docstr: Verifies the output values of the convolution function.
  
  Args:
    tensor_in_sizes: Input tensor dimensions [batch, input_x, input_y,
      input_z, input_depth].
    filter_in_sizes: Filter tensor dimensions [kernel_x, kernel_y, kernel_z,
      input_depth, output_depth].
    stride: [x_stride, y_stride, z_stride]
    padding: Padding type.
    expected: Value that the output of computation should match
    data_format: Format of the data tensors.
    dtype: Data type for inputs and outputs.
    use_gpu: True if the operations should be run on GPU
    op_name: Name of the op to be tested
  
  Returns:
    None
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code: def _VerifyValues(
      self,
      tensor_in_sizes,
      filter_in_sizes,
      stride,
      padding,
      expected,
      data_format,
      dtype,
      use_gpu,
      op_name,
  ):
      """Verifies the output values of the convolution function.
  
      Args:
        tensor_in_sizes: Input tensor dimensions [batch, input_x, input_y,
          input_z, input_depth].
        filter_in_sizes: Filter tensor dimensions [kernel_x, kernel_y, kernel_z,
          input_depth, output_depth].
        stride: [x_stride, y_stride, z_stride]
        padding: Padding type.
        expected: Value that the output of computation should match
        data_format: Format of the data tensors.
        dtype: Data type for inputs and outputs.
        use_gpu: True if the operations should be run on GPU
        op_name: Name of the op to be tested
  
      Returns:
        None
      """
      if use_gpu and not test.is_gpu_available(cuda_only=True):
          self.skipTest("GPU not available")
      results = []
      result = self._SetupValuesForDevice(
          tensor_in_sizes,
          filter_in_sizes,
          stride,
          padding,
          data_format,
          dtype,
          use_gpu=use_gpu,
          op_name=op_name,
      )
      results.append(result)
  
      with self.cached_session() as sess:
          values = self.evaluate(results)
          for value in values:
              tf_logging.debug("expected = %s", expected)
              tf_logging.debug("actual = %s", value)
              self.assertAllCloseAccordingToType(
                  expected, value.flatten()
              )
  
imports: import math
  from absl.testing import parameterized
  import numpy
  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 tensor_shape
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import gen_nn_ops
  from tensorflow.python.ops import gradient_checker
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import nn_ops
  import tensorflow.python.ops.nn_grad
  from tensorflow.python.platform import test
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.util.compat import collections_abc
  from tensorflow.python.eager import context
label_desc: Use image node_id 2 for calling the Conv3DTest obj's underlying member method code with example usage: obj._VerifyValues(tensor_in_sizes, filter_in_sizes, stride, padding, expected, data_format, dtype, use_gpu, op_name) without return types