left= Normal(mu = 0, sigma = 1)
right= Normal(mu = 0, sigma = 1)
transformation= IdentityEvaluation(dimension=1)
inverseTransformation= IdentityEvaluation(dimension=1)
----------------------------------------------------------------------------------------------------
left= ComposedDistribution(Normal(mu = 0, sigma = 1), Gumbel(alpha = 1, beta = 0), IndependentCopula(dimension = 2))
right= ComposedDistribution(Triangular(a = -1, m = 0, b = 1), Triangular(a = -1, m = 0, b = 1), IndependentCopula(dimension = 2))
transformation= | y0 = Normal(mu = 0, sigma = 1) -> y0 : Triangular(a = -1, m = 0, b = 1)
| y1 = Gumbel(alpha = 1, beta = 0) -> y1 : Triangular(a = -1, m = 0, b = 1)

inverseTransformation= | y0 = Triangular(a = -1, m = 0, b = 1) -> y0 : Normal(mu = 0, sigma = 1)
| y1 = Triangular(a = -1, m = 0, b = 1) -> y1 : Gumbel(alpha = 1, beta = 0)

----------------------------------------------------------------------------------------------------
left= ComposedDistribution(Normal(mu = 0, sigma = 1), Gumbel(alpha = 1, beta = 0), IndependentCopula(dimension = 2))
right= ComposedDistribution(Triangular(a = -1, m = 0, b = 1), Triangular(a = -1, m = 0, b = 1), GumbelCopula(theta = 2))
transformation= ((| y0 = Uniform(a = 0, b = 1) -> y0 : Triangular(a = -1, m = 0, b = 1)
| y1 = Uniform(a = 0, b = 1) -> y1 : Triangular(a = -1, m = 0, b = 1)
)o(InverseRosenblattEvaluation(Normal(2)->GumbelCopula(theta = 2))))o(| y0 = [x0]->[x0]
| y1 = Gumbel(alpha = 1, beta = 0) -> y1 : Normal(mu = 0, sigma = 1)
)
inverseTransformation= (| y0 = [x0]->[x0]
| y1 = Normal(mu = 0, sigma = 1) -> y1 : Gumbel(alpha = 1, beta = 0)
)o((RosenblattEvaluation(GumbelCopula(theta = 2)->Normal(2))o(| y0 = Triangular(a = -1, m = 0, b = 1) -> y0 : Uniform(a = 0, b = 1)
| y1 = Triangular(a = -1, m = 0, b = 1) -> y1 : Uniform(a = 0, b = 1)
))
----------------------------------------------------------------------------------------------------
