if (operand.getType() != expected_type) {
        operand = rewriter.create<TF::CastOp>(
            op.getLoc(), expected_type, operand,
            /*Truncate=*/rewriter.getBoolAttr(false));
      }
      casted_operands.push_back(operand);
    }

    auto call = rewriter.create<func::CallOp>(
        op->getLoc(), main_fn.getSymName(), main_fn.getResultTypes(),
        casted_operands);

  } else if (op_.NumOutputs() == 1) {
    RenderExecutionSingleOutput();
  } else {
    RenderExecutionMultipleOutputs();
  }
}

void OpImplementationRenderer::RenderInitialization() {
  // Create Op variable and initialize it
  Statement("AbstractOperationPtr $0(ctx->CreateOperation())",
            op_.VariableName());
  TFStatement(Call(op_.VariableName(), "Reset",

}

FunctionDefLibrary CreateFunctionDefLibWithConstFunction(const string& name) {
  FunctionDefLibrary fdef_lib;
  FunctionDef func = FunctionDefHelper::Create(
      /*function_name=*/name, /*in_def=*/{}, /*out_def=*/{"out: float"},
      /*attr_def*/
      {}, /*node_def=*/{FunctionDefHelper::Const("one", 1.0f)},
      /*ret_def=*/{{"out", "out:output:0"}});

                                                    bcast_dims);
    } else {
      return rewriter.notifyMatchFailure(bcast_op, "Unsupported element type.");
    }
    Value new_const_op =
        rewriter.create<mhlo::ConstantOp>(bcast_op.getLoc(), result);
    rewriter.replaceOp(bcast_op, {new_const_op});
    return success();
  }
};

using FoldBroadcastInDimBeforeMulOp =

  // context in tensorflow_accelerator_device_info(). Also,
  // tensorflow_accelerator_device_info() == nullptr is used as an IsCPU test.
  // We need XlaCpuDevice to be treated not as CPU because it allocates
  // XlaTensors, not regular Tensors.
  Status status = device->UseAcceleratorDeviceInfo();
  if (!status.ok()) {
    errors::AppendToMessage(&status, "while setting up ", DEVICE_GPU_XLA_JIT);

      return op->emitError("expected exactly one called_computation");

    auto decomposition = mlir::cast<FlatSymbolRefAttr>(calledComputations[0]);

    auto composite = rewriter.create<mlir::stablehlo::CompositeOp>(
        op.getLoc(), op.getResultTypes(), op.getOperands(), name.str(), attrs,
        decomposition.getValue());
    rewriter.replaceOp(op, composite.getResults());
    return success();
  }

  auto* ctx = func.getContext();
  patterns.add<PushDownDequantize>(ctx);
  if (failed(applyPatternsAndFoldGreedily(func, std::move(patterns)))) {
    signalPassFailure();
  }
}
}  // namespace

// Creates an instance of the TensorFlow Lite optimize op order pass.
std::unique_ptr<OperationPass<func::FuncOp>> CreateOptimizeOpOrderPass() {
  return std::make_unique<OptimizeOpOrderPass>();
}

  common_error_message_.clear();
  error_collector_->Clear();
}

void ErrorCollectorInstrumentation::runAfterPassFailed(Pass *pass,
                                                       Operation *module) {
  // Create a new error if no errors collected yet.
  if (error_collector_->CollectedErrors().empty() &&
      !common_error_message_.empty()) {
    error_collector_->ReportError(NewConverterErrorData(

    b.setInsertionPoint(op);
    if (auto dq = llvm::dyn_cast<DequantizeOp>(op)) {
      auto dcast = b.create<quantfork::DequantizeCastOp>(
          dq.getLoc(), dq.getOutput().getType(), dq.getInput());
      dq.getOutput().replaceAllUsesWith(dcast);
      dq.erase();
    } else if (auto q = llvm::dyn_cast<QuantizeOp>(op)) {
      auto qcast = b.create<quantfork::QuantizeCastOp>(
          q.getLoc(), q.getOutput().getType(), q.getInput());

  patterns->add<odml::ConvertCustomCallOp>(context);
  populateWithGenerated(*patterns);

  patterns->add<ConvertReduceOpToTFLiteArgmin, ConvertReduceOpToTFLiteArgmax>(
      context);
}

// Creates an instance of the pass.
std::unique_ptr<OperationPass<ModuleOp>> CreateLegalizeHloToTfLitePass() {
  return std::make_unique<LegalizeHloToTfLitePass>();
}

// Registers the pass implementation