code: def concat_aligned(items):
      
  
      if len(items) == 0:
          return []
      elif len(items) == 1:
          # we assume the input is aligned. In any case, it doesn't help
          # performance to force align it since that incurs a needless copy.
          return items[0]
      elif (isinstance(items[0], np.ndarray)
            and items[0].dtype in [np.float32, np.float64, np.uint8]):
          dtype = items[0].dtype
          flat = aligned_array(sum(s.size for s in items), dtype)
          batch_dim = sum(s.shape[0] for s in items)
          new_shape = (batch_dim, ) + items[0].shape[1:]
          output = flat.reshape(new_shape)
          assert output.ctypes.data % 64 == 0, output.ctypes.data
          np.concatenate(items, out=output)
          return output
      else:
          return np.concatenate(items)
docs: Concatenate arrays, ensuring the output is 64-byte aligned.
  
      We only align float arrays; other arrays are concatenated as normal.
  
      This should be used instead of np.concatenate() to improve performance
      when the output array is likely to be fed into TensorFlow.