// these predicates.

// Returns true if the given TensorFlow op does not have a `data_format`
// attribute (then default to "NHWC"), or its `data_format` attribute is "NHWC".
inline bool TFDataFormatIsNHWC(Operation *op) {
  auto attr = op->getAttrOfType<StringAttr>("data_format");
  return !attr || attr.getValue() == "NHWC";
}

// Returns true if the given TensorFlow op does not have a `data_format`

  llvm::DenseMap<SymbolRefAttr, int> called_indexes;

  WalkResult result = module.walk([&](stablehlo::CustomCallOp op) {
    if (!IsTfFuncCustomCall(op)) {
      return WalkResult::advance();
    }

    auto backend_config =
        op->getAttrOfType<DictionaryAttr>(kTfBackendConfigAttrName);
    if (!backend_config) {
      op->emitOpError() << "is missing attribute '" << kTfBackendConfigAttrName
                        << "'";

Operation* GetUserIfOnlyOne(Operation* op) {
  if (op->getNumResults() != 1) return nullptr;
  auto result = op->getResult(0);
  if (!result.hasOneUse()) return nullptr;
  return (*result.getUses().begin()).getOwner();
}

// Gets operation providing value for the given operand of given operation
// if the given operation is the only user.
Operation* GetInputOpWithOneUse(Operation* op, int opr_num) {

}

// 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,

  auto xla_compile = NodeWith(Op("_XlaCompile"), Attr("must_compile", false));
  auto predicated_compilation_key =
      NodeWith(Op("Switch"), Inputs(Out(0, xla_compile), Out(1, xla_compile)));
  auto xla_run =
      NodeWith(Op("_XlaRun"), Inputs(Out(1, predicated_compilation_key)));
  auto tf_call =
      NodeWith(Op("StatefulPartitionedCall"),
               CtrlDeps(NodeWith(Op("Identity"),

void EraseResults(Operation* op, llvm::BitVector erase) {
  assert(!op->getNumRegions());
  std::vector<Type> result_types;
  for (auto result : op->getResults()) {
    if (!erase[result.getResultNumber()]) {
      result_types.push_back(result.getType());
    }
  }
  OpBuilder builder(op);
  builder.setInsertionPointAfter(op);
  OperationState state(op->getLoc(), op->getName().getStringRef(),

                                 parsed_op_name, attrs, node_def))) {
      return failure();
    }
    if (parsed_op_name != tf_op_name) {
      return op.emitOpError(
          "TF op names in 'custom_code' and 'custom_option' don't match");
    }
    const tensorflow::OpDef* op_def;

    // This will fail only if the op is not a registered TensorFlow op.

      rewriter.replaceOpWithNewOp<mhlo::GatherOp>(
          op, op.getType(), op.getParams(), op.getIndices(), dims_attr,
          GetI64ElementsAttr(slice_sizes, &rewriter));
    } else {
      rewriter.replaceOpWithNewOp<mhlo::DynamicGatherOp>(
          op, op.getType(), op.getParams(), op.getIndices(), slice_sizes_value,
          dims_attr);
    }
    return success();
  }
};

template <typename OpTy>
mlir::LogicalResult VerifyExecuteOpCommon(OpTy op) {
  auto op_attr_array = op.getOpAttrs().getValue();
  for (auto op_attr : op_attr_array) {
    auto key_value = mlir::dyn_cast<mlir::ArrayAttr>(op_attr);
    if (!key_value || key_value.getValue().size() != 2 ||
        !mlir::isa<mlir::StringAttr>(key_value.getValue()[0]))
      return op.emitOpError() << "each op_attr should be a key-value pair, "

  TFE_OpSetAttrType(op, "T", TFE_TensorHandleDataType(in));
  TFE_OpSetAttrInt(op, "group_size", group_size);
  TFE_OpSetAttrInt(op, "group_key", 123);
  TFE_OpSetAttrInt(op, "instance_key", 456);
  TFE_OpSetAttrString(op, "merge_op", "Add", 3);
  TFE_OpSetAttrString(op, "final_op", "Id", 2);
  std::vector<int64_t> subdiv_offsets;
  TFE_OpSetAttrIntList(op, "subdiv_offsets", subdiv_offsets.data(),