diff --git a/python/tvm/relay/frontend/pytorch.py b/python/tvm/relay/frontend/pytorch.py
index 48104e570..fb0352567 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -4620,6 +4620,47 @@ def scaled_dot_product_attention(self, inputs, input_types):
             attn_weight = _op.reshape(attn_weight, newshape=[-4, batch_size, -1, -2])
 
         return attn_weight
+    
+     
+    def nan_to_num(self, inputs, input_types):
+        
+        data = inputs[0] 
+        nan_value = inputs[1]  
+        posinf = inputs[2]  
+        neginf = inputs[3]  
+
+        dtype = input_types[0]
+
+        assert dtype == "float32", f"Expected dtype to be float32, but got {dtype}. Support for {dtype} is not added yet."
+
+        dtype_max = np.finfo(dtype).max
+        dtype_min = np.finfo(dtype).min
+
+        nan_tensor = tvm.relay.const(nan_value if nan_value is not None else 0.0, dtype)
+        posinf_tensor = tvm.relay.const(posinf if posinf is not None else dtype_max, dtype)
+        neginf_tensor = tvm.relay.const(neginf if neginf is not None else dtype_min, dtype)
+
+        result = tvm.relay.where(tvm.relay.isnan(data), nan_tensor, data)
+        result = tvm.relay.where(tvm.relay.equal(data, tvm.relay.const(np.inf, dtype)), posinf_tensor, result)
+        result = tvm.relay.where(tvm.relay.equal(data, tvm.relay.const(-np.inf, dtype)), neginf_tensor, result)
+
+        return result
+    
+    def atan2(self, inputs, input_types):
+        
+        data_1 = inputs[1]  
+        data_2 = inputs[0] 
+
+        ratio = tvm.relay.divide(data_2, data_1)
+        atan_res = tvm.relay.atan(ratio)
+
+        pi = tvm.relay.const(np.pi, "float32")
+        zero = tvm.relay.const(0.0, "float32")
+
+        correction = tvm.relay.where(tvm.relay.less(data_1, zero), tvm.relay.where(tvm.relay.greater_equal(data_2, zero), pi, -pi), zero)
+
+        result = tvm.relay.add(atan_res, correction)
+        return result
 
     # Operator mappings
     def create_convert_map(self):
@@ -4920,6 +4961,8 @@ def create_convert_map(self):
             "aten::linalg_vector_norm": self.linalg_vector_norm,
             "aten::scaled_dot_product_attention": self.scaled_dot_product_attention,
             "aten::lift_fresh": self.identity,
+            "aten::nan_to_num":self.nan_to_num,
+            "aten::atan2":self.atan2,
         }
 
     def update_convert_map(self, custom_map):