code: def test_infer_signature_on_list_of_dictionaries():
      signature = infer_signature(
          model_input=[{"query": "test query"}],
          model_output=[
              {
                  "output": "Output from the LLM",
                  "candidate_ids": ["412", "1233"],
                  "candidate_sources": ["file1.md", "file201.md"],
              }
          ],
      )
      assert signature.inputs == Schema(
          [ColSpec(DataType.string, name="query")]
      )
      assert signature.outputs == Schema(
          [
              ColSpec(DataType.string, name="output"),
              ColSpec(Array(DataType.string), name="candidate_ids"),
              ColSpec(Array(DataType.string), name="candidate_sources"),
          ]
      )
  
imports: import json
  import numpy
  import pandas
  import pyspark
  import pytest
  from sklearn.ensemble import RandomForestRegressor
  import mlflow
  from mlflow.exceptions import MlflowException
  from mlflow.models import Model, ModelSignature, infer_signature, set_signature
  from mlflow.models.model import get_model_info
  from mlflow.types import DataType
  from mlflow.types.schema import Array, ColSpec, ParamSchema, ParamSpec, Schema, TensorSpec
label_desc: Use image node_id 6 for calling a global function with example usage: test_infer_signature_on_list_of_dictionaries() without return types