if got, want := _SliceMin(s1), -5; got != want {
		panic(fmt.Sprintf("_Min(%v) = %d, want %d", s1, got, want))
	}

	s2 := []string{"aaa", "a", "aa", "aaaa"}
	if got, want := _SliceMin(s2), "a"; got != want {
		panic(fmt.Sprintf("_Min(%v) = %q, want %q", s2, got, want))
	}

	if got, want := _SliceMin(s2[:0]), ""; got != want {
		panic(fmt.Sprintf("_Min(%v) = %q, want %q", s2[:0], got, want))
	}
}

	_Imag:    {"imag", 1, false, expression},
	_Len:     {"len", 1, false, expression},
	_Make:    {"make", 1, true, expression},
	// To disable max/min, remove the next two lines.
	_Max:     {"max", 1, true, expression},
	_Min:     {"min", 1, true, expression},
	_New:     {"new", 1, false, expression},
	_Panic:   {"panic", 1, false, statement},
	_Print:   {"print", 0, true, statement},

	_Imag:    {"imag", 1, false, expression},
	_Len:     {"len", 1, false, expression},
	_Make:    {"make", 1, true, expression},
	// To disable max/min, remove the next two lines.
	_Max:     {"max", 1, true, expression},
	_Min:     {"min", 1, true, expression},
	_New:     {"new", 1, false, expression},
	_Panic:   {"panic", 1, false, statement},
	_Print:   {"print", 0, true, statement},

              x,
              min=ops.convert_to_tensor(self._min[0]),
              max=ops.convert_to_tensor(self._max[0]),
              num_bits=8,
              narrow_range=False,
          )
          y = array_ops.fake_quant_with_min_max_vars(
              y,
              min=ops.convert_to_tensor(self._min[1]),
              max=ops.convert_to_tensor(self._max[1]),

          self._bias = array_ops.constant(
              np.random.uniform(size=bias_shape), dtype=dtypes.float32
          )

        self._kernel = np.random.uniform(size=y_shape).astype('f4')
        self._min = (-0.8, -0.8, -0.9)
        self._max = (0.9, 0.9, 1.0)

      @def_function.function(
          input_signature=[
              tensor_spec.TensorSpec(

			return
		}
		if nargs < min || min+1 < nargs {
			check.errorf(call, WrongArgCount, invalidOp+"%v expects %d or %d arguments; found %d", call, min, min+1, nargs)
			return
		}

		types := []Type{T}
		var sizes []int64 // constant integer arguments, if any
		for _, arg := range argList[1:] {

        x = array_ops.fake_quant_with_min_max_vars(
            x,
            min=ops.convert_to_tensor(self._min[0]),
            max=ops.convert_to_tensor(self._max[0]),
            num_bits=8,
            narrow_range=False,
        )
        y = array_ops.fake_quant_with_min_max_vars(
            y,
            min=ops.convert_to_tensor(self._min[1]),
            max=ops.convert_to_tensor(self._max[1]),
            num_bits=8,

			return
		}
		if nargs < min || min+1 < nargs {
			check.errorf(call, WrongArgCount, invalidOp+"%v expects %d or %d arguments; found %d", call, min, min+1, nargs)
			return
		}

		types := []Type{T}
		var sizes []int64 // constant integer arguments, if any
		for _, arg := range argList[1:] {

				metrics.LinearBuckets(1, 1, 59),      // 1s, 2s, 3s, ... 59s
				metrics.LinearBuckets(60, 5, 12),     // 60s, 65s, 70s, ... 115s
				metrics.LinearBuckets(120, 30, 7),    // 2min, 2.5min, 3min, ..., 5min
			),
			StabilityLevel: metrics.ALPHA,
		},
	)

	// EndpointChangesPending is the number of pending endpoint changes that
	// have not yet been synced to the proxy.

| `minio_node_cpu_avg_load5`           | CPU load average 5min.                     |
| `minio_node_cpu_avg_load5_avg`       | CPU load average 5min (avg).               |
| `minio_node_cpu_avg_load5_max`       | CPU load average 5min (max).               |
| `minio_node_cpu_avg_load5_perc`      | CPU load average 5min (percentage).        |