code: def run(k, step):
      """
      Arguments:
          k: a float in the range [0, 1] that affects the histogram values written by the run.
          step: an integer value to use for writing summaries.
      """
      # Make a normal distribution, with a shifting mean
      mean_moving_normal = tf.random.normal(
          shape=[1000], mean=(5 * k), stddev=1
      )
  
      # Record that distribution into a histogram summary
      tf.summary.histogram(
          "normal/moving_mean",
          mean_moving_normal,
          description="A normal distribution whose mean changes "
          "over time.",
          step=step,
      )
  
      # Make a normal distribution with shrinking variance
      shrinking_normal = tf.random.normal(
          shape=[1000], mean=0, stddev=1 - (k)
      )
      # Record that distribution too
      tf.summary.histogram(
          "normal/shrinking_variance",
          shrinking_normal,
          description="A normal distribution whose variance "
          "shrinks over time.",
          step=step,
      )
  
      # Let's combine both of those distributions into one dataset
      normal_combined = tf.concat(
          [mean_moving_normal, shrinking_normal], 0
      )
      # We add another histogram summary to record the combined distribution
      tf.summary.histogram(
          "normal/bimodal",
          normal_combined,
          description="A combination of two normal distributions, "
          "one with a moving mean and one with  "
          "shrinking variance. The result is a "
          "distribution that starts as unimodal and "
          "becomes more and more bimodal over time.",
          step=step,
      )
  
      # Add a gamma distribution
      gamma = tf.random.gamma(shape=[1000], alpha=k)
      tf.summary.histogram(
          "gamma",
          gamma,
          description="A gamma distribution whose shape "
          "parameter, α, changes over time.",
          step=step,
      )
  
      # And a poisson distribution
      poisson = tf.random.poisson(shape=[1000], lam=k)
      tf.summary.histogram(
          "poisson",
          poisson,
          description="A Poisson distribution, which only "
          "takes on integer values.",
          step=step,
      )
  
      # And a uniform distribution
      uniform = tf.random.uniform(shape=[1000], maxval=k * 10)
      tf.summary.histogram(
          "uniform",
          uniform,
          description="A simple uniform distribution.",
          step=step,
      )
  
      # And an empty distribution
      empty = tf.constant([])
      tf.summary.histogram(
          "empty",
          empty,
          description="An empty distribution.",
          step=step,
      )
  
      # And a distribution consisting of a single unique value
      single_value = tf.constant([1.0] * 10)
      tf.summary.histogram(
          "single_value",
          single_value,
          description="A distribution containing a single unique value.",
          step=step,
      )
  
      # Finally, combine everything together!
      all_distributions = [
          mean_moving_normal,
          shrinking_normal,
          gamma,
          poisson,
          uniform,
          empty,
          single_value,
      ]
      all_combined = tf.concat(all_distributions, 0)
      tf.summary.histogram(
          "all_combined",
          all_combined,
          description="An amalgamation of several distributions: a "
          "uniform distribution, a gamma distribution, a Poisson "
          "distribution, two normal distributions, an empty "
          "distribution, and a distribution containing a single "
          "unique value.",
          step=step,
      )
  
imports: from absl import app
  import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: run(k, step) without return types