```python
from .debug_utils import DebugUnderflowOverflow
debug_overflow = DebugUnderflowOverflow(model)
```
[~debug_utils.DebugUnderflowOverflow] inserts hooks into the model that immediately after each
forward call will test input and output variables and also the corresponding module's weights. As soon as inf or
nan is detected in at least one element of the activations or weights, the program will assert and print a report
like this (this was caught with google/mt5-small under fp16 mixed precision):
Detected inf/nan during batch_number=0
Last 21 forward frames:
abs min  abs max  metadata
                  encoder.block.1.layer.1.DenseReluDense.dropout Dropout
0.00e+00 2.57e+02 input[0]
0.00e+00 2.85e+02 output
[...]
                  encoder.block.2.layer.0 T5LayerSelfAttention
6.78e-04 3.15e+03 input[0]
