Added Gumbel RV

.gitignore

@@ -46,3 +46,4 @@ core
 
 aesara-venv/
 /notebooks/Sandbox*
+.vscode/

aesara/tensor/random/basic.py

+from typing import List, Optional, Union
+
 import numpy as np
 import scipy.stats as stats
 
@@ -108,6 +110,36 @@ class ParetoRV(RandomVariable):
 pareto = ParetoRV()
 
 
+class GumbelRV(RandomVariable):
+    name = "gumbel"
+    ndim_supp = 0
+    ndims_params = [0, 0]
+    dtype = "floatX"
+    _print_name = ("Gumbel", "\\operatorname{Gumbel}")
+
+    def __call__(
+        self,
+        loc: Union[np.ndarray, float],
+        scale: Union[np.ndarray, float] = 1.0,
+        size: Optional[Union[List[int], int]] = None,
+        **kwargs
+    ) -> RandomVariable:
+        return super().__call__(loc, scale, size=size, **kwargs)
+
+    @classmethod
+    def rng_fn(
+        cls,
+        rng: np.random.RandomState,
+        loc: Union[np.ndarray, float],
+        scale: Union[np.ndarray, float],
+        size: Optional[Union[List[int], int]],
+    ) -> np.ndarray:
+        return stats.gumbel_r.rvs(loc=loc, scale=scale, size=size, random_state=rng)
+
+
+gumbel = GumbelRV()
+
+
 class ExponentialRV(RandomVariable):
     name = "exponential"
     ndim_supp = 0

tests/tensor/random/test_basic.py

@@ -23,6 +23,7 @@ from aesara.tensor.random.basic import (
     dirichlet,
     exponential,
     gamma,
+    gumbel,
     halfcauchy,
     halfnormal,
     invgamma,
@@ -218,6 +219,14 @@ def test_gamma_samples():
     rv_numpy_tester(gamma, test_a, test_b, size=[2, 3], test_fn=stats.gamma.rvs)
 
 
+def test_gumbel_samples():
+    test_mu = np.array(0.0, dtype=config.floatX)
+    test_beta = np.array(1.0, dtype=config.floatX)
+
+    rv_numpy_tester(gumbel, test_mu, test_beta, test_fn=stats.gumbel_r.rvs)
+    rv_numpy_tester(gumbel, test_mu, test_beta, size=[2, 3], test_fn=stats.gumbel_r.rvs)
+
+
 def test_exponential_samples():
 
     rv_numpy_tester(exponential)