code: def _interpolated_thresholds(num_thresholds: int) -> List[float]:
      """Returns thresholds interpolated over a range equal to num_thresholds."""
      # The interpolation strategy used here matches that used by keras for AUC.
      thresholds = [
          (i + 1) * 1.0 / (num_thresholds - 1)
          for i in range(num_thresholds - 2)
      ]
      return [-_EPSILON] + thresholds + [1.0 + _EPSILON]
  
imports: from typing import Any, Callable, Dict, Iterable, List, NamedTuple, Optional, Tuple, Union
  import apache_beam
  import numpy
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.contrib.aggregates import binary_confusion_matrices
  from tensorflow_model_analysis.metrics import calibration_histogram
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.proto import metrics_for_slice_pb2
label_desc: Use image node_id 1 for calling a global function with example usage: _interpolated_thresholds(num_thresholds) and returns: unknown