PoissonLogNormalQuadratureCompound parameterization doesn't match LogNormal parameterization

What are loc and scale in this distribution.

Consider this:

import tensorflow_probability as tfp
import numpy as np
tfd = tfp.distributions

loc = 2.4
scale = 0.8

d1 = tfd.JointDistributionSequential([
    tfd.LogNormal(loc, scale),
    lambda z: tfd.Poisson(z)
])

d2 = tfd.PoissonLogNormalQuadratureCompound(loc, scale)

N = 100000
sample_1 = d1.sample(N)[1].numpy()
sample_2 = d2.sample(N).numpy()
sample_3 = np.random.poisson(np.random.lognormal(loc, scale , size=N))

print(sample_1.mean(), sample_1.var())
print(sample_2.mean(), sample_2.var())
print(sample_3.mean(), sample_3.var())

15.20101 217.6014
28675494000.0 5.8343047e+21
15.22877 224.11053428710002

Is it expected that the parameterization is different to LogNormal?

How can I obtain the "standard parameterization"? . I have tried to do log(scale) which gives closer but not correct answer.

Start by running the provided Python snippet and inspect the implementation and documentation for PoissonLogNormalQuadratureCompound alongside LogNormal. Compare how loc and scale are interpreted and identify whether the discrepancy is in parameter handling or the documented behavior. Done means the parameterization is explained clearly and the reported mismatch is corrected or covered by an appropriate regression check.