diff --git a/ivy/functional/frontends/torch/__init__.py b/ivy/functional/frontends/torch/__init__.py
index 721fe7bd6214..eb05e3c7ce16 100644
--- a/ivy/functional/frontends/torch/__init__.py
+++ b/ivy/functional/frontends/torch/__init__.py
@@ -309,6 +309,7 @@ def promote_types_of_torch_inputs(
 from . import utilities
 from .utilities import *
 from . import linalg
+from .linalg import lu
 from . import func
 from .func import *
 
diff --git a/ivy/functional/frontends/torch/linalg.py b/ivy/functional/frontends/torch/linalg.py
index ed3ca7978e59..34bbedd006e5 100644
--- a/ivy/functional/frontends/torch/linalg.py
+++ b/ivy/functional/frontends/torch/linalg.py
@@ -138,22 +138,34 @@ def inv_ex(A, *, check_errors=False, out=None):
     {"2.2 and below": ("float32", "float64", "complex32", "complex64")}, "torch"
 )
 def lu_factor(A, *, pivot=True, out=None):
-    return ivy.lu_factor(A, pivot=pivot, out=out)
+    LU, pivots = ivy.lu_factor(A, pivot=pivot, out=out)
+    lu_factor_tuple = namedtuple("linalg_lu_factor", ["LU", "pivots"])
+    return lu_factor_tuple(
+        LU=LU,
+        pivots=pivots,
+    )
 
 
 @to_ivy_arrays_and_back
 def lu_factor_ex(A, *, pivot=True, check_errors=False, out=None):
     try:
-        LU = ivy.lu_factor(A, pivot=pivot, out=out)
+        # Perform LU factorization and get the result as a named tuple
+        LU, pivots = ivy.lu_factor(A, pivot=pivot, out=out)
         info = ivy.zeros(A.shape[:-2], dtype=ivy.int32)
-        return LU, info
     except RuntimeError as e:
         if check_errors:
             raise RuntimeError(e) from e
         else:
-            matrix = A * math.nan
+            # If there's an error and check_errors is False, handle the error
+            LU = ivy.full_like(A, math.nan)
+            pivots = ivy.full_like(A.shape[:-1], math.nan)
             info = ivy.ones(A.shape[:-2], dtype=ivy.int32)
-            return matrix, info
+
+    # Create a named tuple for the final result
+    lu_factor_ex_tuple = namedtuple("linalg_lu_factor_ex", ["LU", "pivots", "info"])
+
+    # Return the results
+    return lu_factor_ex_tuple(LU=LU, pivots=pivots, info=info)
 
 
 def lu_solve(LU, pivots, B, *, left=True, adjoint=False, out=None):
@@ -436,3 +448,6 @@ def vector_norm(input, ord=2, dim=None, keepdim=False, *, dtype=None, out=None):
     return ivy.vector_norm(
         input, axis=dim, keepdims=keepdim, ord=ord, out=out, dtype=dtype
     )
+
+
+lu = lu_factor