<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def pack_examples(examples, has_inputs, packed_length=256, spacing=2, queue_size=10, chop_long_sequences=False):
 """Pack examples into longer examples. If has_inputs=False, we are packing single-sequence examples with targets only and no inputs. In this case, we concatenate the targets from several examples to form each new example. We insert a number of zeros for spacing between the original sequences. This is to help the sequences stay separate under convolutions. If chop_long_sequences is set, then any input sequence longer than packed_length gets chopped up into multiple examples. Otherwise, long sequences are emitted as singletons. If has_inputs=True, then we are packing sequence-to-sequence examples. We combine several examples by concatenating the inputs (as above) and concatenating the targets (as above). Chopping of long sequences is not supported. The packed examples are represented as dictionaries containing: "inputs", "targets": the packed sequences described above "inputs_segmentation", "targets_segmentation": Sequences aligned with "inputs", "targets" specifying to which original sequence each position belongs. Numbering starts from 1, and 0 is used for spacing. This information is useful for preventing attention across segments. e.g. [1 1 1 1 1 1 0 0 2 2 2 0 0 3 3 3 3 3 0 0 4 4 4] "inputs_position", "targets_position": Sequences aligned with "inputs", "targets" specifying position within the original sequence. This is useful for positional encodings. e.g. [0 1 2 3 4 5 0 0 0 1 2 0 0 0 1 2 3 4 0 0 0 1 2] Args: examples: a generator returning feature dictionaries. has_inputs: a boolean packed_length: an integer spacing: an integer queue_size: an integer chop_long_sequences: a boolean Yields: feature dictionaries. """

  packer = SequencePairPacker if has_inputs else SequencePacker
  combined = []
  for example in examples:
    x = ((example["inputs"], example["targets"])
         if has_inputs else example["targets"])
    if chop_long_sequences and len(x) > packed_length:
      assert not has_inputs
      num_fragments = len(x) // packed_length
      for i in range(num_fragments):
        yield packer(
            x[packed_length * i:packed_length * (i + 1)], spacing).to_dict()
      x = x[packed_length * num_fragments:]
    added = False
    for c in combined:
      if c.can_fit(x, packed_length):
        c.add(x)
        added = True
        break
    if not added:
      if len(combined) == queue_size:
        yield combined[0].to_dict()
        combined = combined[1:]
      combined.append(packer(x, spacing))
  for c in combined:
    yield c.to_dict()