class_docstr: Internal representation of minibatch data.
  
  Args:
      batch: batch input for `var`.
       It can be:
  
        * a pure Python structure (list of lists, ...),
        * a list of NumPy arrays or SciPy sparse CSR matrices
        * a :class:`~cntk.core.Value` object (e.g. returned by :func:`one_hot`)
      seq_starts (list of `bool`\ s or None): if None, every sequence is
       treated as a new sequence. Otherwise, it is interpreted as a list of
       Booleans that tell whether a sequence is a new sequence (`True`) or a
       continuation of the sequence in the same slot of the previous
       minibatch (`False`)
      device (:class:`~cntk.device.DeviceDescriptor`): device this value
       should be put on
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docstr: The mask matrix of this value. Each row denotes a sequence with its
  elements describing the mask of the element:
  
   * 2: beginning of sequence (e.g. an LSTM would be reset)
   * 1: valid element
   * 0: invalid element
  
  Example:
    A mask of ``[[2, 1, 1], [1, 1, 0]]`` describes a batch of two
    sequences. The first has three elements, of which the first element
    (2) signals the beginning of a sequence. The second sequence has two
    elements (last element marked 'invalid' by '0'). As it starts with
    (1), it is a continuation of the 2nd sequence in the previous
    minibatch.
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code: def mask(self):
      """
      The mask matrix of this value. Each row denotes a sequence with its
      elements describing the mask of the element:
  
       * 2: beginning of sequence (e.g. an LSTM would be reset)
       * 1: valid element
       * 0: invalid element
  
      Example:
        A mask of ``[[2, 1, 1], [1, 1, 0]]`` describes a batch of two
        sequences. The first has three elements, of which the first element
        (2) signals the beginning of a sequence. The second sequence has two
        elements (last element marked 'invalid' by '0'). As it starts with
        (1), it is a continuation of the 2nd sequence in the previous
        minibatch.
      """
      return super(Value, self).mask().asarray()
  
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 7 for calling the Value obj's underlying member method code with example usage: obj.mask() and returns: super