Added lu_solve to PyTorch Frontend

ivy/functional/frontends/torch/linalg.py

@@ -135,6 +135,16 @@ def lu_factor(A, *, pivot=True, out=None):
     return ivy.lu_factor(A, pivot=pivot, out=out)
 
 
+@to_ivy_arrays_and_back
+@with_supported_dtypes(
+    {"2.0.1 and below": ("float32", "float64", "complex32", "complex64")}, "torch"
+)
+def lu_solve(LU, pivots, B, left=True, adjoint=False, out=None):
+    # TODO: Implement left
+    # TODO: Implement adjoint
+    return ivy.solve(LU, ivy.dot(pivots, B), out=out)
+
+
 @to_ivy_arrays_and_back
 @with_supported_dtypes(
     {"2.0.1 and below": ("float32", "float64", "complex32", "complex64")}, "torch"

ivy_tests/test_ivy/test_frontends/test_torch/test_linalg.py

@@ -1289,3 +1289,53 @@ def test_torch_cholesky_ex(
         input=x,
         upper=upper,
     )
+
+
+@handle_frontend_test(
+    fn_tree="torch.linalg.lu_solve",
+    dtype_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+        shape=(3, 3),
+        num_arrays=2,
+        shared_dtype=True,
+        min_value=-1e04,
+        max_value=1e04,
+    ),
+    dtype_y=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+        shape=(3, 1),
+        num_arrays=1,
+        shared_dtype=True,
+        min_value=-1e04,
+        max_value=1e04,
+    ),
+    left=st.booleans(),
+    adjoint=st.booleans(),
+)
+def test_torch_lu_solve(
+    dtype_x,
+    dtype_y,
+    left,
+    adjoint,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+    backend_fw,
+):
+    input_dtype, x = dtype_x
+    input_dtype, y = dtype_y
+    test_flags.num_positional_args = 2
+    helpers.test_frontend_function(
+        input_dtypes=[input_dtype[0], input_dtype[0]],
+        backend_to_test=backend_fw,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        A=x[0],
+        pivots=x[1],
+        B=y[0],
+        left=left,
+        adjoint=adjoint,
+    )