mlir::quant::QuantizedType::castToExpressedType(input_type);
        builder->setInsertionPoint(op);
        auto dequantize_op = builder->create<TFL::DequantizeOp>(
            op->getLoc(), dequantized_input_type, input.get());
        dequantized_inputs.push_back(dequantize_op);
      } else {
        dequantized_inputs.push_back(input.get());
      }
    }

    // Result types.

      rewriter.setInsertionPointAfter(op);
      // create new F16 constant op in that location
      ConstantOp new_const = rewriter.create<ConstantOp>(
          op->getLoc(), new_result_type, new_value_attr);
      ConvertOp dcast =
          rewriter.create<ConvertOp>(op->getLoc(), old_result_type, new_const);
      // replace all convert ops with dq op.
      convert_op->replaceAllUsesWith(dcast);

      auto dynamic_key =
          rewriter.create<TF::_XlaCompileMlirPlaceholderProgramKeyOp>(
              func.getLoc(), /*program=*/result_type, llvm::ArrayRef<Value>{});

      auto recv_at_host = rewriter.create<TF::_XlaRecvAtHostOp>(
          func.getLoc(), op.getOperandTypes(), /*dynamic_key=*/dynamic_key,
          op.getSendKeyAttr(),
          /*device_ordinal=*/rewriter.getI64IntegerAttr(0),

        mlir::isa<quant::QuantizedType>(element_type)) {
      return;
    }
    op_builder->setInsertionPoint(const_op);
    Value scalar = op_builder->create<mhlo::ConstantOp>(
        const_op->getLoc(),
        DenseElementsAttr::get(
            RankedTensorType::get(/*shape=*/{}, element_type),
            splat_elements_attr.getSplatValue<Attribute>()));
    auto broadcast_dims = DenseIntElementsAttr::get(

    BlockArgument param = callee.getArgument(i);
    if (mlir::isa<TF::ResourceType>(getElementTypeOrSelf(param.getType()))) {
      removed_params.set(i);
      callee.getBody().addArgument(param.getType(), param.getLoc());
      param.replaceAllUsesWith(callee.getArguments().back());
      removed_params.push_back(false);
    }
  }
  // Remove old resource-type parameters.
  callee.getBody().front().eraseArguments(removed_params);

    auto name_from_loc = mlir::GetNameFromLoc(op->getLoc());
    if (!name_from_loc.empty()) return name_from_loc;
    // If the location is none of the expected types, then simply use name
    // generated using the op type.
    return std::string(op->getName().getStringRef());
  }
  auto val = op_or_val.dyn_cast<mlir::Value>();
  auto name_from_loc = mlir::GetNameFromLoc(val.getLoc());
  if (!name_from_loc.empty()) return name_from_loc;

}

static Value CreateTranspose(OpBuilder& builder, Value source,
                             ArrayRef<int64_t> perm) {
  return builder.create<stablehlo::TransposeOp>(source.getLoc(), source, perm)
      ->getResult(0);
}

// Transform pad(transpose(x)) to transpose(pad(x))
struct TransposeCommuteWithPad : public OpRewritePattern<stablehlo::PadOp> {

    auto new_reshape_type = RankedTensorType::get(new_reshape_dims, el_ty);
    ReshapeOp new_reshape =
        rewriter.create<ReshapeOp>(new_reshape_shape.getLoc(), new_reshape_type,
                                   op.getInput(), new_reshape_shape);
    TF::ConstOp new_broadcast_shape =
        GetI64ConstantTensor(rewriter, reshape_shape, op.getLoc());

  for (Value result : new_launch_op_results)
    new_launch_op_results_types.push_back(result.getType());

  auto new_launch_op = builder.create<tf_device::LaunchOp>(
      old_launch_op->getLoc(), old_launch_op->getDeviceAttr(),
      /*result_types=*/new_launch_op_results_types);

  new_launch_op.getBody().takeBody(old_launch_op->getBody());

def CreateTFReadVariableOpFromResourceHandle : NativeCodeCall<
    "$_builder.create<TF::ReadVariableOp>("
    "$0.getLoc(), GetResourceSubtype($1), $1)">;

def CreateTFSelectOp: NativeCodeCall<
    "$_builder.create<TF::SelectOp>("
    "$0.getLoc(), $3.getType(), $1, $2, $3)">;

def ConstAttrIfThenElse: NativeCodeCall<
    "($0).getValue() ? ($1) : ($2)">;