code: def convert_iris(directory, output_directory, output_filename='iris.hdf5'):
      
      classes = {b'Iris-setosa': 0, b'Iris-versicolor': 1, b'Iris-virginica': 2}
      data = numpy.loadtxt(
          os.path.join(directory, 'iris.data'),
          converters={4: lambda x: classes[x]},
          delimiter=',')
      features = data[:, :-1].astype('float32')
      targets = data[:, -1].astype('uint8').reshape((-1, 1))
      data = (('all', 'features', features),
              ('all', 'targets', targets))
  
      output_path = os.path.join(output_directory, output_filename)
      h5file = h5py.File(output_path, mode='w')
      fill_hdf5_file(h5file, data)
      h5file['features'].dims[0].label = 'batch'
      h5file['features'].dims[1].label = 'feature'
      h5file['targets'].dims[0].label = 'batch'
      h5file['targets'].dims[1].label = 'index'
  
      h5file.flush()
      h5file.close()
  
      return (output_path,)
docs: Convert the Iris dataset to HDF5.
  
      Converts the Iris dataset to an HDF5 dataset compatible with
      :class:`fuel.datasets.Iris`. The converted dataset is
      saved as 'iris.hdf5'.
      This method assumes the existence of the file `iris.data`.
  
      Parameters
      ----------
      directory : str
          Directory in which input files reside.
      output_directory : str
          Directory in which to save the converted dataset.
      output_filename : str, optional
          Name of the saved dataset. Defaults to `None`, in which case a name
          based on `dtype` will be used.
  
      Returns
      -------
      output_paths : tuple of str
          Single-element tuple containing the path to the converted dataset.