Tinyml Workshop Runtime Error

Running through the tutorial: [Nano 33 BLE Sense Rev2](https://docs.arduino.cc/hardware/nano-33-ble-sense-rev2)
Get Started With Machine Learning on Arduino

In arduino_tinyml_workshop.ipynb in Colab
Got data and uploaded to Colab.
Running the cells, got to the cell: Run with Test Data'
Runtime errors on these lines:
plt.plot(inputs_test, outputs_test, 'b.', label='Actual')
plt.plot(inputs_test, predictions, 'r.', label='Predicted')

Added in a few prints of data...

**OUTPUT WITH ERRORS:**
1/1 [==============================] - 0s 39ms/step
predictions =
[[1. 0. ]
[0.998 0.002]
[0. 1. ]
[1. 0. ]]
inputs_test =
[[0.701375 0.520875 0.378875 ... 0.500183 0.499817 0.499878 ]
[0.5895 0.461625 0.743125 ... 0.500534 0.50016775 0.50004575]
[0.68425 0.4585 0.71725 ... 0.4999695 0.4987335 0.49971 ]
[0.66625 0.51975 0.3385 ... 0.500885 0.500702 0.5009155 ]]
actual =
[[1. 0.]
[1. 0.]
[0. 1.]
[1. 0.]]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[](https://localhost:8080/#) in ()
10 plt.clf()
11 plt.title('Training data predicted vs actual values')
---> 12 plt.plot(inputs_test, outputs_test, 'b.', label='Actual')
13 plt.plot(inputs_test, predictions, 'r.', label='Predicted')
14 plt.show()

3 frames
[/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_base.py](https://localhost:8080/#) in _plot_args(self, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)
520 ncx, ncy = x.shape[1], y.shape[1]
521 if ncx > 1 and ncy > 1 and ncx != ncy:
--> 522 raise ValueError(f"x has {ncx} columns but y has {ncy} columns")
523 if ncx == 0 or ncy == 0:
524 return []

ValueError: x has 714 columns but y has 2 columns

Open arduino_tinyml_workshop.ipynb in Colab and start at the “Run with Test Data” cell. Inspect the reported plotting lines alongside the printed shapes for inputs_test, outputs_test, and predictions, then rerun the cell. Done means the tutorial cell completes without the reported ValueError and displays the actual and predicted data plots.

Written by the indexing model from the issue text.