\begin{tabular}{ll}
\toprule
 & HCF (B) \\
\midrule
Two-Stage Training & True \\
First Marginal Bijector & BernsteinPolynomial \\
Invert First Marginal Bijector & True \\
Marginal Bijector Domain Min & 0 \\
Marginal Bijector Domain Max & 1 \\
Marginal Bijector Extrapolation & False \\
First Marginal Bijector Parameter Constraint Function & mctm.activations.get\_thetas\_constrain\_fn \\
Allow Flexible Bounds for First Marginal Bijector & False \\
Bounds constraints for First Marginal Bijector & linear \\
First Marginal Bijector Codomain Max & 5 \\
First Marginal Bijector Codomain Min & -5 \\
First Marginal Bijector Parameter Shape 0 & 2 \\
First Marginal Bijector Parameter Shape 1 & 300 \\
First Joint Bijector & RealNVP \\
Parameter Shape 0 for Joint Bijector & 1 \\
Marginal Epochs & 1000 \\
Marginal Batch Size & 1024 \\
Marginal Learning Rate & 0.050 \\
Marginal Reduce LR on Plateau & False \\
Marginal Early Stopping & 50 \\
Marginal Monitor & val\_loss \\
Marginal Verbosity & True \\
Marginal Validation Split & 0.250 \\
Joint Epochs & 400 \\
Joint Batch Size & 512 \\
Joint Learning Rate Decay Steps & epochs \\
Joint Initial Learning Rate & 0.001 \\
Joint Learning Rate Scheduler & cosine\_decay \\
Joint Early Stopping & 50 \\
Joint Validation Split & 0.250 \\
Masked Dimensions & 1 \\
First Nested Bijector & BernsteinPolynomial \\
Invert Nested Bijector & True \\
Nested Bernstein Bijector Domain Min & -5 \\
Nested Bernstein Bijector Domain Max & 5 \\
Nested Bernstein Bijector Extrapolation & False \\
Nested Bijector Parameter Constraint Function & mctm.activations.get\_thetas\_constrain\_fn \\
Bounds constraints of Nested Bernstein Bijector & linear \\
Nested Bernstein Bijector Codomain Max & 5 \\
Nested Bernstein Bijector Codomain Min & -5 \\
Activation Function for Joint Bijector & relu \\
Batch Normalization for Joint Bijector & False \\
Dropout Rate for Joint Bijector & False \\
First Hidden Units for Joint Bijector & 512 \\
Input Shape 0 for Joint Bijector & 1 \\
\bottomrule
\end{tabular}
