feature: add baddbmm_ to torch (ivy-llc#21228)

Co-authored-by: paulaehab<eng.paulaehab@gmail.com>

ivy/functional/frontends/torch/tensor.py

@@ -1561,6 +1561,12 @@ def baddbmm(self, batch1, batch2, *, beta=1, alpha=1):
             self, batch1=batch1, batch2=batch2, beta=beta, alpha=alpha
         )
 
+    def baddbmm_(self, batch1, batch2, *, beta=1, alpha=1):
+        self.ivy_array = torch_frontend.baddbmm(
+            self, batch1=batch1, batch2=batch2, beta=beta, alpha=alpha
+        ).ivy_array
+        return self
+
     def bmm(self, mat2):
         return torch_frontend.bmm(self, mat2=mat2)
 

ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py

@@ -10578,6 +10578,55 @@ def test_torch_baddbmm(
     )
 
 
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="torch.tensor",
+    method_name="baddbmm_",
+    dtype_and_matrices=_get_dtype_and_3dbatch_matrices(with_input=True, input_3d=True),
+    beta=st.floats(
+        min_value=-5,
+        max_value=5,
+        allow_nan=False,
+        allow_subnormal=False,
+        allow_infinity=False,
+    ),
+    alpha=st.floats(
+        min_value=-5,
+        max_value=5,
+        allow_nan=False,
+        allow_subnormal=False,
+        allow_infinity=False,
+    ),
+)
+def test_torch_baddbmm_(
+    dtype_and_matrices,
+    beta,
+    alpha,
+    frontend,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    on_device,
+):
+    input_dtype, x, batch1, batch2 = dtype_and_matrices
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        init_all_as_kwargs_np={"data": x[0]},
+        method_input_dtypes=input_dtype,
+        method_all_as_kwargs_np={
+            "batch1": batch1,
+            "batch2": batch2,
+            "beta": beta,
+            "alpha": alpha,
+        },
+        frontend=frontend,
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        on_device=on_device,
+    )
+
+
 @handle_frontend_method(
     class_tree=CLASS_TREE,
     init_tree="torch.tensor",