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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 dependency_of_fetches(fetches, op):
 """ Check that op is in the subgraph induced by the dependencies of fetches. fetches may have more general structure. Args: fetches: An argument to `sess.run`. Nested structure will affect performance. op (tf.Operation or tf.Tensor):
 Returns: bool: True if any of `fetches` depend on `op`. """

    try:
        from tensorflow.python.client.session import _FetchHandler as FetchHandler
        # use the graph of the op, so that this function can be called without being under a default graph
        handler = FetchHandler(op.graph, fetches, {})
        targets = tuple(handler.fetches() + handler.targets())
    except ImportError:
        if isinstance(fetches, list):
            targets = tuple(fetches)
        elif isinstance(fetches, dict):
            raise ValueError("Don't know how to parse dictionary to fetch list! "
                             "This is a bug of tensorpack.")
        else:
            targets = (fetches, )
    return dependency_of_targets(targets, op)