diff --git a/pytorch_lightning/callbacks/gradient_accumulation_scheduler.py b/pytorch_lightning/callbacks/gradient_accumulation_scheduler.py
index 97ea029619..7b723c3fc9 100644
--- a/pytorch_lightning/callbacks/gradient_accumulation_scheduler.py
+++ b/pytorch_lightning/callbacks/gradient_accumulation_scheduler.py
@@ -21,6 +21,8 @@ Trainer also calls ``optimizer.step()`` for the last indivisible step number.
 
 """
 
+from typing import Dict
+
 from pytorch_lightning.callbacks.base import Callback
 
 
@@ -44,7 +46,7 @@ class GradientAccumulationScheduler(Callback):
         >>> trainer = Trainer(accumulate_grad_batches={5: 2})
     """
 
-    def __init__(self, scheduling: dict):
+    def __init__(self, scheduling: Dict[int, int]):
         super().__init__()
 
         if not scheduling:  # empty dict error
@@ -56,7 +58,9 @@ class GradientAccumulationScheduler(Callback):
 
         minimal_epoch = min(scheduling.keys())
         if minimal_epoch < 0:
-            raise IndexError(f"Epochs indexing from 1, epoch {minimal_epoch} cannot be interpreted correct")
+            raise IndexError(
+                f"Epochs indexing from 1, epoch {minimal_epoch} cannot be interpreted correct"
+            )
         if minimal_epoch != 0:  # if user didnt define first epoch accumulation factor
             scheduling.update({0: 1})