auto dcast = b.create<DequantizeOp>(dq.getLoc(), dq.getResult().getType(),
                                          dq.getArg());
      dq.getResult().replaceAllUsesWith(dcast);
      if (auto extra_attr = op->getAttr(mlir::quant::kVolatileOpAttrName)) {
        dcast->setAttr(mlir::quant::kVolatileOpAttrName, extra_attr);
      }
      dq.erase();
    } else if (auto q = llvm::dyn_cast<quantfork::QuantizeCastOp>(op)) {

  LogicalResult matchAndRewrite(TF::CustomAggregatorOp op,
                                PatternRewriter &rewriter) const override {
    FloatAttr min = mlir::dyn_cast_or_null<FloatAttr>(op->getAttr("min"));
    FloatAttr max = mlir::dyn_cast_or_null<FloatAttr>(op->getAttr("max"));

    // When there are no min and max attributes, remove op.
    if (min == nullptr || max == nullptr) {
      op.getOutput().replaceAllUsesWith(op.getInput());

    OP_REQUIRES_OK(context,
                   context->GetAttr("output_file_path", &output_file_path));
    OP_REQUIRES_OK(context, context->env()->NewWritableFile(output_file_path,
                                                            &output_file_));

    OP_REQUIRES_OK(context, context->GetAttr("ids", &ids_));
    OP_REQUIRES_OK(context, context->GetAttr("calibration_methods",

  // Check that there are no duplicated exported_names.
  DenseMap<StringRef, Operation *> exported_name_to_op;
  for (auto &op : module) {
    auto attr = op.getAttr(exported_names_ident);
    if (!attr) continue;
    // If this verifier is called before we verify the
    // 'tf_saved_model.exported_names' attribute, then it might be invalid.

                        ctx->GetAttr("Tconstants", &constant_types));
  std::vector<int> constants(constant_types.size());
  std::iota(constants.begin(), constants.end(), 0);
  return constants;
}

std::vector<int> ResourcesVector(OpKernelConstruction* ctx) {
  DataTypeVector constant_types;
  OP_REQUIRES_OK_RETURN(ctx, std::vector<int>(),
                        ctx->GetAttr("Tconstants", &constant_types));

    cc_type v;                                                                 \
    auto* cc_ctx = reinterpret_cast<::tensorflow::OpKernelConstruction*>(ctx); \
    absl::Status s = cc_ctx->GetAttr(attr_name, &v);                           \
    ::tensorflow::Set_TF_Status_from_Status(status, s);                        \
    if (s.ok()) {                                                              \

  void runOnOperation() override {
    auto module = getOperation();
    // If TFLite variable legalization is not allowed, then we skip this pass.
    if (auto legalize_tfl_variables_attr =
            module->getAttr(kLegalizeTflVariables)) {
      if (!mlir::cast<BoolAttr>(legalize_tfl_variables_attr).getValue()) return;
    }

    RewritePatternSet patterns(&getContext());
    populateWithGenerated(patterns);

             << type_string() << " on an XLA device. This should never happen.";
}

XlaAssignVariableOp::XlaAssignVariableOp(OpKernelConstruction* c)
    : OpKernel(c) {
  OP_REQUIRES_OK(c, c->GetAttr("dtype", &dtype_));
}

void XlaAssignVariableOp::Compute(OpKernelContext* context) {
  OP_REQUIRES(context, dtype_ == context->input(1).dtype(),
              errors::InvalidArgument(

    OpBuilder builder(op.getContext());
    SmallVector<Type> result_types(op.getResultTypes().begin(),
                                   op.getResultTypes().end());
    if (!op->getAttr("layout")) {
      auto layout = mlir::GetTPUInfeedLayout(result_types, builder);
      if (failed(layout)) return;

      op->setAttr("layout", layout.value());
    }
  }
};

    }
  }

  if (!in_order) {
    // Functions do not get reused in practice, so skip the check for if the
    // callee has been updated.
    StringAttr callee_sym = cluster_func_op.getFuncAttr().getAttr();
    MoveResourceArgsToEnd(symtab.lookup<func::FuncOp>(callee_sym));
  }
  builder.setInsertionPoint(cluster_func_op);
  auto xla_launch_op = builder.create<TF::XlaLaunchOp>(