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.

	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

//      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>

			"the runtime/debug.SetMemoryLimit function.",
		Kind: KindUint64,
	},
	{
		Name: "/gc/heap/allocs-by-size:bytes",
		Description: "Distribution of heap allocations by approximate size. " +
			"Bucket counts increase monotonically. " +
			"Note that this does not include tiny objects as defined by " +
			"/gc/heap/tiny/allocs:objects, only tiny blocks.",
		Kind:       KindFloat64Histogram,

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

      }
    }

    // Optimization - use signed division if dividend < 2^63
    if (dividend >= 0) {
      return dividend / divisor;
    }

    /*
     * Otherwise, approximate the quotient, check, and correct if necessary. Our approximation is
     * guaranteed to be either exact or one less than the correct value. This follows from fact that