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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 sg_summary_gradient(tensor, gradient, prefix=None, name=None):
 r"""Register `tensor` to summary report as `gradient` Args: tensor: A `Tensor` to log as gradient gradient: A 0-D `Tensor`. A gradient to log prefix: A `string`. A prefix to display in the tensor board web UI. name: A `string`. A name to display in the tensor board web UI. Returns: None """

    # defaults
    prefix = '' if prefix is None else prefix + '/'
    # summary name
    name = prefix + _pretty_name(tensor) if name is None else prefix + name
    # summary statistics
    # noinspection PyBroadException
    _scalar(name + '/grad', tf.reduce_mean(tf.abs(gradient)))
    _histogram(name + '/grad-h', tf.abs(gradient))