Hello,

I am trying to replicate the below piece of code (which is in pymc3) to the latest pymc version:

```
with model_2:
prior_pred = pm.sample_prior_predictive()
fig, ax = plt.subplots(figsize = (20, 8))
_ = ax.plot(prior_pred["likelihood"].T, color = "0.5", alpha = 0.1)
_ = ax.plot(data_transformed[target].values[START_ANALYSIS_INDEX:END_ANALYSIS_INDEX], color = "red")
```

And below is my code :

```
with model_2:
prior_pred = pm.sample_prior_predictive()
fig, ax = plt.subplots(figsize = (20, 8))
_ = ax.plot(prior_pred.prior_predictive.likelihood.values.T, color = "0.5", alpha = 0.1)
_ = ax.plot(data_transformed[target].values[START_ANALYSIS_INDEX:END_ANALYSIS_INDEX], color = "red")
```

This however throws the below error:

```
ValueError Traceback (most recent call last)
Cell In[57], line 5
1 #prior_pred.prior_predictive.likelihood.values.T
2 #data_transformed[target].values[START_ANALYSIS_INDEX:END_ANALYSIS_INDEX]
4 fig, ax = plt.subplots(figsize = (20, 8))
----> 5 _ = ax.plot(prior_pred.prior_predictive.likelihood.values.T, color = "0.5", alpha = 0.1)
6 _ = ax.plot(data_transformed[target].values[START_ANALYSIS_INDEX:END_ANALYSIS_INDEX], color = "red")
File C:\Py\Lib\site-packages\matplotlib\axes\_axes.py:1688, in Axes.plot(self, scalex, scaley, data, *args, **kwargs)
1445 """
1446 Plot y versus x as lines and/or markers.
1447
(...)
1685 (``'green'``) or hex strings (``'#008000'``).
1686 """
1687 kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)
-> 1688 lines = [*self._get_lines(*args, data=data, **kwargs)]
1689 for line in lines:
1690 self.add_line(line)
File C:\Py\Lib\site-packages\matplotlib\axes\_base.py:311, in _process_plot_var_args.__call__(self, data, *args, **kwargs)
309 this += args[0],
310 args = args[1:]
--> 311 yield from self._plot_args(
312 this, kwargs, ambiguous_fmt_datakey=ambiguous_fmt_datakey)
File C:\Py\Lib\site-packages\matplotlib\axes\_base.py:507, in _process_plot_var_args._plot_args(self, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)
504 raise ValueError(f"x and y must have same first dimension, but "
505 f"have shapes {x.shape} and {y.shape}")
506 if x.ndim > 2 or y.ndim > 2:
--> 507 raise ValueError(f"x and y can be no greater than 2D, but have "
508 f"shapes {x.shape} and {y.shape}")
509 if x.ndim == 1:
510 x = x[:, np.newaxis]
ValueError: x and y can be no greater than 2D, but have shapes (291,) and (291, 500, 1)
```

How to replicate the code in pymc3 so that the shapes error is resolved.

My current likelihood values are as below:

```
array([[[
[
[
[
[
[
1184.862],
1187.833],
1191.296],
1188.727],
1189.769],
1190.376]],
1190.777],
1196.505],
1194.140],
1191.161],
1194.093],
1194.907]],
1190.804],
1194.115],
1191.686],
1193.252],
1189.390],
1193.189]],
[
[
[
[
[
1188.201],
1185.301],
1187.260],
1186.038],
1185.586],
1186.955]],
1184.725],
1185.391],
1185.949],
1186.355],
1188.212],
1187.906]],
1184.055],
1185.312],
1187.122],
1185.468],
1187.763],
1188.102]]])
```

This array is of shape 291, 500, 1.

291 is the number of rows in the data, 500 is the sample size during pm.sample.

Can anyone please help me resolve this?