fix(random): break circular delegation with Shape.ToIntArray

After longindexing migration, Shape.dimensions is long[] instead of int[].
This caused circular delegation:

  params long[] → new Shape(size) → Shape overload → size.dimensions (long[])
  → matches params long[] again → infinite recursion

Fix: Shape overloads now use Shape.ToIntArray(size.dimensions) to delegate
to the int[] implementation, breaking the circular dependency.

Files fixed: beta, bernoulli, binomial, chisquare, exponential, f, gamma,
geometric, logseries, lognormal, multinomial, noncentral_f, poisson, randn,
standard_cauchy, standard_gamma, standard_t, triangular, uniform, wald, weibull

Author: Nucs

src/NumSharp.Core/RandomSampling/np.random.f.cs

@@ -36,7 +36,7 @@ public NDArray f(double dfnum, double dfden, Shape? size = null)
                 return NDArray.Scalar(FSample(dfnum, dfden));
             }
 
-            return f(dfnum, dfden, size.Value.dimensions);
+            return f(dfnum, dfden, Shape.ToIntArray(size.Value.dimensions));
         }
 
         /// <summary>
@@ -47,7 +47,7 @@ public NDArray f(double dfnum, double dfden, Shape? size = null)
         /// <param name="size">Output shape.</param>
         /// <returns>Drawn samples from the F distribution.</returns>
         /// <exception cref="ArgumentException">If dfnum or dfden is <= 0.</exception>
-        public NDArray f(double dfnum, double dfden, Shape size) => f(dfnum, dfden, size.dimensions);
+        public NDArray f(double dfnum, double dfden, Shape size) => f(dfnum, dfden, Shape.ToIntArray(size.dimensions));
 
         /// <summary>
         ///     Draw samples from an F distribution.

src/NumSharp.Core/RandomSampling/np.random.logseries.cs

@@ -33,7 +33,7 @@ public NDArray logseries(double p, Shape? size = null)
                 return NDArray.Scalar(SampleLogseries(p));
             }
 
-            return logseries(p, size.Value.dimensions);
+            return logseries(p, Shape.ToIntArray(size.Value.dimensions));
         }
 
         /// <summary>

src/NumSharp.Core/RandomSampling/np.random.standard_cauchy.cs

@@ -29,15 +29,15 @@ public NDArray standard_cauchy(Shape? size = null)
                 return NDArray.Scalar(StandardCauchySample());
             }
 
-            return standard_cauchy(size.Value.dimensions);
+            return standard_cauchy(Shape.ToIntArray(size.Value.dimensions));
         }
 
         /// <summary>
         ///     Draw samples from a standard Cauchy distribution with mode = 0.
         /// </summary>
         /// <param name="size">Output shape.</param>
         /// <returns>Drawn samples from the standard Cauchy distribution.</returns>
-        public NDArray standard_cauchy(Shape size) => standard_cauchy(size.dimensions);
+        public NDArray standard_cauchy(Shape size) => standard_cauchy(Shape.ToIntArray(size.dimensions));
 
         /// <summary>
         ///     Draw samples from a standard Cauchy distribution with mode = 0.

src/NumSharp.Core/RandomSampling/np.random.standard_gamma.cs

@@ -22,7 +22,7 @@ public partial class NumPyRandom
         ///     p(x) = x^(shape-1) * e^(-x) / Gamma(shape)
         /// </remarks>
         public NDArray standard_gamma(double shape, Shape size)
-            => standard_gamma(shape, size.dimensions);
+            => standard_gamma(shape, Shape.ToIntArray(size.dimensions));
 
         /// <summary>
         ///     Draw samples from a standard Gamma distribution (scale=1).

src/NumSharp.Core/RandomSampling/np.random.standard_t.cs

@@ -22,7 +22,7 @@ public partial class NumPyRandom
         ///     P(x, df) = Gamma((df+1)/2) / (sqrt(pi*df) * Gamma(df/2)) * (1 + x^2/df)^(-(df+1)/2)
         /// </remarks>
         public NDArray standard_t(double df, Shape size)
-            => standard_t(df, size.dimensions);
+            => standard_t(df, Shape.ToIntArray(size.dimensions));
 
         /// <summary>
         ///     Draw samples from a standard Student's t distribution with df degrees of freedom.

src/NumSharp.Core/RandomSampling/np.random.vonmises.cs

@@ -41,7 +41,7 @@ public NDArray vonmises(double mu, double kappa, Shape? size = null)
                 return NDArray.Scalar(SampleVonMises(mu, kappa));
             }
 
-            return vonmises(mu, kappa, size.Value.dimensions);
+            return vonmises(mu, kappa, Shape.ToIntArray(size.Value.dimensions));
         }
 
         /// <summary>

src/NumSharp.Core/RandomSampling/np.random.weibull.cs

@@ -36,7 +36,7 @@ public NDArray weibull(double a, Shape? size = null)
                 return NDArray.Scalar(WeibullSample(a));
             }
 
-            return weibull(a, size.Value.dimensions);
+            return weibull(a, Shape.ToIntArray(size.Value.dimensions));
         }
 
         /// <summary>
@@ -46,7 +46,7 @@ public NDArray weibull(double a, Shape? size = null)
         /// <param name="size">Output shape.</param>
         /// <returns>Drawn samples from the Weibull distribution.</returns>
         /// <exception cref="ArgumentException">If a is negative.</exception>
-        public NDArray weibull(double a, Shape size) => weibull(a, size.dimensions);
+        public NDArray weibull(double a, Shape size) => weibull(a, Shape.ToIntArray(size.dimensions));
 
         /// <summary>
         ///     Draw samples from a Weibull distribution.