legalize_to_tfl = false;
        return WalkResult::interrupt();
      }
    }
    return WalkResult::advance();
  });
  module->setAttr(kLegalizeTflVariables,
                  BoolAttr::get(context, legalize_to_tfl));
}

}  // namespace

std::unique_ptr<OperationPass<ModuleOp>> CreateAnalyzeVariablesPass() {
  return std::make_unique<AnalyzeVariablesPass>();
}

}  // namespace TFL

  // to stateful, otherwise the op will not be executed.
  OpBuilder builder(&ctx);
  module_op.walk([&builder](Operation* op) {
    if (op->hasAttrOfType<BoolAttr>("is_stateless") &&
        ContainCalibrationStatisticsSaverOp(op)) {
      op->setAttr("is_stateless", builder.getBoolAttr(false));
    }
  });
}

std::unique_ptr<OperationPass<ModuleOp>>

    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());
    Type res_type = tf_op.getType();
    TypeAttr qtype = quant::GetQuantizedTypeAttr(
        rewriter, res_type, min_value, max_value, quant_dim, num_bits,

    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());
    Type res_type = tf_op.getType();
    TypeAttr qtype = quant::GetQuantizedTypeAttr(
        rewriter, input_type, min_value, max_value, quant_dim, num_bits,

    This op returns a scalar string tensor as a key for user to look for the loaded array
    and a future containing the restored tensor.
  }];

  let arguments = (ins
    TF_Tensor:$variable,
    DefaultValuedAttr<BoolAttr, "false">:$used_by_host
  );

  let results = (outs
    TF_Tensor:$array_key,
    MlrtFutureType: $tensor_future
  );
}

    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());
    Type res_type = tf_op.getType();
    TypeAttr qtype = quant::GetQuantizedTypeAttr(
        rewriter, res_type, min_value, max_value, quant_dim, num_bits,

      failed(AddFloatAttr(func, attrs, "h_scale", &fbb)) ||
      failed(AddFloatAttr(func, attrs, "w_scale", &fbb)))
    return failure();
  auto use_regular_nms =
      mlir::dyn_cast_or_null<BoolAttr>(attrs.get("use_regular_nms"));
  if (!use_regular_nms) {
    return func.emitError()
           << "use_regular_nms attribute is not set or not a bool";
  }
  fbb.Int("use_regular_nms", use_regular_nms.getValue());

    Other ops executed by TFRT may make use of $tensor_future.
  }];

  let arguments = (ins
    Arg<TF_Tensor, [{The variable tensor to be loaded}], []>:$variable,
    DefaultValuedAttr<BoolAttr, "false">:$used_by_host
  );

  let results = (outs
    TF_StrTensor:$array_key,
    TF_Tensor: $tensor_future
  );

  TF_DerivedOperandTypeListAttr Tin = TF_DerivedOperandTypeListAttr<0>;

      .def("create", [](mlir::OpBuilder& opb, mlir::Location loc, mlir::Value x,
                        mlir::Value y) {
        return opb
            .create<mlir::TF::NotEqualOp>(
                loc, x, y, mlir::BoolAttr::get(opb.getContext(), true))
            .getOperation();
      });

  // mlir::TF::SubOp
  py::class_<mlir::TF::SubOp>(m, "Tf_SubOp")
      .def("create",

        "getElementTypeOrSelf($_self.cast<ElementsAttr>().getType()).isa<FloatType>()">,
        "float constant tensor">;

// Checks if the boolean value is false.
def IsFalseBoolAttr : AttrConstraint<
  CPred<"!$_self.cast<BoolAttr>().getValue()">>;

// Checks if the value has only one user.
def HasOneUse : Constraint<CPred<"$0.hasOneUse()">>;

// Gets the type of a value.
def GetValueType : NativeCodeCall<"$0.getType()">;