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diff --git a/code/sunlab/common/distribution/s_gaussian_distribution.py b/code/sunlab/common/distribution/s_gaussian_distribution.py
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+++ b/code/sunlab/common/distribution/s_gaussian_distribution.py
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+from .adversarial_distribution import *
+
+
+class SGaussianDistribution(AdversarialDistribution):
+    """# S Gaussian Distribution"""
+
+    def __init__(self, N, scale=0):
+        """# S Gaussian Distribution Initialization
+
+        Initializes the name and dimensions"""
+        super().__init__(N)
+        assert self.dims == 2, "This Distribution only Supports 2-Dimensions"
+        self.full_name = "2-Dimensional S-Gaussian Distribution"
+        self.name = "SG"
+        self.scale = scale
+
+    def __call__(self, *args):
+        """# Magic method when calling the distribution
+
+        This method is going to be called when you use xgauss(case_count)"""
+        import numpy as np
+
+        assert len(args) == 1, "Only 1 argument supported"
+        N = args[0]
+        sample_base = np.zeros((4 * N, 2))
+        scale = self.scale
+        sample_base[0 * N : (0 + 1) * N, :] = np.random.multivariate_normal(
+            mean=[1, 1], cov=[[1, scale], [scale, 1]], size=[N]
+        )
+        sample_base[1 * N : (1 + 1) * N, :] = np.random.multivariate_normal(
+            mean=[-1, -1], cov=[[1, scale], [scale, 1]], size=[N]
+        )
+        sample_base[2 * N : (2 + 1) * N, :] = np.random.multivariate_normal(
+            mean=[-1, 1], cov=[[1, -scale], [-scale, 1]], size=[N]
+        )
+        sample_base[3 * N : (3 + 1) * N, :] = np.random.multivariate_normal(
+            mean=[1, -1], cov=[[1, -scale], [-scale, 1]], size=[N]
+        )
+        np.random.shuffle(sample_base)
+        return sample_base[:N, :]