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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 run(logdir, run_name, wave_name, wave_constructor):
 """Generate wave data of the given form. The provided function `wave_constructor` should accept a scalar tensor of type float32, representing the frequency (in Hz) at which to construct a wave, and return a tensor of shape [1, _samples(), `n`] representing audio data (for some number of channels `n`). Waves will be generated at frequencies ranging from A4 to A5. Arguments: logdir: the top-level directory into which to write summary data run_name: the name of this run; will be created as a subdirectory under logdir wave_name: the name of the wave being generated wave_constructor: see above """

  tf.compat.v1.reset_default_graph()
  tf.compat.v1.set_random_seed(0)

  # On each step `i`, we'll set this placeholder to `i`. This allows us
  # to know "what time it is" at each step.
  step_placeholder = tf.compat.v1.placeholder(tf.float32, shape=[])

  # We want to linearly interpolate a frequency between A4 (440 Hz) and
  # A5 (880 Hz).
  with tf.name_scope('compute_frequency'):
    f_min = 440.0
    f_max = 880.0
    t = step_placeholder / (FLAGS.steps - 1)
    frequency = f_min * (1.0 - t) + f_max * t

  # Let's log this frequency, just so that we can make sure that it's as
  # expected.
  tf.compat.v1.summary.scalar('frequency', frequency)

  # Now, we pass this to the wave constructor to get our waveform. Doing
  # so within a name scope means that any summaries that the wave
  # constructor produces will be namespaced.
  with tf.name_scope(wave_name):
    waveform = wave_constructor(frequency)

  # We also have the opportunity to annotate each audio clip with a
  # label. This is a good place to include the frequency, because it'll
  # be visible immediately next to the audio clip.
  with tf.name_scope('compute_labels'):
    samples = tf.shape(input=waveform)[0]
    wave_types = tf.tile(["*Wave type:* `%s`." % wave_name], [samples])
    frequencies = tf.strings.join([
        "*Frequency:* ",
        tf.tile([tf.as_string(frequency, precision=2)], [samples]),
        " Hz.",
    ])
    samples = tf.strings.join([
        "*Sample:* ", tf.as_string(tf.range(samples) + 1),
        " of ", tf.as_string(samples), ".",
    ])
    labels = tf.strings.join([wave_types, frequencies, samples], separator=" ")

  # We can place a description next to the summary in TensorBoard. This
  # is a good place to explain what the summary represents, methodology
  # for creating it, etc. Let's include the source code of the function
  # that generated the wave.
  source = '\n'.join('    %s' % line.rstrip()
                     for line in inspect.getsourcelines(wave_constructor)[0])
  description = ("A wave of type `%r`, generated via:\n\n%s"
                 % (wave_name, source))

  # Here's the crucial piece: we interpret this result as audio.
  summary.op('waveform', waveform, FLAGS.sample_rate,
             labels=labels,
             display_name=wave_name,
             description=description)

  # Now, we can collect up all the summaries and begin the run.
  summ = tf.compat.v1.summary.merge_all()

  sess = tf.compat.v1.Session()
  writer = tf.summary.FileWriter(os.path.join(logdir, run_name))
  writer.add_graph(sess.graph)
  sess.run(tf.compat.v1.global_variables_initializer())
  for step in xrange(FLAGS.steps):
    s = sess.run(summ, feed_dict={step_placeholder: float(step)})
    writer.add_summary(s, global_step=step)
  writer.close()