let description = [{
    A recurrent neural network specified by an RNN cell. This Op takes in input
    in a format {batch_size, seq_len, input_size} or
    {seq_len, batch_size, input_size} if it's time-majored.

    It implements the following operation for
    each element in the sequence s = 1...sequence_length:
      outputs[s] = state = activation(RNNOp(inputs[s]))

  context.getOrLoadDialect<TF::TensorFlowDialect>();
  const std::string input_file =
      "tensorflow/compiler/mlir/lite/metrics/testdata/strided_slice.mlir";
  auto input_file_id = StringAttr::get(&context, input_file);

  context.enableMultithreading();

  auto module =
      LoadModule(&context, tensorflow::GetDataDependencyFilepath(input_file));
  EXPECT_EQ(module.ok(), true);

        assertEquals("test1 \" test2", list.get(1));

        cmd = "test.sh $INPUT_FILE";
        params.clear();
        list = extractor.parseCommand(cmd, params);
        assertEquals(2, list.size());
        assertEquals("test.sh", list.get(0));
        assertEquals("$INPUT_FILE", list.get(1));

        cmd = "test.sh $INPUT_FILE $OUTPUT_FILE";
        params.clear();
        list = extractor.parseCommand(cmd, params);

    return errors::InvalidArgument(
        "Could not open input file ",
        string(input_filename.data(), input_filename.size()).c_str());
  }

  const auto& input_file = *file_or_err;
  absl::string_view content(input_file->getBufferStart(),
                            input_file->getBufferSize());
  return LoadProtoFromBuffer(content, proto);
}

Status LoadProtoFromFile(absl::string_view input_filename,

  TF_ASSERT_OK(mlir::TF::RunBridgeWithStandardPipeline(
      module_ref.get(),
      /*enable_logging=*/true, /*enable_inliner=*/false));

  std::string errorMessage;
  auto input_file = mlir::openInputFile(filepath, &errorMessage);
  EXPECT_THAT(input_file, Not(IsNull()));

  auto output_stream = mlir::openOutputFile(output_dump, &errorMessage);
  EXPECT_THAT(output_stream, Not(IsNull()));

  mlir::PassPipelineCLParser passPipeline(

      // $filter_sizes
      CompileTimeConstantOperand<TF::Conv2DBackpropFilterOp, 1>,
      CompileTimeConstantOperand<TF::Conv2DBackpropInputOp, 0>,  // $input_sizes
      // $filter_sizes
      CompileTimeConstantOperand<TF::Conv3DBackpropFilterV2Op, 1>,
      // $input_sizes
      CompileTimeConstantOperand<TF::Conv3DBackpropInputV2Op, 0>,
      // $group_assignment
      CompileTimeConstantOperand<TF::CrossReplicaSumOp, 1>,

        private FreeListBlock prev;
        private FreeListBlock next;

        @Override
        protected int getSize() {
            return Block.LONG_SIZE + Block.INT_SIZE + Block.INT_SIZE + maxBlockEntries * (Block.LONG_SIZE
                    + Block.INT_SIZE);
        }

        @Override
        protected byte getType() {
            return 0x44;
        }

        @Override

def LegalizeConv2DBackpropInput : Pat<
  (TF_Conv2DBackpropInputOp $input_sizes, $filter, $out_backprop,
     IsIntList1XY1:$strides,
     BoolAttr:$use_cudnn_on_gpu,
     IsSameOrValid:$padding,
     I64ArrayAttr:$explicit_paddings,
     IsDataFormatNHWC:$data_format,
     IsAllOnes:$dilations),
  (TFL_TransposeConvOp $input_sizes,
     (TFL_TransposeOp $filter,

        if (StringUtil.isBlank(command)) {
            throw new CrawlerSystemException("command is empty.");
        }

        final Map<String, String> params = new HashMap<>();
        params.put("$INPUT_FILE", inputFile.getAbsolutePath());
        params.put("$OUTPUT_FILE", outputFile.getAbsolutePath());

        final List<String> cmdList = parseCommand(command, params);
        if (logger.isInfoEnabled()) {

						// at least one new coverage bit.
						c.queueForMinimization(result, keepCoverage)
					} else {
						// Update the coordinator's coverage mask and save the value.
						inputSize := len(result.entry.Data)
						entryNew, err := c.addCorpusEntries(true, result.entry)
						if err != nil {
							stop(err)
							break
						}
						if !entryNew {
							if shouldPrintDebugInfo() {