code: def testConv3DTransposeSingleStride(self):
      with self.session(), self.test_scope():
          strides = [1, 1, 1, 1, 1]
  
          # Input, output: [batch, depth, height, width, channel]
          x_shape = [2, 5, 6, 4, 3]
          y_shape = [2, 5, 6, 4, 2]
  
          # Filter: [kernel_depth, kernel_height, kernel_width, out_depth, in_depth]
          f_shape = [3, 3, 3, 2, 3]
  
          x = constant_op.constant(
              1.0, shape=x_shape, name="x", dtype=dtypes.float32
          )
          f = constant_op.constant(
              1.0, shape=f_shape, name="filter", dtype=dtypes.float32
          )
          output = nn_ops.conv3d_transpose(
              x, f, y_shape, strides=strides, padding="SAME"
          )
          value = self.evaluate(output)
  
          # We count the number of cells being added at the locations in the output.
          # At the center, #cells = kernel_depth * kernel_height * kernel_width
          # At the corners, #cells = ceil(kernel_depth/2) * ceil(kernel_height/2)
          #                          * ceil(kernel_width/2)
          # At the edges, #cells =
          #   kernel_depth * ceil(kernel_height/2) * ceil(kernel_width/2) or
          #   ceil(kernel_depth/2) * kernel_height * ceil(kernel_width/2) or
          #   ceil(kernel_depth/2) * ceil(kernel_height/2) * kernel_width
          # At the borders, #cells =
          #   ceil(kernel_depth/2) * kernel_height * kernel_width or
          #   kernel_depth * ceil(kernel_height/2) * kernel_width or
          #   kernel_depth * kernel_height * ceil(kernel_width/2)
  
          for n in range(x_shape[0]):
              for k in range(f_shape[3]):
                  for w in range(y_shape[3]):
                      for h in range(y_shape[2]):
                          for d in range(y_shape[1]):
                              d_in = d > 0 and d < y_shape[1] - 1
                              h_in = h > 0 and h < y_shape[2] - 1
                              w_in = w > 0 and w < y_shape[3] - 1
                              if d_in + h_in + w_in == 3:
                                  target = 27 * 3.0
                              elif d_in + h_in + w_in == 2:
                                  target = 18 * 3.0
                              elif d_in or h_in or w_in:
                                  target = 12 * 3.0
                              else:
                                  target = 8 * 3.0
                              self.assertAllClose(
                                  target, value[n, d, h, w, k]
                              )
  
imports: from absl.testing import parameterized
  import numpy
  from tensorflow.compiler.tests import test_utils
  from tensorflow.compiler.tests import xla_test
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_nn_ops
  from tensorflow.python.ops import gradient_checker
  from tensorflow.python.ops import nn_ops
  import tensorflow.python.ops.nn_grad
  from tensorflow.python.platform import googletest
label_desc: Use image node_id 1 for calling the Conv3DTransposeTest obj's underlying member method code with example usage: obj.testConv3DTransposeSingleStride() without return types