feat: add log_softmax torch frontend Tenor method and the test

ivy/functional/frontends/torch/tensor.py

@@ -1161,6 +1161,10 @@ def fmax(self, other):
     def fmin(self, other):
         return torch_frontend.fmin(self, other)
 
+    @with_unsupported_dtypes({"2.2 and below": ("float16",)}, "torch")
+    def log_softmax(self, dim=None, _stack_level=3, dtype=None):
+        return torch_frontend.nn.functional.log_softmax(self, dim=dim, dtype=dtype)
+
     def msort(self):
         return torch_frontend.msort(self)
 

ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py

@@ -8824,6 +8824,51 @@ def test_torch_log_(
     )
 
 
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="torch.tensor",
+    method_name="log_softmax",
+    dtype_x_and_axis=helpers.dtype_values_axis(
+        available_dtypes=helpers.get_dtypes("float"),
+        min_num_dims=1,
+        max_axes_size=1,
+        force_int_axis=True,
+        valid_axis=True,
+    ),
+    dtypes=helpers.get_dtypes("float", none=False, full=False),
+)
+def test_torch_log_softmax(
+    *,
+    dtype_x_and_axis,
+    dtypes,
+    on_device,
+    frontend,
+    backend_fw,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+):
+    input_dtype, x, axis = dtype_x_and_axis
+
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        backend_to_test=backend_fw,
+        init_all_as_kwargs_np={
+            "data": x[0],
+        },
+        method_input_dtypes=input_dtype,
+        method_all_as_kwargs_np={
+            "dim": axis,
+            "dtype": dtypes[0],
+        },
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        frontend=frontend,
+        on_device=on_device,
+    )
+
+
 # logaddexp
 @handle_frontend_method(
     class_tree=CLASS_TREE,