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(

 * limitations under the License.
 */
package org.gradle.cache.internal.btree;

public abstract class Block {
    static final int LONG_SIZE = 8;
    static final int INT_SIZE = 4;
    static final int SHORT_SIZE = 2;

    private BlockPayload payload;

    protected Block(BlockPayload payload) {
        this.payload = payload;
        payload.setBlock(this);
    }

         -> tensor<1x1x3x8xf32>

  func.return %0 : tensor<1x1x3x8xf32>
}

// CHECK-LABEL: func @transposeConv2DBackpropInput
func.func @transposeConv2DBackpropInput(
  %input_sizes: tensor<4xi32>,
  %filter: tensor<1x1x3x8xf32>,
  %out_backprop: tensor<1x32x32x8xf32>
) -> tensor<1x32x32x3xf32> {

  // CHECK: %[[INPUT_PERM:[0-9]*]] = "tf.DataFormatVecPermute"

        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

        nextBlock += length;
        return pos;
    }

    private final class BlockImpl extends Block {
        private static final int HEADER_SIZE = 1 + INT_SIZE; // type, payload size
        private static final int TAIL_SIZE = INT_SIZE;

        private BlockPointer pos;
        private int payloadSize;

        private BlockImpl(BlockPayload payload, BlockPointer pos) {
            this(payload);