DCHECK(insert_successful);
    (void)insert_successful;
    return key;
  }

  ExecutableClosure<ExecutableType, ClientType> Consume(const KeyT& key) {
    mutex_lock l(mutex_);
    auto it = closures_.find(key);
    DCHECK(it != closures_.end());
    ExecutableClosure<ExecutableType, ClientType> value = std::move(it->second);
    closures_.erase(it);

      py_function_lib_(ABSL_DIE_IF_NULL(py_function_lib)),  // Crash OK
      src_saved_model_path_(src_saved_model_path),
      function_aliases_(std::move(function_aliases)),
      tags_(std::move(tags)),
      signature_def_map_(std::move(signature_def_map)),
      signature_keys_(std::move(signature_keys)) {}

absl::Status CalibrationComponent::ExportToSavedModel(
    ModuleOp module_op, absl::string_view calibration_data_dir,

      std::vector<uint8_t>(expected_value.begin(), expected_value.end());
  input_model.buffers.push_back(std::move(model_metadata_buffer));
  auto metadata_t = std::make_unique<tflite::MetadataT>();
  metadata_t->name = tflite::optimize::kTfLiteReducedPrecisionKey;
  metadata_t->buffer = input_model.buffers.size() - 1;
  input_model.metadata.push_back(std::move(metadata_t));

  // Sparsify and create output model
  flatbuffers::FlatBufferBuilder output_builder;

  if (failed(applyPatternsAndFoldGreedily(func, std::move(patterns)))) {
    signalPassFailure();
  }

  RewritePatternSet patterns_2(&getContext());
  patterns_2
      .add<QuantizeConstPattern, ConvertQuantizeCastToUniformQuantizePattern,
           ConvertDequantizeCastToUniformDequantizePattern>(ctx);
  if (failed(applyPatternsAndFoldGreedily(func, std::move(patterns_2)))) {
    signalPassFailure();
  }
}

     *
     * @param x the index (1-based) of the column to move to
     * @return this Ansi instance
     */
    public Ansi cursorToColumn(final int x) {
        return appendEscapeSequence('G', Math.max(1, x));
    }

    /**
     * Moves the cursor up. If the parameter y is negative it moves the cursor down.
     *
     * @param y the number of lines to move up
     * @return this Ansi instance
     */

	&& clobber(move)
	=> (Zero {t} [n] dst1 mem)
(Move {t} [n] dst1 src1 move:(Move {t} [n] dst2 _ mem))
	&& move.Uses == 1
	&& isSamePtr(dst1, dst2) && disjoint(src1, n, dst2, n)
	&& clobber(move)
	=> (Move {t} [n] dst1 src1 mem)
(Zero {t} [n] dst1 vardef:(VarDef {x} move:(Move {t} [n] dst2 _ mem)))
	&& move.Uses == 1 && vardef.Uses == 1
	&& isSamePtr(dst1, dst2)
	&& clobber(move, vardef)

// Small moves
(Move [0] _ _ mem) => mem
(Move [1] dst src mem) => (MOVBstore dst (MOVBload src mem) mem)
(Move [2] {t} dst src mem) && t.Alignment()%2 == 0 =>
	(MOVHstore dst (MOVHload src mem) mem)
(Move [2] dst src mem) =>
	(MOVBstore [1] dst (MOVBload [1] src mem)
		(MOVBstore dst (MOVBload src mem) mem))
(Move [4] {t} dst src mem) && t.Alignment()%4 == 0 =>

  if (av_tensor == nullptr) {
    TF_ASSIGN_OR_RETURN(
        *tensor, MakeTensorFromPjRtBuffer(tensor->dtype(), tensor->shape(),
                                          std::move(buffer)));
  } else {
    av_tensor->SetBuffer(std::move(buffer));
  }
  return absl::OkStatus();
}

absl::StatusOr<xla::PjRtCApiClient*> GetPjRtCApiClient(
    const DeviceType& device_type) {

    arg_layout_ptrs[i] = &arg_layouts[i];
  }
  xla::CompileOnlyClient::AotXlaComputationInstance instance;
  instance.computation = &computation;
  instance.argument_layouts = std::move(arg_layout_ptrs);
  xla::Shape result_shape(pshape->result());
  instance.result_layout = &result_shape;
  absl::StatusOr<std::vector<std::unique_ptr<xla::AotCompilationResult>>>

        persistor,
    std::unique_ptr<DeviceCompilerClient<ExecutableType, ClientType>>
        compiler_client)
    : persistor_(std::move(persistor)),
      compiler_client_(std::move(compiler_client)) {
  cache_ = std::make_unique<DeviceCompilationCache<ExecutableType>>();
  async_compiler_threads_ = std::make_unique<tensorflow::thread::ThreadPool>(