code: def minibatches(
      inputs=None,
      targets=None,
      batch_size=None,
      allow_dynamic_batch_size=False,
      shuffle=False,
  ):
      """Generate a generator that input a group of example in numpy.array and
      their labels, return the examples and labels by the given batch size.
  
      Parameters
      ----------
      inputs : numpy.array
          The input features, every row is a example.
      targets : numpy.array
          The labels of inputs, every row is a example.
      batch_size : int
          The batch size.
      allow_dynamic_batch_size: boolean
          Allow the use of the last data batch in case the number of examples is not a multiple of batch_size, this may result in unexpected behaviour if other functions expect a fixed-sized batch-size.
      shuffle : boolean
          Indicating whether to use a shuffling queue, shuffle the dataset before return.
  
      Examples
      --------
      >>> X = np.asarray([['a','a'], ['b','b'], ['c','c'], ['d','d'], ['e','e'], ['f','f']])
      >>> y = np.asarray([0,1,2,3,4,5])
      >>> for batch in tl.iterate.minibatches(inputs=X, targets=y, batch_size=2, shuffle=False):
      >>>     print(batch)
      ... (array([['a', 'a'], ['b', 'b']], dtype='<U1'), array([0, 1]))
      ... (array([['c', 'c'], ['d', 'd']], dtype='<U1'), array([2, 3]))
      ... (array([['e', 'e'], ['f', 'f']], dtype='<U1'), array([4, 5]))
  
      Notes
      -----
      If you have two inputs and one label and want to shuffle them together, e.g. X1 (1000, 100), X2 (1000, 80) and Y (1000, 1), you can stack them together (`np.hstack((X1, X2))`)
      into (1000, 180) and feed to ``inputs``. After getting a batch, you can split it back into X1 and X2.
  
      """
      if len(inputs) != len(targets):
          raise AssertionError(
              "The length of inputs and targets should be equal"
          )
  
      if shuffle:
          indices = np.arange(len(inputs))
          np.random.shuffle(indices)
  
      # for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):
      # chulei: handling the case where the number of samples is not a multiple of batch_size, avoiding wasting samples
      for start_idx in range(0, len(inputs), batch_size):
          end_idx = start_idx + batch_size
          if end_idx > len(inputs):
              if allow_dynamic_batch_size:
                  end_idx = len(inputs)
              else:
                  break
          if shuffle:
              excerpt = indices[start_idx:end_idx]
          else:
              excerpt = slice(start_idx, end_idx)
          if (
              isinstance(inputs, list) or isinstance(targets, list)
          ) and (shuffle == True):
              # zsdonghao: for list indexing when shuffle==True
              yield [inputs[i] for i in excerpt], [
                  targets[i] for i in excerpt
              ]
          else:
              yield inputs[excerpt], targets[excerpt]
  
imports: import numpy
  from six.moves import xrange
label_desc: Use image node_id 1 for calling a global function with example usage: minibatches(inputs, targets, batch_size, allow_dynamic_batch_size, shuffle) without return types