I am learning dask and facing a challenge. The following code is a minimum replication code. Please kindly advise how to write a compact code to parallelize a loop that applies the same model class to different size of data.

```
import dask
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.datasets import make_regression
samples=[make_regression(n_samples=100,n_features=nx,noise=10,random_state=10) for nx in range(1,30)]
# This is a simple OLS here, but I want to define a big model somewhere else
model = LinearRegression()
def reg(y,X,f):
y = pd.DataFrame(y)
X = pd.DataFrame(X)
y_pred = f.fit(y=y.iloc[:-1,:],X=X.iloc[:-1,:]).predict(X=X.iloc[-1:,:])
return y_pred
# this gives error saying ValueError: X has 9 features, but LinearRegression is expecting 20 features as input. Sometimes 9 becomes 6, 13, 17 etc, a bit random...
results = dask.compute(*[dask.delayed(reg)(X=X,y=y,f=model) for (X,y) in samples])
# This works, which is puzzling since I thought dask just parallelly process the same thing...
results = [reg(y=y,X=X,f=model) for (X,y) in samples]
# this works, so the error seems to come from initiating a class?
results = dask.compute(*[dask.delayed(reg)(X=X,y=y,f=LinearRegression()) for (X,y) in samples])
# This is inconvenient since I need to write a big model inline. How can I use "model" instead of "LinearRegression()" to write the code?
```