code: def matrix_1d(categories, values_err, counts, counts_err, metric):
      """Constructs a 1D matrix.
  
      The matrix is in a dictionary format which can then be saved to JSON.
  
      :param categories: The categories for the selected feature.
      :type categories: list
      :param values_err: The error values for the selected feature.
      :type values_err: list
      :param counts: The counts for the selected feature.
      :type counts: list
      :param counts_err: The error counts for the selected feature.
      :type counts_err: list
      :param metric: The calculated metric.
      :type metric: str
      :returns: The 1D matrix dictionary.
      :rtype: dict
      """
      matrix = []
      matrix_row = []
      for col_idx in reversed(range(len(categories))):
          cat = categories[col_idx]
          if metric == Metrics.ERROR_RATE:
              false_count = 0
              if cat in values_err:
                  index_err = list(values_err).index(cat)
                  false_count = int(counts_err[index_err])
              matrix_row.append(
                  {
                      FALSE_COUNT: false_count,
                      COUNT: int(counts[col_idx]),
                      METRIC_NAME: metric_to_display_name[metric],
                  }
              )
          elif is_multi_agg_metric(metric):
              tp_sum = []
              fp_sum = []
              fn_sum = []
              tn_sum = []
              metric_value = 0
              error = 0
              if cat in values_err:
                  metric_value = float(counts_err[col_idx][0])
                  tp_sum = counts_err[col_idx][1]
                  fp_sum = counts_err[col_idx][2]
                  fn_sum = counts_err[col_idx][3]
                  tn_sum = counts_err[col_idx][4]
                  error = float(counts_err[col_idx][5])
                  if math.isnan(metric_value):
                      metric_value = 0.0
              matrix_row.append(
                  {
                      METRIC_VALUE: metric_value,
                      TP: tp_sum,
                      FP: fp_sum,
                      FN: fn_sum,
                      TN: tn_sum,
                      ERROR: error,
                      METRIC_NAME: metric_to_display_name[metric],
                      COUNT: int(counts[col_idx]),
                  }
              )
          else:
              metric_value = 0
              if cat in values_err:
                  metric_value = float(counts_err[col_idx])
                  if math.isnan(metric_value):
                      metric_value = 0.0
              matrix_row.append(
                  {
                      METRIC_VALUE: metric_value,
                      METRIC_NAME: metric_to_display_name[metric],
                      COUNT: int(counts[col_idx]),
                  }
              )
      matrix.append(matrix_row)
      category = []
      category_min_interval = []
      category_max_interval = []
      for cat in reversed(categories):
          if isinstance(categories, pd.IntervalIndex):
              category_min_interval.append(cat.left)
              category_max_interval.append(cat.right)
              category.append(str(cat))
          else:
              category.append(get_py_value(cat))
      category1 = {
          VALUES: category,
          INTERVAL_MIN: category_min_interval,
          INTERVAL_MAX: category_max_interval,
      }
      return {MATRIX: matrix, CATEGORY1: category1}
  
imports: import logging
  import math
  import warnings
  from abc import ABC, abstractmethod
  import numpy
  import pandas
  from sklearn.metrics import multilabel_confusion_matrix
  from erroranalysis._internal.cohort_filter import filter_from_cohort
  from erroranalysis._internal.constants import DIFF, PRED_Y, ROW_INDEX, TRUE_Y, MatrixParams, MetricKeys, Metrics, ModelTask, metric_to_display_name
  from erroranalysis._internal.metrics import get_ordered_classes, is_multi_agg_metric, metric_to_func
  from raiutils.exceptions import UserConfigValidationException
label_desc: Use image node_id 9 for calling a global function with example usage: matrix_1d(categories, values_err, counts, counts_err, metric) and returns: dict