fix: throw an error message when setting is_row_processor=True to rea…

…d a multi param function (#1160)

* fix: throw an error message when setting is_row_processor=True to read a multi param function

* add type ignore

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bigframes/functions/remote_function.py

@@ -130,6 +130,12 @@ def read_gbq_function(
     except google.api_core.exceptions.NotFound:
         raise ValueError(f"Unknown function '{routine_ref}'. {constants.FEEDBACK_LINK}")
 
+    if is_row_processor and len(routine.arguments) > 1:
+        raise ValueError(
+            "A multi-input function cannot be a row processor. A row processor function "
+            "takes in a single input representing the row."
+        )
+
     try:
         ibis_signature = ibis_signature_from_routine(routine)
     except ReturnTypeMissingError:

bigframes/session/__init__.py

@@ -1362,6 +1362,58 @@ def read_gbq_function(
             2    TestCad$123456Str
             dtype: string
 
+        Another use case is to define your own remote funtion and use it later.
+        For example, define the remote function:
+
+            >>> @bpd.remote_function()
+            ... def tenfold(num: int) -> float:
+            ...     return num * 10
+
+        Then, read back the deployed BQ remote function:
+
+            >>> tenfold_ref = bpd.read_gbq_function(
+            ...     tenfold.bigframes_remote_function,
+            ... )
+
+            >>> df = bpd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [5, 6]})
+            >>> df
+                a   b   c
+            0   1   3   5
+            1   2   4   6
+            <BLANKLINE>
+            [2 rows x 3 columns]
+
+            >>> df['a'].apply(tenfold_ref)
+            0    10.0
+            1    20.0
+            Name: a, dtype: Float64
+
+        It also supports row processing by using `is_row_processor=True`. Please
+        note, row processor implies that the function has only one input
+        parameter.
+
+            >>> @bpd.remote_function()
+            ... def row_sum(s: bpd.Series) -> float:
+            ...     return s['a'] + s['b'] + s['c']
+
+            >>> row_sum_ref = bpd.read_gbq_function(
+            ...     row_sum.bigframes_remote_function,
+            ...     is_row_processor=True,
+            ... )
+
+            >>> df = bpd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [5, 6]})
+            >>> df
+                a   b   c
+            0   1   3   5
+            1   2   4   6
+            <BLANKLINE>
+            [2 rows x 3 columns]
+
+            >>> df.apply(row_sum_ref, axis=1)
+            0     9.0
+            1    12.0
+            dtype: Float64
+
         Args:
             function_name (str):
                 The function's name in BigQuery in the format

tests/system/small/test_remote_function.py

@@ -839,6 +839,21 @@ def test_read_gbq_function_enforces_explicit_types(
         )
 
 
+@pytest.mark.flaky(retries=2, delay=120)
+def test_read_gbq_function_multiple_inputs_not_a_row_processor(session):
+    with pytest.raises(ValueError) as context:
+        # The remote function has two args, which cannot be row processed. Throw
+        # a ValueError for it.
+        session.read_gbq_function(
+            function_name="bqutil.fn.cw_regexp_instr_2",
+            is_row_processor=True,
+        )
+    assert str(context.value) == (
+        "A multi-input function cannot be a row processor. A row processor function "
+        "takes in a single input representing the row."
+    )
+
+
 @pytest.mark.flaky(retries=2, delay=120)
 def test_df_apply_axis_1(session, scalars_dfs, dataset_id_permanent):
     columns = [
@@ -864,6 +879,8 @@ def add_ints(row):
             dataset_id_permanent,
             name=get_rf_name(add_ints, is_row_processor=True),
         )(add_ints)
+        assert add_ints_remote.bigframes_remote_function  # type: ignore
+        assert add_ints_remote.bigframes_cloud_function  # type: ignore
 
     with pytest.warns(
         bigframes.exceptions.PreviewWarning, match="axis=1 scenario is in preview."
@@ -882,6 +899,19 @@ def add_ints(row):
         pd_result, bf_result, check_dtype=False, check_exact=False
     )
 
+    # Read back the deployed BQ remote function using read_gbq_function.
+    func_ref = session.read_gbq_function(
+        function_name=add_ints_remote.bigframes_remote_function,  # type: ignore
+        is_row_processor=True,
+    )
+
+    assert func_ref.bigframes_remote_function == add_ints_remote.bigframes_remote_function  # type: ignore
+
+    bf_result_gbq = scalars_df[columns].apply(func_ref, axis=1).to_pandas()
+    pd.testing.assert_series_equal(
+        pd_result, bf_result_gbq, check_dtype=False, check_exact=False
+    )
+
 
 @pytest.mark.flaky(retries=2, delay=120)
 def test_df_apply_axis_1_ordering(session, scalars_dfs, dataset_id_permanent):