docstr: Generate samples that are far enough from a set of anchor points.
  
  We generate uniform samples in [low, high], then reject those that are less
  than radius away from any point in anchors. We stop after we have accepted
  num_samples samples.
  
  Args:
    low: The lower end of the interval.
    high: The upper end of the interval.
    anchors: A list of length num_crops with anchor points to avoid.
    radius: Distance threshold for the samples from the anchors.
    num_samples: How many samples to produce.
  
  Returns:
    samples: A list of length num_samples with the accepted samples.
code: def _randomUniformAvoidAnchors(
      self, low, high, anchors, radius, num_samples
  ):
      """Generate samples that are far enough from a set of anchor points.
  
      We generate uniform samples in [low, high], then reject those that are less
      than radius away from any point in anchors. We stop after we have accepted
      num_samples samples.
  
      Args:
        low: The lower end of the interval.
        high: The upper end of the interval.
        anchors: A list of length num_crops with anchor points to avoid.
        radius: Distance threshold for the samples from the anchors.
        num_samples: How many samples to produce.
  
      Returns:
        samples: A list of length num_samples with the accepted samples.
      """
      self.assertTrue(low < high)
      self.assertTrue(radius >= 0)
      num_anchors = len(anchors)
      # Make sure that at least half of the interval is not forbidden.
      self.assertTrue(2 * radius * num_anchors < 0.5 * (high - low))
      anchors = np.reshape(anchors, num_anchors)
      samples = []
      while len(samples) < num_samples:
          sample = np.random.uniform(low, high)
          if np.all(np.fabs(sample - anchors) > radius):
              samples.append(sample)
      return samples
  
imports: from absl.testing import parameterized
  import numpy
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager import context
  from tensorflow.python.framework import config
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors_impl
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import gen_image_ops
  from tensorflow.python.ops import gradient_checker_v2
  from tensorflow.python.ops import image_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 2 for calling the CropAndResizeOpTestBase obj's underlying member method code with example usage: obj._randomUniformAvoidAnchors(low, high, anchors, radius, num_samples) and returns: samples