code: def get_ttest_pvalue(metric: float, num_unique_vals: int) -> float:
      """
      Returns the t-test p-value. Once the t-test statistic and degrees
      of freedom are determined, the p-value can be found using a table
      of values from the t-distribution.
      https://en.wikipedia.org/wiki/Student%27s_t-test
  
      :param metric: The t-test statistic of the feature.
      :type metric: float
      :param num_unique_vals: The number of unique values in the feature.
      :type num_unique_vals: int
      :return: The t-test p-value of the feature.
      :rtype: float
      """
      t_statistic = metric
      dof = num_unique_vals - 1
      return scipy.stats.t.sf(abs(t_statistic), dof)
  
imports: import itertools
  import warnings
  from typing import Callable, Dict, List, Tuple
  import numpy
  import pandas
  import scipy
  from responsibleai.databalanceanalysis.balance_measures import BalanceMeasures
  from responsibleai.databalanceanalysis.constants import Constants, Measures
label_desc: Use image node_id 8 for calling a global function with example usage: get_ttest_pvalue(metric, num_unique_vals) and returns: scipy