private val source: BufferedSource,
  private val client: Boolean,
) : Closeable {
  private val continuation: ContinuationSource = ContinuationSource(this.source)
  private val hpackReader: Hpack.Reader =
    Hpack.Reader(
      source = continuation,
      headerTableSizeSetting = 4096,
    )

  @Throws(IOException::class)
  fun readConnectionPreface(handler: Handler) {
    if (client) {

    return 1 == (1 & (filter >> c));
  }

  // This is all essentially copied from ImmutableSet, but we have to duplicate because
  // of dependencies.

  // Represents how tightly we can pack things, as a maximum.
  private static final double DESIRED_LOAD_FACTOR = 0.5;

  /**
   * Returns an array size suitable for the backing array of a hash table that uses open addressing

    slice_outputs.push_back(slice_op.getResult());
  }
  return slice_outputs;
}

}  // namespace

// ================== Pack ========================

LogicalResult LowerPackIntoConcatReshape::matchAndRewrite(
    TFL::PackOp pack_op, PatternRewriter& rewriter) const {
  // Pack op should have same shape type.
  SmallVector<Value, 5> pack_inputs(pack_op.getValues());

 *  New: APIs to iterate and selectively clear the response cache.
 *  New: Support for SOCKS proxies.
 *  New: Support for `TLS_FALLBACK_SCSV`.
 *  New: Update HTTP/2 support to `h2-16` and `hpack-10`.
 *  New: APIs to prevent retrying non-idempotent requests.
 *  Fix: Drop NPN support. Going forward we support ALPN only.
 *  Fix: The hostname verifier is now strict. This is consistent with the hostname

    // expected-remark@+1 {{Cost: 5}}
    %11 = "tf.Pack"(%9, %0, %10, %2) {axis = 0 : i64, device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<4xi32>
    // expected-remark@+1 {{Cost: 1}}

        def writingOperations = pushTaskOperation.descendants {
            it.descriptor.displayName =~ /Store entry .+ in (local|remote) build cache/ ||
                it.descriptor.displayName =~ /Pack build cache entry .+/
        }
        writingOperations.each {
            assert !it.children
        }
        return writingOperations
    }

  // Import input_model to a MLIR module
  flatbuffers::FlatBufferBuilder input_builder;
  flatbuffers::Offset<tflite::Model> input_model_location =
      tflite::Model::Pack(input_builder, &input_model);
  tflite::FinishModelBuffer(input_builder, input_model_location);

  std::string serialized_model(
      reinterpret_cast<const char*>(input_builder.GetBufferPointer()),

  func.func @func_2_CPU_FLOAT(%arg0: tensor<100xf32>, %arg1: tensor<100xf32>) -> tensor<2x100xf32> attributes {tac.cost = 2.000000e+01 : f32, tac.device = "CPU", tac.inference_type = "FLOAT", tac.interface_name = "func_2"} {
    %0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, tac.device = "CPU", values_count = 2 : i32} : (tensor<100xf32>, tensor<100xf32>) -> tensor<2x100xf32>
    func.return %0 : tensor<2x100xf32>
  }

        "//tensorflow/lite/tools/optimize:testdata/mixed16x8.bin",
        "//tensorflow/lite/tools/optimize:testdata/multi_input_add_reshape.bin",
        "//tensorflow/lite/tools/optimize:testdata/pack.bin",
        "//tensorflow/lite/tools/optimize:testdata/single_avg_pool_min_minus_5_max_plus_5.bin",
        "//tensorflow/lite/tools/optimize:testdata/single_conv_no_bias.bin",

  %2 = "tfl.add"(%arg0, %arg3) {fused_activation_function = "RELU6", per_device_costs = {CPU = 5.0 : f32, GPU = 1.0 : f32}, tac.device = "GPU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  %3 = "tfl.pack"(%1, %2) {axis = 0 : i32, per_device_costs = {CPU = 2.0 : f32, GPU = -1.0 : f32}, values_count = 2 : i32, tac.device = "CPU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32>
  func.return %3 : tensor<2x1xf32>
})";