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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 actnorm_3d(name, x, logscale_factor=3.):
 """Applies actnorm to each time-step independently. There are a total of 2*n_channels*n_steps parameters learnt. Args: name: variable scope. x: 5-D Tensor, (NTHWC) logscale_factor: Increases the learning rate of the scale by logscale_factor. Returns: x: 5-D Tensor, (NTHWC) with the per-timestep, per-channel normalization. """

  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    x = tf.unstack(x, axis=1)
    x_normed = []
    for ind, x_step in enumerate(x):
      x_step, _ = actnorm("actnorm_%d" % ind, x_step,
                          logscale_factor=logscale_factor)
      x_normed.append(x_step)
    return tf.stack(x_normed, axis=1), None