code: def calculate_confidence_interval(
      t_distribution_value: types.ValueWithTDistribution,
  ):
      """Calculate confidence intervals based 95% confidence level."""
      alpha = 0.05
      std_err = t_distribution_value.sample_standard_deviation
      t_stat = stats.t.ppf(
          1 - (alpha / 2.0),
          t_distribution_value.sample_degrees_of_freedom,
      )
      # The order of operands matters here because we want to use the
      # std_err.__mul__ operator below, rather than the t_stat.__mul__.
      # TODO(b/197669322): make StructuredMetricValues robust to operand ordering.
      upper_bound = t_distribution_value.sample_mean + std_err * t_stat
      lower_bound = t_distribution_value.sample_mean - std_err * t_stat
      return t_distribution_value.sample_mean, lower_bound, upper_bound
  
label_desc: Use image node_id 1 for calling a global function with example usage: calculate_confidence_interval(t_distribution_value) and returns: t_distribution_value,  lower_bound,  upper_bound