class_docstr: Tests for deterministic cuDNN functionality.
code: def testConvTransposeBackwardFilterGradient(self, rate=1):
      in_channels = 8
      out_channels = 8
      in_shape = LayerShapeNHWC(
          batch=8, height=64, width=64, channels=in_channels
      )
      filter_shape = FilterShape2DTranspose(
          height=3,
          width=3,
          out_channels=out_channels,
          in_channels=in_channels,
      )
      in_op = self._random_data_op(in_shape)
      filter_op = self._random_data_op(filter_shape)
      out_shape = LayerShapeNHWC(
          batch=in_shape.batch,
          height=in_shape.height,
          width=in_shape.width,
          channels=out_channels,
      )
      upstream_gradients = self._random_data_op(out_shape)
  
      def gradient():
          with backprop.GradientTape() as tape:
              tape.watch(filter_op)
              op_output = nn_ops.conv2d_transpose_v2(
                  in_op,
                  filter_op,
                  out_shape,
                  strides=1,
                  padding="SAME",
                  data_format="NHWC",
                  dilations=[1, rate, rate, 1],
              )
              gradient_injector_output = op_output * upstream_gradients
          return tape.gradient(gradient_injector_output, [filter_op])[0]
  
      self._assert_reproducible(gradient)
  
imports: import collections
  import numpy
  from tensorflow.python.eager import backprop
  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 nn_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 12 for calling the ConvolutionTest obj's underlying member method code with example usage: obj.testConvTransposeBackwardFilterGradient(rate) and returns: tape