SABR calibration

Hello,
I'm trying to use the SABR calibration with real BTC option prices but it stops right away without any error message (is_converged is False).
If anyone could help to see what is wrong with this pretty simple code:
[Colab notebook (created a wheel with the latest code)](https://colab.research.google.com/drive/120q5yXZ7p2PvHupNPnOFiLZrknHHiU9A?usp=sharing)

```python
import pandas as pd
import requests
import tf_quant_finance as tff
import time
from datetime import datetime
import numpy

vals = pd.DataFrame(requests.get(
'https://www.deribit.com/api/v2/public/get_instruments?currency=BTC&expired=false&kind=option').json()['result'])
jul_exp = vals[(vals['expiration_timestamp'] == 1627632000000)]
jul_exp['price'] = [requests.get(f'https://www.deribit.com/api/v2/public/ticker?instrument_name={x}').json()['result']['last_price'] for x in jul_exp['instrument_name']]
jul_exp.dropna(subset=['price'], inplace = True)
underlying = requests.get('https://www.deribit.com/api/v2/public/ticker?instrument_name=BTC-30JUL21').json()['result']
jul_exp['price'] = jul_exp['price'] * underlying['index_price']
is_call = numpy.array([[t == 'call' for t in jul_exp['option_type']]], dtype=numpy.bool)
delta =(( jul_exp.iloc[0]['expiration_timestamp']/1000 - time.time()) / 60 / 60 / 24) / 365
dtype = numpy.float64
models, is_converged, it = tff.models.sabr.approximations.calibration(
prices=numpy.array([jul_exp['price'].to_numpy()], dtype=dtype),
strikes=numpy.array([jul_exp['strike'].to_numpy()], dtype=dtype),
expiries=numpy.array([delta], dtype=dtype),
forwards=numpy.array([underlying['index_price']], dtype=dtype),
is_call_options=is_call,
beta=numpy.array([0.5], dtype=dtype),
calibrate_beta=True,
nu=numpy.array([0.1], dtype=dtype),
nu_lower_bound=0.0,
nu_upper_bound=10.0,
rho=numpy.array([0.0], dtype=dtype),
rho_lower_bound=-0.75,
rho_upper_bound=0.75,
maximum_iterations=1000)
```