public interface KaTypeProviderMixIn : KaSessionMixIn {
    public val builtinTypes: KaBuiltinTypes
        get() = withValidityAssertion { analysisSession.typeProvider.builtinTypes }

    /**
     * Approximates [KaType] with a supertype which can be rendered in a source code
     *
     * Return `null` if the type do not need approximation and can be rendered as is

Follow these steps to run the Spark Pi example:

- Login as user **‘spark’**.
- When the job runs, the library can now use **MinIO** during intermediate processing.

     IN PART, THIS LIMITATION MAY NOT APPLY TO YOU.

  c. The disclaimer of warranties and limitation of liability provided
     above shall be interpreted in a manner that, to the extent
     possible, most closely approximates an absolute disclaimer and
     waiver of all liability.


Section 6 -- Term and Termination.

  a. This Public License applies for the term of the Copyright and

	priv.E = 65537

	if nprimes < 2 {
		return nil, errors.New("crypto/rsa: GenerateMultiPrimeKey: nprimes must be >= 2")
	}

	if bits < 64 {
		primeLimit := float64(uint64(1) << uint(bits/nprimes))
		// pi approximates the number of primes less than primeLimit
		pi := primeLimit / (math.Log(primeLimit) - 1)
		// Generated primes start with 11 (in binary) so we can only
		// use a quarter of them.
		pi /= 4

  If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.

//      1. For x<2.
//         Since
//              y0(x) = 2/pi*(j0(x)*(ln(x/2)+Euler) + x**2/4 - ...)
//         therefore y0(x)-2/pi*j0(x)*ln(x) is an even function.
//         We use the following function to approximate y0,
//              y0(x) = U(z)/V(z) + (2/pi)*(j0(x)*ln(x)), z= x**2
//         where
//              U(z) = u00 + u01*z + ... + u06*z**6
//              V(z) = 1  + v01*z + ... + v04*z**4

//      2. For x<2.
//         Since
//              y1(x) = 2/pi*(j1(x)*(ln(x/2)+Euler)-1/x-x/2+5/64*x**3-...)
//         therefore y1(x)-2/pi*j1(x)*ln(x)-1/x is an odd function.
//         We use the following function to approximate y1,
//              y1(x) = x*U(z)/V(z) + (2/pi)*(j1(x)*ln(x)-1/x), z= x**2
//         where for x in [0,2] (abs err less than 2**-65.89)
//              U(z) = U0[0] + U0[1]*z + ... + U0[4]*z**4

//         Remark: here we use the taylor series expansion at x=1.
//              erf(1+s) = erf(1) + s*Poly(s)
//                       = 0.845.. + P1(s)/Q1(s)
//         That is, we use rational approximation to approximate
//                      erf(1+s) - (c = (single)0.84506291151)
//         Note that |P1/Q1|< 0.078 for x in [0.84375,1.25]
//         where
//              P1(s) = degree 6 poly in s

  %1 = "tf.Identity"(%0) {device = ""} : (tensor<5x10xf32>) -> tensor<*xf32>
  %2 = "tf.Identity"(%1) {device = ""} : (tensor<*xf32>) -> tensor<*xf32>
  return %2 : tensor<*xf32>
}

// CHECK-LABEL: gelu_aten
// CHECK: %0 = "tfl.gelu"(%arg0) <{approximate = false}> : (tensor<5x10xf32>) -> tensor<5x10xf32>

        }
        val needLocalTypeApproximation = needLocalTypeApproximation(visibilityForApproximation, isInlineFunction, session, useSitePosition)
        // TODO: can we approximate local types in type arguments *selectively* ?
        currentType = PublicTypeApproximator.approximateTypeToPublicDenotable(currentType, session, needLocalTypeApproximation)
            ?: currentType