diff --git a/tumourkit/classification/models/gin.py b/tumourkit/classification/models/gin.py
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+
+"""
+How Powerful are Graph Neural Networks?
+
+References
+----------
+Paper: https://arxiv.org/abs/1810.00826
+
+Copyright (C) 2023  Jose Pérez Cano
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU Affero General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU Affero General Public License for more details.
+
+You should have received a copy of the GNU Affero General Public License
+along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Contact information: joseperez2000@hotmail.es
+"""
+import torch.nn as nn
+import torch.nn.functional as F
+from dgl.nn import GINConv
+from .norm import Norm
+
+class GIN(nn.Module):
+    def __init__(self, in_feats, h_feats, num_classes, num_layers, drop_rate, norm_type, enable_background=False):
+        super(GIN, self).__init__()
+        self.conv_layers = nn.ModuleList()
+        self.conv_layers.append(GINConv(nn.Linear(in_feats, h_feats), 'max', activation=F.elu))
+        self.conv_layers.append(nn.Dropout(drop_rate)) # Feature map dropout
+        self.conv_layers.append(Norm(norm_type=norm_type, hidden_dim=h_feats))
+        for l in range(1,num_layers):
+            self.conv_layers.append(GINConv(nn.Linear(h_feats, h_feats), 'max', activation=F.elu))
+            self.conv_layers.append(nn.Dropout(drop_rate))
+            self.conv_layers.append(Norm(norm_type=norm_type, hidden_dim=h_feats))
+        self.conv_layers.append(GINConv(nn.Linear(h_feats, num_classes), 'max',))
+
+        self.enable_background = enable_background
+        if enable_background:
+            self.bkgr_head = GINConv(nn.Linear(h_feats, 2), 'max',)
+
+
+    def forward(self, g, in_feat):
+        h = in_feat
+        for i, layer in enumerate(self.conv_layers):
+            if i == len(self.conv_layers) - 1:
+                if self.enable_background:  # Last layer
+                    h_bkgr = self.bkgr_head(g, h)
+                    h = layer(g, h)
+                    return h, h_bkgr 
+                h = layer(g, h)
+            else:
+                if i % 3 == 1:
+                    h = layer(h)  # Dropout
+                else:
+                    h = layer(g, h)  # Other layers
+        return h
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