diff --git a/elasticai/creator/base_modules/autograd_functions/round_to_block_biased_float.py b/elasticai/creator/base_modules/autograd_functions/round_to_block_biased_float.py
new file mode 100644
index 00000000..ffb51921
--- /dev/null
+++ b/elasticai/creator/base_modules/autograd_functions/round_to_block_biased_float.py
@@ -0,0 +1,36 @@
+from typing import Any
+
+import torch
+
+
+class RoundToBlockMinifloat(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx: Any, *args: Any, **kwargs: Any) -> torch.Tensor:
+        if len(args) != 3:
+            raise TypeError(
+                "apply() takes exactly three arguments ("
+                "x: torch.Tensor, mantissa_bits: int, exponent_bits: int"
+                ")"
+            )
+        x: torch.Tensor = args[0]
+        mantissa_bits: int = args[1]
+        exponent_bits: int = args[2]
+
+        shared_bias = x.abs().log2().floor().max() - (2**exponent_bits - 1)
+
+        largest_value = (2 - 1 / 2**mantissa_bits) * 2 ** (
+            (2**exponent_bits - 1) - shared_bias
+        )
+
+        out_of_bounds = (x < -largest_value) | (x > largest_value)
+        if torch.any(out_of_bounds):
+            raise ValueError("Cannot quantize tensor. Values out of bounds.")
+
+        # TODO: Here we are...
+
+        scale = 2 ** (x.abs().log2().floor() - mantissa_bits)
+        return scale * torch.round(x / scale)
+
+    @staticmethod
+    def backward(ctx: Any, *grad_outputs: Any) -> Any:
+        return *grad_outputs, None, None