<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def leaky_relu(x, name=None):
 """Creates a leaky_relu. This is an alternate non-linearity to relu. The leaky part of the relu may prevent dead Neurons in a model since the gradient doesn't go completely to 0. Args: x: The input tensor. name: Optional name for this op. Returns: x if x > 0 otherwise 0.01 * x. """

  with tf.name_scope(name, 'leaky_relu', [x]) as scope:
    x = tf.convert_to_tensor(x, name='x')
    return tf.where(tf.less(x, 0.0), 0.01 * x, x, name=scope)