TF_RETURN_IF_ERROR(HandleInputOutputArraysWithModule(model_flags, &module));
  }

  mlir::TFL::PassConfig pass_config(quant_specs);
  bool emit_builtin_tflite_ops = !toco_flags.force_select_tf_ops();
  pass_config.emit_builtin_tflite_ops = emit_builtin_tflite_ops;
  pass_config.enable_tflite_variables =
      toco_flags.enable_tflite_resource_variables();
  pass_config.unfold_batch_matmul = toco_flags.unfold_batchmatmul();

      return kTrFailure;
    }
    quant_specs.serialized_quant_stats = file->getBuffer().str();
  }

  mlir::TFL::PassConfig pass_config(quant_specs);
  pass_config.emit_builtin_tflite_ops = emit_builtin_tflite_ops;
  pass_config.lower_tensor_list_ops = lower_tensor_list_ops;
  pass_config.unfold_batch_matmul = unfold_batchmatmul;
  pass_config.unfold_large_splat_constant = unfold_large_splat_constant;

  // The tensorflow list is not supported right now by that pass.
  // Enable fusing composite ops that can be lowered to built-in TFLite ops.
  if (pass_config.emit_builtin_tflite_ops &&
      toco_flags.tf_quantization_mode().empty()) {
    pass_manager->addPass(mlir::TFL::CreatePrepareCompositeFunctionsPass());
  }

  pass_manager->addPass(mlir::createInlinerPass());

  }

  // TODO: b/176267167 - Quantize flex fallback in the MLIR pipeline
  if (quant_specs.weight_quantization &&
      (!quant_specs.RunAndRewriteDynamicRangeQuantizationPasses() ||
       !pass_config.emit_builtin_tflite_ops)) {
    // Apply post-training dynamic range quantization from the old TOCO
    // quantizer.Once MLIR has support for this, we can remove this if
    // statement.