Feature/add frontend function pytorch eigh

ivy/functional/frontends/torch/linalg.py

@@ -370,3 +370,9 @@ 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
     )
+
+@with_supported_dtypes(
+    {"2.0.1 and below": ("float32", "float64", "complex32", "complex64", "complex128")}, "torch", 
+)
+def eigh(A, UPLO="L", *, out=None):
+    return ivy.eigh(A, UPLO=UPLO, out=out)

ivy_tests/test_ivy/test_frontends/test_torch/test_linalg.py

@@ -1289,3 +1289,48 @@ def test_torch_cholesky_ex(
         input=x,
         upper=upper,
     )
+
+
+@handle_frontend_test(
+    fn_tree="torch.linalg.eigh",
+    dtype_and_x=_get_dtype_and_matrix(dtype="valid", square=True, invertible=True),
+    UPLO=st.sampled_from(("L", "U")),
+)
+def test_torch_eigh(
+    *,
+    dtype_and_x,
+    UPLO,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+    backend_fw,
+):
+    dtype, x = dtype_and_x
+    x = np.array(x[0], dtype=dtype[0])
+    # make symmetric positive-definite beforehand
+    x = np.matmul(x.T, x) + np.identity(x.shape[0]) * 1e-3
+
+    ret, frontend_ret = helpers.test_frontend_function(
+        input_dtypes=dtype,
+        backend_to_test=backend_fw,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        test_values=False,
+        a=x,
+        UPLO=UPLO,
+    )
+    ret = [ivy.to_numpy(x) for x in ret]
+    frontend_ret = [np.asarray(x) for x in frontend_ret]
+
+    L, Q = ret
+    frontend_L, frontend_Q = frontend_ret
+
+    assert_all_close(
+        ret_np=Q @ np.diag(L) @ Q.T,
+        ret_from_gt_np=frontend_Q @ np.diag(frontend_L) @ frontend_Q.T,
+        atol=1e-02,
+
+    )