. 8.]
    [1. 2. 3. 4.]]]
  <NDArray 2x2x4 @cpu_shared(0)>
code: def __init__(self, dtype=None):
      self._dtype = dtype
  
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 1 to create a new Stack object  with example: obj = Stack(dtype)