code: def _find_and_set_features_col_as_vector_if_needed(
      spark_df, spark_model
  ):
      """
      Finds the `featuresCol` column in spark_model and
      then tries to cast that column to `vector` type.
      This method is noop if the `featuresCol` is already of type `vector`
      or if it can't be cast to `vector` type
      Note:
      If a spark ML pipeline contains a single Estimator stage, it requires
      the input dataframe to contain features column of vector type.
      But the autologging for pyspark ML casts vector column to array<double> type
      for parity with the pd Dataframe. The following fix is required, which transforms
      that features column back to vector type so that the pipeline stages can correctly work.
      A valid scenario is if the auto-logged input example is directly used
      for prediction, which would otherwise fail without this transformation.
  
      :param spark_df: Input dataframe that contains `featuresCol`
      :param spark_model: A pipeline model or a single transformer that contains `featuresCol` param
      :return: A spark dataframe that contains features column of `vector` type.
      """
      from pyspark.ml.linalg import Vectors, VectorUDT
      from pyspark.sql import types as t
      from pyspark.sql.functions import udf
  
      def _find_stage_with_features_col(stage):
          if stage.hasParam("featuresCol"):
  
              def _array_to_vector(input_array):
                  return Vectors.dense(input_array)
  
              array_to_vector_udf = udf(
                  f=_array_to_vector, returnType=VectorUDT()
              )
              features_col_name = stage.extractParamMap().get(
                  stage.featuresCol
              )
              features_col_type = [
                  _field
                  for _field in spark_df.schema.fields
                  if _field.name == features_col_name
                  and _field.dataType
                  in [
                      t.ArrayType(t.DoubleType(), True),
                      t.ArrayType(t.DoubleType(), False),
                  ]
              ]
              if len(features_col_type) == 1:
                  return spark_df.withColumn(
                      features_col_name,
                      array_to_vector_udf(features_col_name),
                  )
          return spark_df
  
      if hasattr(spark_model, "stages"):
          for stage in reversed(spark_model.stages):
              return _find_stage_with_features_col(stage)
      return _find_stage_with_features_col(spark_model)
  
imports: import logging
  import os
  import posixpath
  import re
  import shutil
  from typing import Any, Dict, Optional
  import pandas
  import yaml
  from packaging.version import Version
  import mlflow
  from mlflow import environment_variables, mleap, pyfunc
  from mlflow.environment_variables import MLFLOW_DFS_TMP
  from mlflow.exceptions import MlflowException
  from mlflow.models import Model, ModelInputExample, ModelSignature, infer_signature
  from mlflow.models.model import MLMODEL_FILE_NAME
  from mlflow.models.signature import _LOG_MODEL_INFER_SIGNATURE_WARNING_TEMPLATE
  from mlflow.models.utils import _Example, _save_example
  from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
  from mlflow.store.artifact.databricks_artifact_repo import DatabricksArtifactRepository
  from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri, _get_root_uri_and_artifact_path
  from mlflow.utils import _get_fully_qualified_class_name, databricks_utils
  from mlflow.utils.autologging_utils import autologging_integration, safe_patch
  from mlflow.utils.class_utils import _get_class_from_string
  from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring
  from mlflow.utils.environment import _CONDA_ENV_FILE_NAME, _CONSTRAINTS_FILE_NAME, _PYTHON_ENV_FILE_NAME, _REQUIREMENTS_FILE_NAME, _mlflow_conda_env, _process_conda_env, _process_pip_requirements, _PythonEnv, _validate_env_arguments
  from mlflow.utils.file_utils import TempDir, get_total_file_size, shutil_copytree_without_file_permissions, write_to
  from mlflow.utils.model_utils import _add_code_from_conf_to_system_path, _get_flavor_configuration_from_uri, _validate_and_copy_code_paths
  from mlflow.utils.requirements_utils import _get_pinned_requirement
  from mlflow.utils.uri import append_to_uri_path, dbfs_hdfs_uri_to_fuse_path, generate_tmp_dfs_path, get_databricks_profile_uri_from_artifact_uri, is_databricks_acled_artifacts_uri, is_local_uri, is_valid_dbfs_uri
label_desc: Use image node_id 16 for calling a global function with example usage: _find_and_set_features_col_as_vector_if_needed(spark_df, spark_model) and returns: _find_stage_with_features_col, spark_df, _find_stage_with_features_col, Vectors, spark_df