if (variable_name.empty()) {
      return signalPassFailure();
    }
    VarHandleOp var_handle_op = builder.create<VarHandleOp>(
        variable_v2_op.getLoc(),
        ArrayRef<Type>{RankedTensorType::get(
            {}, TF::ResourceType::get(ArrayRef<TensorType>{tensor_type},
                                      builder.getContext()))},
        ArrayRef<Value>{},

  if (IsKVCacheCompositeOp(composite)) {
    return builder.create<TFL::CustomOp>(
        composite->getLoc(), composite->getResultTypes(),
        composite->getOperands().slice(2, 3), composite.getName(),
        CustomOption(&builder, custom_option_buffer));
  }
  return builder.create<TFL::CustomOp>(
      composite->getLoc(), composite->getResultTypes(),
      composite->getOperands(), composite.getName(),

    // and its users.
    Value value = tf_op.getOutputs();
    auto quantize = rewriter.create<TFL::QuantizeOp>(
        tf_op.getLoc(), qtype.getValue(), value, qtype);
    auto dequantize = rewriter.create<TFL::DequantizeOp>(
        tf_op.getLoc(), res_type, quantize.getOutput());
    value.replaceAllUsesWith(dequantize);
    quantize.getOperation()->replaceUsesOfWith(dequantize, value);

  Block* block = builder->createBlock(body);

  // Block arguments are scalars of the given element type.
  Type type = RankedTensorType::get(/*shape=*/{}, element_type);
  Location loc = body->getLoc();
  block->addArguments({type, type}, SmallVector<Location, 2>(2, loc));

  auto reducer =
      builder->create<Op>(loc, block->getArgument(0), block->getArgument(1));
  builder->create<ReturnOp>(loc, reducer.getResult());

        transposer.TransposeValues(original_values);

    // Create a new constant op with the transposed values.
    const Location combined_loc =
        rewriter.getFusedLoc({const_op.getLoc(), op.getLoc()});
    auto new_value_type =
        RankedTensorType::getChecked(combined_loc, transposer.GetTargetShape(),
                                     /*elementType=*/rewriter.getF32Type());
    auto new_value_attr =

LogicalResult ConvertCustomCallOp::matchAndRewrite(
    mhlo::CustomCallOp mhlo_custom_call, OpAdaptor adaptor,
    ConversionPatternRewriter& rewriter) const {
  auto tfl_custom = rewriter.create<TFL::CustomOp>(
      mhlo_custom_call.getLoc(), mhlo_custom_call.getResultTypes(),
      mhlo_custom_call.getInputs());
  tfl_custom.setCustomCodeAttr(
      rewriter.getStringAttr(mhlo_custom_call.getCallTargetName()));

     * method directly whenever you need the logger, it is fast enough and needs no caching.
     *
     * @see org.apache.maven.plugin.Mojo#getLog()
     * @deprecated Use SLF4J directly
     */
    @Deprecated
    @Override
    public Log getLog() {
        if (log == null) {
            log = new SystemStreamLog();
        }

        return log;
    }

    @Override

  for (Value result : subgraph.FuncOutputs()) {
    Value cloned_output = values_in_scope.lookup(result);
    return_operands.push_back(cloned_output);
  }
  function_builder.create<mlir::func::ReturnOp>(new_func.getLoc(),
                                                return_operands);
  ops_added.func_op = new_func;
  module.push_back(new_func);
  return new_func;
}

  std::unique_ptr<llvm::MemoryBuffer> mem_buffer;
  llvm::StringRef quantized_function_library =
      GetFunctionLibrary(quantization_method_, op_set_);

  if (quantized_function_library.empty()) {
    emitError(module.getLoc())
        << "Failed to get function library for the opset.";
    signalPassFailure();
    return;
  }

  mem_buffer =
      llvm::MemoryBuffer::getMemBuffer(quantized_function_library,

     * <code>"info"</code>, <code>"warn"</code>, and <code>"error"</code>.
     * @deprecated Use SLF4J directly
     */
    @Deprecated
    Log getLog();