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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _generate_examples(self, filepath):
 """Generate examples for the Shapes3d dataset. Args: filepath: path to the Shapes3d hdf5 file. Yields: Dictionaries with images and the different labels. """

    # Simultaneously iterating through the different data sets in the hdf5
    # file will be slow with a single file. Instead, we first load everything
    # into memory before yielding the samples.
    image_array, values_array = _load_data(filepath)

    # We need to calculate the class labels from the float values in the file.
    labels_array = np.zeros_like(values_array, dtype=np.int64)
    for i in range(values_array.shape[1]):
      labels_array[:, i] = _discretize(values_array[:, i])  # pylint: disable=unsupported-assignment-operation

    for image, labels, values in moves.zip(image_array, labels_array,
                                           values_array):
      yield {
          "image": image,
          "label_floor_hue": labels[0],
          "label_wall_hue": labels[1],
          "label_object_hue": labels[2],
          "label_scale": labels[3],
          "label_shape": labels[4],
          "label_orientation": labels[5],
          "value_floor_hue": values[0],
          "value_wall_hue": values[1],
          "value_object_hue": values[2],
          "value_scale": values[3],
          "value_shape": values[4],
          "value_orientation": values[5],
      }