diff --git a/src/transformers/utils/__init__.py b/src/transformers/utils/__init__.py
index c5b80c617c85de..08f9fc82643ac8 100644
--- a/src/transformers/utils/__init__.py
+++ b/src/transformers/utils/__init__.py
@@ -186,7 +186,9 @@
     is_training_run_on_sagemaker,
     is_vision_available,
     requires_backends,
+    tf_required,
     torch_only_method,
+    torch_required,
 )
 from .peft_utils import (
     ADAPTER_CONFIG_NAME,
diff --git a/src/transformers/utils/import_utils.py b/src/transformers/utils/import_utils.py
index 837fb24af42a61..e2920b291a3462 100644
--- a/src/transformers/utils/import_utils.py
+++ b/src/transformers/utils/import_utils.py
@@ -24,7 +24,7 @@
 import sys
 import warnings
 from collections import OrderedDict
-from functools import lru_cache
+from functools import lru_cache, wraps
 from itertools import chain
 from types import ModuleType
 from typing import Any, Tuple, Union
@@ -1222,6 +1222,40 @@ def __getattribute__(cls, key):
         requires_backends(cls, cls._backends)
 
 
+def torch_required(func):
+    warnings.warn(
+        "The method `torch_required` is deprecated and will be removed in v4.36. Use `requires_backends` instead.",
+        FutureWarning,
+    )
+
+    # Chose a different decorator name than in tests so it's clear they are not the same.
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        if is_torch_available():
+            return func(*args, **kwargs)
+        else:
+            raise ImportError(f"Method `{func.__name__}` requires PyTorch.")
+
+    return wrapper
+
+
+def tf_required(func):
+    warnings.warn(
+        "The method `tf_required` is deprecated and will be removed in v4.36. Use `requires_backends` instead.",
+        FutureWarning,
+    )
+
+    # Chose a different decorator name than in tests so it's clear they are not the same.
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        if is_tf_available():
+            return func(*args, **kwargs)
+        else:
+            raise ImportError(f"Method `{func.__name__}` requires TF.")
+
+    return wrapper
+
+
 def is_torch_fx_proxy(x):
     if is_torch_fx_available():
         import torch.fx