mypy fixes

doctr/models/modules/transformer/pytorch.py

@@ -50,7 +50,7 @@ def scaled_dot_product_attention(
     if mask is not None:
         # NOTE: to ensure the ONNX compatibility, masked_fill works only with int equal condition
         scores = scores.masked_fill(mask == 0, float("-inf"))  # type: ignore[attr-defined]
-    p_attn = torch.softmax(scores, dim=-1)
+    p_attn = torch.softmax(scores, dim=-1)  # type: ignore[call-overload]
     return torch.matmul(p_attn, value), p_attn