UDF Connection to Lakehouse does not work

This works! Thanks a lot! 

Hi same issue here and I used your code - did not work. 

I am testing Microsoft Fabric User Data Functions in Develop mode and keep getting the following error even when using the official/default-style sample code for writing a CSV file to a Lakehouse.

The error happens at function registration time on this line:

```python
@udf.function()
```

It happens before the function is executed and before the UI test input is submitted.

**Code used**

We are using this code exactly, only changing the Lakehouse alias to our environment:

```python
import datetime
import logging

import fabric.functions as fn
import pandas as pd

udf = fn.UserDataFunctions()

@udf.connection(argName="myLakehouse", alias="dltranslyticalpoc")
@udf.function()
def write_csv_file_in_lakehouse(
myLakehouse: fn.FabricLakehouseClient,
employees: list,
) -> str:
"""
Writes employee data to Lakehouse Files as a CSV.
"""
logging.info("Starting CSV file write to Lakehouse")

csv_file_name = (
"Employees_" + str(round(datetime.datetime.now().timestamp())) + ".csv"
)

df = pd.DataFrame(employees, columns=["ID", "EmpName", "DepID"])
csv_string = df.to_csv(index=False)
csv_bytes = csv_string.encode("utf-8")

connection = myLakehouse.connectToFiles()
file_client = connection.get_file_client(csv_file_name)
file_client.upload_data(csv_bytes, overwrite=True)

file_client.close()
connection.close()

return f"File '{csv_file_name}' was uploaded successfully."
```

We also tried our original Fabric UDF code using a Lakehouse connection and request context:

```python
import fabric.functions as fn

udf = fn.UserDataFunctions()

@udf.connection(argName="retailLakehouse", alias="dltranslyticalpoc")
@udf.context(argName="requestContext")
@udf.function()
def submitReplenishment(
retailLakehouse: fn.FabricLakehouseClient,
requestContext: fn.UserDataFunctionContext,
requestId: int,
storeKey: int,
productKey: int,
actionType: str = "Replenish stock",
priority: str = "High",
triggerValue: int = 0,
thresholdValue: int = 10,
suggestedQuantity: int = 0,
comment: str = "",
) -> str:
return "OK"
```

Both versions fail with the same error.

**Error**

```text
AttributeError: 'str' object has no attribute '__name__'
```

Traceback excerpt:

```text
File ~/.local/lib/python3.12/site-packages/fabric/internal/decorators/configure_fabric_function_builder.py:97,
in configure_fabric_function_builder.<locals>.decorator(func)
fb = udf._validate_type(user_func)
udf._function_builders.append(fb)
_build_spec(user_func, udfParams, functions_metadata, func.__annotations__)

File ~/.local/lib/python3.12/site-packages/fabric/internal/decorators/configure_fabric_function_builder.py:195,
in _build_spec(user_func, udfParams, functions_metadata, func_annotations)
multipart_req, binary_res, multipart_req_model = get_multipart_content(user_func.__name__, udfParams)

File ~/.local/lib/python3.12/site-packages/fabric/internal/utils/spec_utils.py:18,
in get_multipart_content.<locals>.<lambda>(param)
lambda param: param.annotation.__name__ == "DataFrame"
or param.annotation.__name__ == "Series",

AttributeError: 'str' object has no attribute '__name__'
```

**What we expected**

The function should register successfully in Fabric UDF Develop mode, then allow us to test it with a list parameter such as:

```json
[[12345, "chris", 10]]
```

**What actually happens**

The function fails during decoration/registration at `@udf.function()`. The error occurs before the local test UI or Lakehouse write logic is reached.

**Question**

Is this a known issue with the Fabric UDF Python SDK, especially on Python 3.12, where function annotations may be interpreted as strings?

The traceback suggests Fabric is doing this internally:

```python
param.annotation.__name__
```

but at least one annotation appears to be a string, so the SDK raises:

```text
'str' object has no attribute '__name__'
```

Has anyone else hit this with the official/default Lakehouse CSV sample? Is there a required package version, runtime setting, notebook reset step, or workaround to force annotations to be evaluated as actual Python types rather than strings? 

Please also note alias connections and dependencies are all set up

Any guidance would be appreciated.