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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 scheduled_sample_count(ground_truth_x, generated_x, batch_size, scheduled_sample_var):
 """Sample batch with specified mix of groundtruth and generated data points. Args: ground_truth_x: tensor of ground-truth data points. generated_x: tensor of generated data points. batch_size: batch size scheduled_sample_var: number of ground-truth examples to include in batch. Returns: New batch with num_ground_truth sampled from ground_truth_x and the rest from generated_x. """

  num_ground_truth = scheduled_sample_var
  idx = tf.random_shuffle(tf.range(batch_size))
  ground_truth_idx = tf.gather(idx, tf.range(num_ground_truth))
  generated_idx = tf.gather(idx, tf.range(num_ground_truth, batch_size))

  ground_truth_examps = tf.gather(ground_truth_x, ground_truth_idx)
  generated_examps = tf.gather(generated_x, generated_idx)

  output = tf.dynamic_stitch([ground_truth_idx, generated_idx],
                             [ground_truth_examps, generated_examps])
  # if batch size is known set it.
  if isinstance(batch_size, int):
    output.set_shape([batch_size] + common_layers.shape_list(output)[1:])
  return output