Here's the code to import `process` from `thefuzz`, store unique cuisine types, and calculate similarity of 'asian', 'american', and 'italian' to all possible cuisine types:
```python
from fuzzywuzzy import process
# Store the unique values of cuisine_type in unique_types
unique_types = restaurants['cuisine_type'].unique()
# Calculate similarity of 'asian' to all values of unique_types
print(process.extract('asian', unique_types, limit=len(unique_types)))
# Calculate similarity of 'american' to all values of unique_types
print(process.extract('american', unique_types, limit=len(unique_types)))
# Calculate similarity of 'italian' to all values of unique_types
print(process.extract('italian', unique_types, limit=len(unique_types)))
```
In this code, we first import `process` from `fuzzywuzzy`.
We then use the `.unique()` method to get the unique values of the `cuisine_type` column in the `restaurants` DataFrame, and store them in a new variable called `unique_types`.
Finally, we use the `process.extract()` function to calculate the similarity of 'asian', 'american', and 'italian' to all possible cuisine types. We pass the string to match as the first argument, the list of choices as the second argument (`unique_types`), and the number of matches to return as the `limit` parameter (which we set to the length of `unique_types`). We print the results using the `print()` function.