* {@linkplain #intersection intersection} (as a single, possibly-empty range) if and only if this
   * method returns {@code true}.
   *
   * <p>The connectedness relation is both reflexive and symmetric, but does not form an {@linkplain
   * Equivalence equivalence relation} as it is not transitive.
   *
   * <p>Note that certain discrete ranges are not considered connected, even though there are no

	}

	// Add final 0 utilization event to any remaining series. This
	// is important to mark the end of the trace. The exact value
	// shouldn't matter since no window should extend beyond this,
	// but using 0 is symmetric with the start of the trace.
	mu := MutatorUtil{int64(lastEv.Time()), 0}
	for i := range ps {
		out[ps[i].series] = addUtil(out[ps[i].series], mu)
	}
	return out
}

  func.func @mirror_pad(%arg0: tensor<2x3xcomplex<f64>>) -> tensor<4x7xcomplex<f64>> {
    %0 = mhlo.constant dense<[[1, 1], [2, 2]]> : tensor<2x2xi32>
    // CHECK-NOT: tf.MirrorPad
    %1 = "tf.MirrorPad"(%arg0, %0) {mode = "SYMMETRIC"} : (tensor<2x3xcomplex<f64>>, tensor<2x2xi32>) -> tensor<4x7xcomplex<f64>>
    func.return %1 : tensor<4x7xcomplex<f64>>
  }

  // CHECK-LABEL: bucketize

			u.tracef("depth %d >= %d", u.depth, unificationDepthLimit)
		}
		if panicAtUnificationDepthLimit {
			panic("unification reached recursion depth limit")
		}
		return false
	}

	// Unification is symmetric, so we can swap the operands.
	// Ensure that if we have at least one
	// - defined type, make sure one is in y
	// - type parameter recorded with u, make sure one is in x

			u.tracef("depth %d >= %d", u.depth, unificationDepthLimit)
		}
		if panicAtUnificationDepthLimit {
			panic("unification reached recursion depth limit")
		}
		return false
	}

	// Unification is symmetric, so we can swap the operands.
	// Ensure that if we have at least one
	// - defined type, make sure one is in y
	// - type parameter recorded with u, make sure one is in x

        `TFLiteConverter.from_saved_model`.
    *   Added DEPTH_TO_SPACE support in Post training quantization.
    *   Added dynamic range quantization support for the BatchMatMul op.
        *   Both symmetric and asymmetric quantized input tensor are supported.
    *   Add `RFFT2D` as builtin op. (`RFFT2D` also supports `RFFTD`.) Currently
        only supports float32 input.
    *   Add 5D support to `SLICE` op.

    //
    // Furthermore, pi * abs(frac(x)) loses precision when abs(frac(x)) is close
    // to 1.  To remedy this, we can use the fact that sin(pi * x) in the domain
    // [0, 1] is symmetric across the line Y=0.5.
    Value abs_input = rewriter.create<AbsOp>(loc, input);
    Value abs_input_floor = rewriter.create<FloorOp>(loc, abs_input);
    Value abs_frac_input =

	// should be the same as rounding by SetInt64 after setting the
	// precision)
	g := new(Float).SetMode(mode).SetPrec(prec).SetInt64(x)
	if !alike(g, f) {
		t.Errorf("round %s (%d bits, %s) not symmetric: got %s and %s; want %s",
			toBinary(x), prec, mode,
			toBinary(g.int64()),
			toBinary(r1),
			toBinary(r),
		)
		return
	}

	// h and f should be the same

// but using the sign of z for rounding the result.
// x and y must have a non-empty mantissa and valid exponent.
func (z *Float) usub(x, y *Float) {
	// This code is symmetric to uadd.
	// We have not factored the common code out because
	// eventually uadd (and usub) should be optimized
	// by special-casing, and the code will diverge.

	if debugFloat {

^bb0(%arg0: tensor<2x1x3xf32>, %arg1: tensor<3x2xi32>):
  %0 = "tf.MirrorPad"(%arg0, %arg1) { mode = "SYMMETRIC" }: (tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<? x f32>
  func.return %0#0 : tensor<? x f32>

  // CHECK-LABEL: mirror_pad
  // CHECK:  "tfl.mirror_pad"(%arg0, %arg1) <{mode = #tfl<mirror_pad_attr SYMMETRIC>}> : (tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<?xf32>
  // CHECK:  return
}