mlir::Operation *op = use.getOwner();
    if (llvm::isa<TF::TensorListGetItemOp>(op) ||
        llvm::isa<TF::TensorListLengthOp>(op) ||
        llvm::isa<TF::TensorListPushBackOp>(op) ||
        llvm::isa<TF::TensorListReserveOp>(op) ||
        llvm::isa<TF::TensorListSetItemOp>(op) ||
        llvm::isa<TF::TensorListStackOp>(op) ||
        llvm::isa<TF::TensorListResizeOp>(op)) {

      operand = rewriter.create<CastOp>(op.getLoc(), tensor_type_f32, operand);
    }
    Value result = rewriter.create<MatMulOp>(
        op.getLoc(), op.getProduct().getType(), operands[0], operands[1],
        op.getTransposeA(), op.getTransposeB());

    rewriter.replaceOp(op, {result});
    return success();
  }
};

// Lowers _UnaryOpsComposition op as a series of original TensorFlow ops that

      })
      .wasInterrupted();
}

// Returns whether `op` or any ancestors of `op` are outside compiled.
bool HasOutsideCompilationAncestor(Operation* op) {
  while (op) {
    if (op->hasAttr(kXlaOutsideCompilationAttr)) {
      return true;
    }
    op = op->getParentOp();
  }
  return false;
}

// Returns whether any ancestors of `op` are outside compiled.

  return BinaryGradCommon(scope, op, grad_outputs, gx_1, gx_2);
}

Status MaximumGrad(const Scope& scope, const Operation& op,
                   const std::vector<Output>& grad_inputs,
                   std::vector<Output>* grad_outputs) {
  auto comparator = GreaterEqual(scope, op.input(0), op.input(1));
  return MaximumMinimumGradCommon(scope, op, grad_inputs, grad_outputs,

}

// Returns true if this is a basic LSTM op.
bool IsBasicLSTMOp(tflite::BuiltinOptionsUnion op_union) {
  if (const auto* op = op_union.AsLSTMOptions()) {
    return op->kernel_type == tflite::LSTMKernelType_BASIC;
  } else {
    return false;
  }
}

// Gets the MLIR op name with the dialect name for the flatbuffer operator.
std::string GetMlirOpName(const tflite::OperatorT& op,

    for (auto& op : graph->operators) {
      for (int idx = 0; idx < op->inputs.size(); idx++) {
        if (op->inputs[idx] < 0) {
          continue;
        }
        const auto& tensor = graph->tensors[op->inputs[idx]];
        auto* expected_tensor = FindMatchingExpectedTensor(
            *expected_graph, expected_model, model, *op, idx);
        if (!expected_tensor) {
          continue;

  return num_ops;
}

std::vector<Value> ResultsAsVector(Operation* op) {
  std::vector<Value> vec;
  vec.reserve(op->getNumResults());
  for (auto res : op->getResults()) vec.push_back(res);
  return vec;
}

void SetBasicBlockAttributes(OpBuilder& builder, Operation* op) {
  op->setAttr(kDevice, builder.getStringAttr(""));
  op->setAttr(kLower, builder.getBoolAttr(true));
}

  // to begin with. So we can safely replace each old op result with the
  // corresponding new op result.
  int old_num_results = op_->getNumResults();
  op_->replaceAllUsesWith(new_op->getResults().take_front(old_num_results));
  op_->erase();
  op_ = nullptr;
}

// Lift resource load and stores out of regions attached to `op`, where op is
// an If/case/cluster op.

// Returns success if all the operations in the `op`'s regions including `op`
// itself are legal in a TFLite pipeline.
LogicalResult ValidateOp(Operation *op) {
  bool has_illegal_ops = false;
  op->walk([&](Operation *op) {
    if (isa<TF::VariableV2Op>(op)) {
      has_illegal_ops = true;
      op->emitOpError() << "is illegal in a TFLite pipeline";
    }
  });

		return OP(0, 6)
	case ASRA:
		return OP(0, 7)
	case AROTR:
		return OP(8, 6)
	case ASLLV:
		return OP(2, 4)
	case ASRLV:
		return OP(2, 6)
	case ASRAV:
		return OP(2, 7)
	case AROTRV:
		return OP(10, 6)
	case AADDV:
		return OP(5, 4)
	case AADDVU:
		return OP(5, 5)
	case ASUBV:
		return OP(5, 6)
	case ASUBVU, ANEGV:
		return OP(5, 7)
	case AREM,