def test_standard_handling(self):
assert get_input_schema(standard_py_func) == self.standard_sample_input_schema
assert get_output_schema(standard_py_func) == self.standard_sample_output_schema
def test_spark_handling(self):
assert get_input_schema(spark_func) == self.spark_sample_input_schema
assert get_output_schema(spark_func) == self.spark_sample_output_schema
def test_pandas_handling(self):
assert get_input_schema(pandas_func) == self.pandas_sample_input_schema
assert get_output_schema(pandas_func) == self.pandas_sample_output_schema
def test_numpy_handling(self):
assert get_input_schema(numpy_func) == self.numpy_sample_input_schema
assert get_output_schema(numpy_func) == self.numpy_sample_output_schema
def test_nested_handling(self):
assert ordered(get_input_schema(nested_func)) == ordered(
self.nested_sample_input_schema)
assert ordered(get_output_schema(nested_func)) == ordered(
self.nested_sample_output_schema)
from inference_schema.schema_decorators import input_schema, output_schema
from inference_schema.parameter_types.standard_py_parameter_type import StandardPythonParameterType
# %%
def init():
print("This is init")
# %%
@input_schema('data', StandardPythonParameterType('input data'))
@output_schema(StandardPythonParameterType('test is inputdata'))
def run(data):
test = json.loads(data)
print(f"received data {test}")
return f"test is {test}"
# %%
from inference_schema import schema_util
# %%
schema_util.get_input_schema(run)
# %%
schema_util.get_output_schema(run)
# %%
schema_util.get_schemas_dict()
# %%
schema_util.is_schema_decorated(run)
# %%