class_docstr: Creates an empty dense internal data representation of a
  :class:`~cntk.core.Value` object.
  To create an NDArrayView from a NumPy array, use :meth:`from_dense`.
  To create an NDArrayView from a sparse array, use :meth:`from_csr`.
  
  Args:
      shape (tuple): shape of the data
      data_type (np.float32, np.float64, np.float16): data type of the data
      device (:class:`~cntk.device.DeviceDescriptor`): device this value
       should be put on
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docstr: Returns a sliced view of the instance.
  
  Example:
      >>> # Creating an array of shape (1, 1, 2, 3)
      >>> np_array = np.asarray([[[[10, 20, 30], [40, 50, 60]]]],                                       dtype=np.float32)
      >>> nd = NDArrayView.from_dense(np_array)
      >>> sliced = nd.slice_view([0, 0, 0, 0], [2, 3])
      >>> np_sliced = sliced.asarray()
      >>> # Result is an array of shape (2, 3)
      >>> print(np_sliced)
      [[10. 20. 30.]
       [40. 50. 60.]]
  
  Args:
    start_offset (tuple or list): shape of the same rank as this Value
     instance that denotes the start of the slicing
    extent (tuple or list): shape of the right-aligned extent to keep
    read_only (bool): whether the returned slice is read only or not
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code: def slice_view(self, start_offset, extent, read_only=True):
      """
          Returns a sliced view of the instance.
  
          Example:
              >>> # Creating an array of shape (1, 1, 2, 3)
              >>> np_array = np.asarray([[[[10, 20, 30], [40, 50, 60]]]], \
                                        dtype=np.float32)
              >>> nd = NDArrayView.from_dense(np_array)
              >>> sliced = nd.slice_view([0, 0, 0, 0], [2, 3])
              >>> np_sliced = sliced.asarray()
              >>> # Result is an array of shape (2, 3)
              >>> print(np_sliced)
              [[10. 20. 30.]
               [40. 50. 60.]]
  
          Args:
            start_offset (tuple or list): shape of the same rank as this Value
             instance that denotes the start of the slicing
            extent (tuple or list): shape of the right-aligned extent to keep
            read_only (bool): whether the returned slice is read only or not
          """
      return super(NDArrayView, self).slice_view(
          list(reversed(start_offset)),
          list(reversed(extent)),
          read_only,
      )
  
imports: import warnings
  import numbers
  import numpy
  from scipy import sparse
  from .None import cntk_py
  from .device import use_default_device, cpu, DeviceKind
  from cntk.internal import typemap
  from cntk.internal.sanitize import sanitize_batch, _sparse_to_dense_network_cache, data_type_to_dtype
label_desc: Use image node_id 6 for calling the NDArrayView obj's underlying member method code with example usage: obj.slice_view(start_offset, extent, read_only) and returns: super