diff --git a/torch/ao/quantization/observer.py b/torch/ao/quantization/observer.py
index 6a39bdc0fc3..673d52e8924 100644
--- a/torch/ao/quantization/observer.py
+++ b/torch/ao/quantization/observer.py
@@ -493,6 +493,7 @@ class MinMaxObserver(UniformQuantizationObserverBase):
     .. note:: If the running minimum equals to the running maximum, the scale
               and zero_point are set to 1.0 and 0.
     """
+
     min_val: torch.Tensor
     max_val: torch.Tensor
 
@@ -702,6 +703,7 @@ class PerChannelMinMaxObserver(UniformQuantizationObserverBase):
     .. note:: If the running minimum equals to the running maximum, the scales
               and zero_points are set to 1.0 and 0.
     """
+
     min_val: torch.Tensor
     max_val: torch.Tensor
 
@@ -997,6 +999,7 @@ class HistogramObserver(UniformQuantizationObserverBase):
     3. Compute the scale and zero point the same way as in the
         :class:`~torch.ao.quantization.MinMaxObserver`
     """
+
     histogram: torch.Tensor
     min_val: torch.Tensor
     max_val: torch.Tensor
@@ -1524,6 +1527,7 @@ class RecordingObserver(ObserverBase):
         qscheme: Quantization scheme to be used
         reduce_range: Reduces the range of the quantized data type by 1 bit
     """
+
     __annotations__ = {"tensor_val": list[Optional[torch.Tensor]]}
 
     def __init__(self, dtype=torch.quint8):
@@ -1790,7 +1794,7 @@ def get_block_size(
         ), f"Expecting input shape dim to be 2 for per group quantization, gotinput shape: {input_shape}"
         return (1, granularity.group_size)
     elif isinstance(granularity, PerToken):
-        block_size = list(input_shape)
+        block_size = [1] * len(input_shape)
         block_size[-1] = input_shape[-1]
         return tuple(block_size)
     raise ValueError(f"Unsupported Granularity: {granularity}")