base model

+""" base model"""
+import logging
+
+import numpy as np
+import torch.nn as nn
+
+logger = logging.getLogger()
+
+
+class Base(nn.Module):
+    """ Base model with some util functions"""
+
+    def stats(self, print_model=True):
+        # print network model and information about parameters
+        logger.info("Model info:::")
+        if print_model:
+            logger.info(self)
+        count = 0
+        for i in self.parameters():
+            count += np.prod(i.shape)
+        logger.info(f"Total parameters : {count}")
+
+    def to(self, *args, **kwargs):
+        if kwargs.get("device"):
+            self.device = kwargs.get("device")
+        if len(args) > 0:
+            self.device = args[0]
+        return super().to(*args, **kwargs)
+
+    def forward(self, x):
+        raise NotImplementedError()