code: def test_write_eval_table_to_delta_append(spark_session_with_delta):
      spark_session, tmpdir = spark_session_with_delta
      with mlflow.start_run():
          model_info = mlflow.pyfunc.log_model(
              artifact_path="model",
              python_model=language_model,
              input_example=["a", "b"],
          )
          data = pd.DataFrame(
              {"text": ["Hello world", "My name is MLflow"]}
          )
          mlflow.evaluate(
              model_info.model_uri,
              data,
              extra_metrics=[mlflow.metrics.latency()],
              evaluators="default",
              evaluator_config={
                  "eval_results_path": "my_path",
                  "eval_results_mode": "overwrite",
              },
          )
  
          mlflow.evaluate(
              model_info.model_uri,
              data,
              extra_metrics=[mlflow.metrics.latency()],
              evaluators="default",
              evaluator_config={
                  "eval_results_path": "my_path",
                  "eval_results_mode": "append",
              },
          )
  
          eval_table_from_delta = spark_session.read.format(
              "delta"
          ).load(f"{tmpdir}/my_path")
  
          assert eval_table_from_delta.count() == 4
  
file_path: mlflow/tests/evaluate/test_default_evaluator_delta.py
imports: import tempfile
  from typing import List
  import pandas
  import pytest
  from pyspark.sql import SparkSession
  import mlflow
  from mlflow.exceptions import MlflowException
label_desc: Use image node_id 6 for calling a global function with example usage: test_write_eval_table_to_delta_append(spark_session_with_delta) without return types