diff --git a/src/brevitas/function/ops.py b/src/brevitas/function/ops.py
index 6751ab69c..74da08e19 100644
--- a/src/brevitas/function/ops.py
+++ b/src/brevitas/function/ops.py
@@ -189,7 +189,7 @@ def min_int(signed: bool, narrow_range: bool, bit_width: Tensor) -> Tensor:
     return value
 
 
-@brevitas.jit.script
+@brevitas.jit.ignore
 def max_float(exponent_bit_width: Tensor, mantissa_bit_width: Tensor, exponent_bias: Tensor):
     max_exponent = (2. ** exponent_bit_width) - 1. - exponent_bias
     max_mantissa = torch.sum((
diff --git a/src/brevitas/jit.py b/src/brevitas/jit.py
index 0719e1017..6acf43728 100644
--- a/src/brevitas/jit.py
+++ b/src/brevitas/jit.py
@@ -14,6 +14,7 @@ def _disabled(fn):
 
     script_method = torch.jit.script_method
     script = torch.jit.script
+    ignore = torch.jit.ignore
     ScriptModule = torch.jit.ScriptModule
     Attribute = torch.jit.Attribute
 
@@ -21,5 +22,6 @@ def _disabled(fn):
 
     script_method = _disabled
     script = _disabled
+    ignore = _disabled
     ScriptModule = torch.nn.Module
     Attribute = lambda val, type: val
diff --git a/src/brevitas/utils/torch_utils.py b/src/brevitas/utils/torch_utils.py
index a2aed48d9..225cacaac 100644
--- a/src/brevitas/utils/torch_utils.py
+++ b/src/brevitas/utils/torch_utils.py
@@ -104,6 +104,7 @@ def float_internal_scale(
     return internal_scale
 
 
+@brevitas.jit.ignore
 def padding(x, group_size, group_dim):
     # Given a tensor X, compute the padding aloing group_dim so that groupwise shaping is possible
     padding = [0, 0] * len(x.shape)