// Remove the call_op to finish the op expansion.
    call_op.erase();
    changed |= true;
    return WalkResult::advance();
  });

  if (walk_result.wasInterrupted()) {
    signalPassFailure();
    return failure();
  }

  // If `changed` is false, it is considered as a failure, so the recursive
  // rewrite will stop.
  return success(changed);
}

  // to make things more modular.
  if (failed(ValidateOp(func))) {
    func.emitError() << "tfl-prepare-tf pass failed.";
    signalPassFailure();
    return;
  }

  if (failed(ConvertTf2XlaOps(func, ctx))) {
    signalPassFailure();
    return;
  }

  // This pattern will try to identify and optimize for dilated convolution.

    if (quant_specs_.inference_type == tensorflow::DT_INVALID) {
      func.emitError() << "prepare-quantize pass failed: unsupported "
                          "inference type specification";
      signalPassFailure();
      return;
    }
    quant_specs_.post_training_quantization = post_training_quantize_;
    quant_specs_.legacy_float_scale = legacy_float_scale_;
    quant_specs_.disable_set_input_nodes_quantization_params =

                        {producer, min_consumer, bad_consumers})));
    break;
  }

  if (failed(applyFullConversion(module.getOperation(), target, finalPatterns)))
    signalPassFailure();

  // The uses of arg control dependency has been dropped. We can safely remove
  // the block argument here.
  module.walk([&](tf_executor::GraphOp graph) {
    graph.getBody().front().eraseArguments(

  addPatterns(context, stage1Patterns, this->preserve_assert_op_);

  FrozenRewritePatternSet stage1FrozenPatterns(std::move(stage1Patterns));
  if (!applyPatterns(func, target, stage1FrozenPatterns)) {
    return signalPassFailure();
  }
  // Explict BroadcastTo addition for left-over broadcast-able ops.
  // The following pattern matchings should be done after the other legalization
  // rules in order not to add unnecessary BroadcastTo ops.

void LegalizeTFCommunication::runOnOperation() {
  auto module = getOperation();
  llvm::SmallDenseMap<StringRef, FuncToRewrite> funcs_to_rewrite;
  if (failed(GetFunctionsToRewrite(module, funcs_to_rewrite)))
    return signalPassFailure();

  OpBuilder builder(&getContext());
  for (const auto& func_and_name : funcs_to_rewrite) {
    const auto& func_to_rewrite = func_and_name.getSecond();
    func::FuncOp func = func_to_rewrite.original;

    if (IsTpuRegularOp(op)) {
      success &= IsValidMAXIMALSharding(op, metadata_map);
      success &= IsValidShardingTupleForArity(op);
    }
    success &= !HasSingleCoreTpu(op);
    if (!success) {
      signalPassFailure();
    }
  });
}

}  // anonymous namespace

std::unique_ptr<OperationPass<ModuleOp>> CreateTPUValidateInputsPass() {
  return std::make_unique<TPUValidateInputsPass>();
}

  RuntimeDevices devices;
  if (failed(::tensorflow::GetDevicesFromOp(func->getParentOfType<ModuleOp>(),
                                            &devices)))
    return signalPassFailure();

  // If there is no runtime device information and data format is not explicitly
  // forced, there is nothing to do.
  if (devices.NumDevices() == 0 && force_data_format_.empty()) return;

  if (!shouldKeepUnusedQdqPattern()) {
    RewritePatternSet patterns_2(&getContext());
    patterns_2.add<RemoveUnusedQdqPattern>(ctx);
    if (failed(applyPatternsAndFoldGreedily(func, std::move(patterns_2)))) {
      signalPassFailure();
    }
  }
}
}  // namespace

// Creates an instance of the TensorFlow dialect Quantize pass.
std::unique_ptr<OperationPass<func::FuncOp>> CreateQuantizePass() {
  QuantizationSpecs quant_specs;

              execute_launch, resource_analysis_info,
              /*check_device=*/!parent_is_replicate,
              /*check_same_region=*/parent_is_replicate, &builder))) {
        signalPassFailure();
        return;
      }
    }
  });
}

}  // namespace

std::unique_ptr<OperationPass<ModuleOp>>
CreateTPUMergeVariablesWithExecutePass() {