fix: Issue with unsupported dtypes (#27934)

1. fixed an issue with _nested_get that was failing the unsupported dtypes function when testing with a frontend tensor method that uses the .to call as we can't infer the type of the instance where .to is being called in arbitrary code.
2. also updated the log1p frontend method and the relu frontend method implementations for the same.

ivy/functional/frontends/torch/tensor.py

@@ -5,7 +5,6 @@
 # local
 import ivy
 import ivy.functional.frontends.torch as torch_frontend
-import ivy.functional.frontends.torch.nn.functional as torch_frontend_nn
 from ivy.functional.frontends.numpy.creation_routines.from_existing_data import (
     array as np_frontend_array,
 )
@@ -390,7 +389,7 @@ def log2(self):
 
     @with_unsupported_dtypes({"2.1.2 and below": ("float16", "bfloat16")}, "torch")
     def relu(self):
-        return torch_frontend_nn.relu(self)
+        return torch_frontend.nn.functional.relu(self)
 
     @numpy_to_torch_style_args
     @with_unsupported_dtypes({"2.1.2 and below": ("complex",)}, "torch")
@@ -1714,12 +1713,14 @@ def stride(self, dim=None):
     )
     def log1p(self):
         promoted_type = ivy.promote_types(self.dtype, "float32")
-        return torch_frontend.log1p(self).to(promoted_type)
+        res = torch_frontend.log1p(self)
+        return res.to(promoted_type)
 
     @with_supported_dtypes({"2.1.2 and below": ("float32", "float64")}, "torch")
     def log1p_(self):
         promoted_type = ivy.promote_types(self.dtype, "float32")
-        self.ivy_array = torch_frontend.log1p(self).to(promoted_type).ivy_array
+        res = torch_frontend.log1p(self)
+        self.ivy_array = res.to(promoted_type).ivy_array
         return self
 
     def baddbmm(self, batch1, batch2, *, beta=1, alpha=1):

ivy/functional/ivy/data_type.py

@@ -188,16 +188,15 @@ def _nested_get(f, base_set, merge_fn, get_fn, wrapper=set):
             }
             for key in fl:
                 if "frontend" in key:
-                    fn_tree = key.split(".")
-                    root, tree, fn_name = fn_tree[0], fn_tree[1:-1], fn_tree[-1]
-                    root_module = importlib.import_module(
-                        ".".join([frontends[root], *tree])
-                    )
-                    frontend_fn = getattr(root_module, fn_name)
-                    frontend_fl = _get_function_list(frontend_fn)
+                    frontend_fn = fl[key]
+                    for frontend in frontends:
+                        if frontend in key:
+                            key = key.replace(frontend, frontends[frontend])
+                    frontend_module = ".".join(key.split(".")[:-1])
+                    frontend_fl = {key: frontend_fn}
                     res += list(
                         _get_functions_from_string(
-                            frontend_fl, __import__(frontend_fn.__module__)
+                            frontend_fl, importlib.import_module(frontend_module)
                         )
                     )
         to_visit.extend(set(res))