class_docstr: Test that CropAndResizeBackprop{Image|Boxes} operates reproducibly.
  
  Inheriting from test_base.CropAndResizeOpTestBase ensures that regular op
  functionality is correct when the deterministic code-path is selected.
docstr: Generate a tensor of random floating-point values.
  
  Values will be continuously distributed in the range [low, high).
  
  Note that we use numpy to generate random numbers and then feed the result
  through a constant op to avoid the re-rolling of TensorFlow random ops on
  each run in graph mode.
  
  Args:
    shape: The output shape.
    low: Lower bound of random numbers generated, inclusive.
    high: Upper bound of random numbers generated, exclusive.
    dtype: The output dtype.
  
  Returns:
    A random tensor
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code: def _randomFloats(
      self, shape, low=0.0, high=1.0, dtype=dtypes.float32
  ):
      """Generate a tensor of random floating-point values.
  
      Values will be continuously distributed in the range [low, high).
  
      Note that we use numpy to generate random numbers and then feed the result
      through a constant op to avoid the re-rolling of TensorFlow random ops on
      each run in graph mode.
  
      Args:
        shape: The output shape.
        low: Lower bound of random numbers generated, inclusive.
        high: Upper bound of random numbers generated, exclusive.
        dtype: The output dtype.
  
      Returns:
        A random tensor
      """
      val = np.random.random_sample(
          shape
      )  # float64 continuous uniform [0.0, 1.0)
      diff = high - low
      val *= diff
      val += low
      return constant_op.constant(val, dtype=dtype)
  
imports: import numpy
  from absl.testing import parameterized
  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 errors
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors_impl
  from tensorflow.python.framework import random_seed
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import image_grad_test_base
  from tensorflow.python.ops import image_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 1 for calling the CropAndResizeOpDeterministicTest obj's underlying member method code with example usage: obj._randomFloats(shape, low, high, dtype) and returns: constant_op