func.func @transposeConv2DBackpropInput_f32(
  %input_size:   tensor<4xi32>,
  %filter:       tensor<1x28x28x64xf32>,
  %out_backprop: tensor<1x28x28x64xf32>
) -> tensor<1x28x28x64xf32> {

  // CHECK: "tf.Conv2DBackpropInput"
  // CHECK-SAME: data_format = "NCHW"
  %0 = "tf.Conv2DBackpropInput"(%input_size, %filter, %out_backprop)
       {
         data_format = "NHWC",

func.func @transposeConv2DBackpropInput_f16(
  %input_size:   tensor<4xi32>,
  %filter:       tensor<1x28x28x64xf16>,
  %out_backprop: tensor<1x28x28x64xf16>
) -> tensor<1x28x28x64xf16> {

  // CHECK: "tf.Conv2DBackpropInput"
  // CHECK-SAME: data_format = "NCHW"
  %0 = "tf.Conv2DBackpropInput"(%input_size, %filter, %out_backprop)
       {
         data_format = "NHWC",
         padding = "VALID",

	for _, test := range tests {
		t.Run(fmt.Sprintf("inputSize=%d limit=%d writes=%d", test.inputSize, test.limit, test.writeSize), func(t *testing.T) {
			input := make([]byte, test.inputSize)
			r.Read(input)
			output := &bytes.Buffer{}
			w := LimitWriter(output, test.limit)

			var (
				err     error
				written int64
				n       int
			)
			for written < test.inputSize && err == nil {

  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