return true
	case OpSignmask:
		return rewriteValueARM_OpSignmask(v)
	case OpSlicemask:
		return rewriteValueARM_OpSlicemask(v)
	case OpSqrt:
		v.Op = OpARMSQRTD
		return true
	case OpSqrt32:
		v.Op = OpARMSQRTF
		return true
	case OpStaticCall:
		v.Op = OpARMCALLstatic
		return true
	case OpStore:
		return rewriteValueARM_OpStore(v)
	case OpSub16:
		v.Op = OpARMSUB
		return true

		return true
	case OpSignExt8to64:
		v.Op = OpARM64MOVBreg
		return true
	case OpSlicemask:
		return rewriteValueARM64_OpSlicemask(v)
	case OpSqrt:
		v.Op = OpARM64FSQRTD
		return true
	case OpSqrt32:
		v.Op = OpARM64FSQRTS
		return true
	case OpStaticCall:
		v.Op = OpARM64CALLstatic
		return true
	case OpStore:
		return rewriteValueARM64_OpStore(v)
	case OpSub16:
		v.Op = OpARM64SUB

	case OpSpectreIndex:
		return rewriteValueAMD64_OpSpectreIndex(v)
	case OpSpectreSliceIndex:
		return rewriteValueAMD64_OpSpectreSliceIndex(v)
	case OpSqrt:
		v.Op = OpAMD64SQRTSD
		return true
	case OpSqrt32:
		v.Op = OpAMD64SQRTSS
		return true
	case OpStaticCall:
		v.Op = OpAMD64CALLstatic
		return true
	case OpStore:
		return rewriteValueAMD64_OpStore(v)
	case OpSub16:
		v.Op = OpAMD64SUBL

        1e-10))` Alternatively, you can override `convolution_op`: `python class
        StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs,
        kernel): mean, var = tf.nn.moments(kernel, axes=[0, 1, 2],
        keepdims=True) # Author code uses std + 1e-5 return
        super().convolution_op(inputs, (kernel - mean) / tf.sqrt(var + 1e-10))`

Sa=Math.PI,ka=2*Sa,Ea=Sa/2,Aa=1e-6,Ca=Aa*Aa,Na=Sa/180,La=180/Sa,Ta=Math.SQRT2,qa=2,za=4;Xo.interpolateZoom=function(n,t){function e(n){var t=n*y;if(m){var e=B(v),o=i/(qa*h)*(e*W(Ta*t+v)-$(v));return[r+o*s,u+o*l,i*e/B(Ta*t+v)]}return[r+n*s,u+n*l,i*Math.exp(Ta*t)]}var r=n[0],u=n[1],i=n[2],o=t[0],a=t[1],c=t[2],s=o-r,l=a-u,f=s*s+l*l,h=Math.sqrt(f),g=(c*c-i*i+za*f)/(2*i*qa*h),p=(c*c-i*i-za*f)/(2*c*qa*h),v=Math.log(Math.sqrt(g*g+1)-g),d=Math.log(Math.sqrt(p*p+1)-p),m=d-v,y=(m||Math.log(c/i))/Ta;return e.duration=1...