code: def _storeSample(self, inputVector, trueCatIndex, partition=0):
      
  
      # If this is the first sample, then allocate a numpy array
      # of the appropriate size in which to store all samples.
      if self._samples is None:
        self._samples = numpy.zeros((0, len(inputVector)), dtype=RealNumpyDType)
        assert self._labels is None
        self._labels = []
  
      # Add the sample vector and category lable
      self._samples = numpy.concatenate((self._samples, numpy.atleast_2d(inputVector)), axis=0)
      self._labels += [trueCatIndex]
  
      # Add the partition ID
      if self._partitions is None:
        self._partitions = []
      if partition is None:
        partition = 0
      self._partitions += [partition]
docs: Store a training sample and associated category label