code: def describe(data):  # verbose=True):
      """Print input/output multiple times
  
      Eventually will be used instead of matrix.nan_indices everywhere
  
      Parameters
      ----------
      data: numpy.nd.array
          The data you want to get a description from
      verbose: boolean(optional)
          Decides whether the description is short or long form
  
      Returns
      -------
      dict
          missingness: list
              Confidence interval of data being MCAR, MAR or MNAR - in that order
          nan_xy: list of tuples
              Indices of all null points
          nan_n: list
              Total number of null values for each column
          pmissing_n: float
              Percentage of missing values in dataset
          nan_rows: list
              Indices of all rows that are completely null
          nan_cols: list
              Indices of all columns that are completely null
          mean_rows: list
              Mean value of each row
          mean_cols: list
              Mean value of each column
          std_dev: list
              std dev for each row/column
          min_max: list
              Finds the minimum and maximum for each row
  
      """
      #    missingness = [0.33, 0.33, 0.33]  # find_missingness(data)
      nan_xy = matrix.nan_indices(data)
      nan_n = len(nan_xy)
      pmissing_n = float(nan_n / len(data.flatten))
      #    pmissing_rows = ""
      #    pmissing_cols = ""
      #    nan_rows = ""
      #    nan_cols = ""
      #    mean_rows = ""
      #    mean_cols = ""
      #    std_dev = ""
      #                   "missingness": missingness,
      description = {
          "nan_xy": nan_xy,
          "nan_n": nan_n,
          "pmissing_n": pmissing_n,
      }
      #                   "pmissing_rows": pmissing_rows,
      #                   "pmissing_cols": pmissing_cols,
      #                   "nan_rows": nan_rows,
      #                   "nan_cols": nan_cols,
      #                   "mean_rows": mean_rows,
      #                   "mean_cols": mean_cols,
      #                   "std_dev": std_dev}
      return description
  
label_desc: Use image node_id 1 for calling a global function with example usage: describe(data) and returns: description