feat : added nansum function for Pytorch frontend

ivy/functional/frontends/torch/reduction_ops.py

@@ -190,6 +190,11 @@ def moveaxis(input, source, destination):
 def nanmean(input, dim=None, keepdim=False, *, dtype=None, out=None):
     return ivy.nanmean(input, axis=dim, keepdims=keepdim, dtype=dtype, out=out)
 
+@numpy_to_torch_style_args
+@to_ivy_arrays_and_back
+def nansum(input, dim=None, keepdim=False, dtype=None, out=None):
+    return ivy.nansum(input, axis=dim, keepdims=keepdim, dtype=dtype, out=out)
+
 
 @to_ivy_arrays_and_back
 @with_supported_dtypes(

ivy_tests/test_ivy/test_frontends/test_torch/test_reduction_ops.py

@@ -671,6 +671,37 @@ def test_torch_nanmean(
         keepdim=keepdims,
     )
 
+@handle_frontend_test(
+    fn_tree="torch.nansum",
+    dtype_and_x=_statistical_dtype_values(
+        function="nansum",
+        min_value=-1e04,
+        max_value=1e04,
+    ),
+    keepdims=st.booleans(),
+)
+def test_torch_nansum(
+    *,
+    dtype_and_x,
+    keepdims,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+    backend_fw,
+):
+    input_dtype, x, axis = dtype_and_x
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        backend_to_test=backend_fw,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        input=x[0],
+        dim=axis,
+        keepdim=keepdims,
+    )
 
 # norm
 @handle_frontend_test(