code: def __init__(self, *args, **kwargs):
      super(ReconstructionOpsTest, self).__init__(*args, **kwargs)
      self.batch_size = 3
      self.frames = 3
      self.samples = 5
  
      self.bases = np.array(range(2, 5))
      exponents = np.array(range(self.frames * self.samples))
      powers = np.power(
          self.bases[:, np.newaxis], exponents[np.newaxis, :]
      )
  
      self.powers = np.reshape(
          powers, [self.batch_size, self.frames, self.samples]
      )
      self.frame_hop = 2
  
      # Hand computed example using powers of unique numbers: this is easily
      # verified.
      self.expected_string = [
          "1",
          "10",
          "100100",
          "1001000",
          "10010010000",
          "100100000000",
          "1001000000000",
          "10000000000000",
          "100000000000000",
      ]
  
imports: from absl.testing import parameterized
  import numpy
  from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import gradient_checker_v2
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops.signal import reconstruction_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 1 to create a new ReconstructionOpsTest object from inherited base classes: test, parameterized with example: obj = ReconstructionOpsTest()