old_contents: import numpy as np
  import tensorflow as tf
  
  
  class ParameterEditor():
    """Conveniently edit the parameters of a lucid model.
    Example usage:
      model = models.InceptionV1()
      param = ParameterEditor(model.graph_def)
      # Flip weights of first channel of conv2d0
      param["conv2d0_w", :, :, :, 0] *= -1
    """
  
    def __init__(self, graph_def):
      self.nodes = {}
      for node in graph_def.node:
        if "value" in node.attr:
          self.nodes[str(node.name)] = node
      # Set a flag to mark the fact that this is an edited model
      if not "lucid_is_edited" in self.nodes:
        with tf.Graph().as_default() as temp_graph:
          const = tf.constant(True, name="lucid_is_edited")
          const_node = temp_graph.as_graph_def().node[0]
          graph_def.node.extend([const_node])
  
  
    def __getitem__(self, key):
      name = key[0] if isinstance(key, tuple) else key
      tensor = self.nodes[name].attr["value"].tensor
      shape = [int(d.size) for d in tensor.tensor_shape.dim]
      array = np.frombuffer(tensor.tensor_content, dtype="float32").reshape(shape).copy()
      return array[key[1:]] if isinstance(key, tuple) else array
  
    def __setitem__(self, key, new_value):
      name = key[0] if isinstance(key, tuple) else key
      tensor = self.nodes[name].attr["value"].tensor
      node_shape = tuple([int(d.size) for d in tensor.tensor_shape.dim])
      if isinstance(key, tuple):
        array = np.frombuffer(tensor.tensor_content, dtype="float32")
        array = array.reshape(node_shape).copy()
        array[key[1:]] = new_value
        tensor.tensor_content = array.tostring()
      else:
        assert new_value.shape == node_shape
        dtype = tf.DType(tensor.dtype).as_numpy_dtype
        tensor.tensor_content = new_value.astype(dtype).tostring()
  
new_contents: import numpy as np
  import tensorflow as tf
  
  
  class ParameterEditor():
    """Conveniently edit the parameters of a lucid model.
  
    Example usage:
  
      model = models.InceptionV1()
      param = ParameterEditor(model.graph_def)
      # Flip weights of first channel of conv2d0
      param["conv2d0_w", :, :, :, 0] *= -1
  
    """
  
    def __init__(self, graph_def):
      self.nodes = {}
      for node in graph_def.node:
        if "value" in node.attr:
          self.nodes[str(node.name)] = node
      # Set a flag to mark the fact that this is an edited model
      if not "lucid_is_edited" in self.nodes:
        with tf.Graph().as_default() as temp_graph:
          const = tf.constant(True, name="lucid_is_edited")
          const_node = temp_graph.as_graph_def().node[0]
          graph_def.node.extend([const_node])
  
  
    def __getitem__(self, key):
      name = key[0] if isinstance(key, tuple) else key
      tensor = self.nodes[name].attr["value"].tensor
      shape = [int(d.size) for d in tensor.tensor_shape.dim]
      array = np.frombuffer(tensor.tensor_content, dtype="float32").reshape(shape).copy()
      return array[key[1:]] if isinstance(key, tuple) else array
  
    def __setitem__(self, key, new_value):
      name = key[0] if isinstance(key, tuple) else key
      tensor = self.nodes[name].attr["value"].tensor
      node_shape = tuple([int(d.size) for d in tensor.tensor_shape.dim])
      if isinstance(key, tuple):
        array = np.frombuffer(tensor.tensor_content, dtype="float32")
        array = array.reshape(node_shape).copy()
        array[key[1:]] = new_value
        tensor.tensor_content = array.tostring()
      else:
        assert new_value.shape == node_shape
        dtype = tf.DType(tensor.dtype).as_numpy_dtype
        tensor.tensor_content = new_value.astype(dtype).tostring()
  
subject: undo accidental docstring modifications