func AddUintptr(addr *uintptr, delta uintptr) (new uintptr)

// AndInt32 atomically performs a bitwise AND operation on *addr using the bitmask provided as mask
// and returns the old value.
// Consider using the more ergonomic and less error-prone [Int32.And] instead.
func AndInt32(addr *int32, mask int32) (old int32)

// AndUint32 atomically performs a bitwise AND operation on *addr using the bitmask provided as mask

        ).astype('f4')

      @def_function.function
      def conv2d(self, input_tensor: core.Tensor) -> Mapping[str, core.Tensor]:
        """Performs a 2D convolution operation.

        Args:
          input_tensor: Input tensor to perform convolution on.

        Returns:
          A map of: output key -> output result.
        """
        scale = [1.0] * self.out_channel_size

	Store8(&u.value, value)
}

// And takes value and performs a bit-wise
// "and" operation with the value of u, storing
// the result into u.
//
// The full process is performed atomically.
//
//go:nosplit
func (u *Uint8) And(value uint8) {
	And8(&u.value, value)
}

// Or takes value and performs a bit-wise
// "or" operation with the value of u, storing
// the result into u.
//

    // If it's a BiasAdd, check that the conv op is the first input.
    if (bias_add && bias_add.getValue() == op) return bias_add;
  }
  // No BiasAddOps found among uses.
  return BiasAddOp();
}

// Performs a fusion of the following pattern(s), if possible:
//   <Contraction> + BiasAdd + <Activation> -> <FusedContraction>
//
// Note that fusion with activation is preferred, but a contraction and BiasAdd

			TypeMeta: runtime.TypeMeta{
				Kind:       kind.Kind,
				APIVersion: kind.GroupVersion().String(),
			},
		}
	}

	switch t := obj.(type) {
	case bufferedMarshaller:
		// this path performs a single allocation during write only when the Allocator wasn't provided
		// it also requires the caller to implement the more efficient Size and MarshalToSizedBuffer methods
		encodedSize := uint64(t.Size())

  return builder->create<TF::TPUCopyWithLayoutOp>(
      execute_launch.getLoc(), llvm::ArrayRef<Type>{input.getType()},
      llvm::ArrayRef<Value>{input, get_layout.getLayout()});
}

// Performs transformation for a non-replicated input.
void HandleInput(Value input, const int64_t execute_arg_index,
                 TF::TPUExecuteOp execute, tf_device::LaunchOp execute_launch,

	ResponseWriteTracker DurationTracker

	// DecodeTracker is used to track latency incurred inside the function
	// that takes an object returned from the underlying storage layer
	// (etcd) and performs decoding of the response object.
	// When called multiple times, the latency incurred inside to
	// decode func each time will be summed up.
	DecodeTracker DurationTracker
}

type latencyTrackersKeyType int

    use_tf2xla_fallback_ = tf2xla_fallback_device_type.has_value();
    if (tf2xla_fallback_device_type.has_value()) {
      device_type_ = tf2xla_fallback_device_type.value().str();
    }
  }
  /// Performs the lowering to XLA dialect.
  void runOnOperation() override;
};

#define GEN_PASS_DEF_LEGALIZETFMODULEPASS
#include "tensorflow/compiler/mlir/tf2xla/transforms/xla_legalize_tf_passes.h.inc"

}

// UniversalDeserializer can convert any stored data recognized by this factory into a Go object that satisfies
// runtime.Object. It does not perform conversion. It does not perform defaulting.
func (f CodecFactory) UniversalDeserializer() runtime.Decoder {
	return f.universal
}

	"k8s.io/apiserver/pkg/endpoints/handlers/responsewriters"
	"k8s.io/apiserver/pkg/endpoints/metrics"
	endpointsrequest "k8s.io/apiserver/pkg/endpoints/request"

	klog "k8s.io/klog/v2"
)

// watchEmbeddedEncoder performs encoding of the embedded object.
//
// NOTE: watchEmbeddedEncoder is NOT thread-safe.
type watchEmbeddedEncoder struct {
	encoder runtime.Encoder

	ctx context.Context