docstr: Path to a parquet file of a test dataset. If set, it is used to calculate
  statistics on it during training.
  
  The parquet file must be a dataframe that has the same columns as the model that
  is being trained. For example, if the model needs as input `DOCUMENT`, `TOKEN`,
  `WORD_EMBEDDINGS` (Features) and `NAMED_ENTITY` (label) then these columns also
  need to be present while saving the dataframe. The pre-processing steps for the
  training dataframe should also be applied to the test dataframe.
  
  An example on how to create such a parquet file could be:
  
  >>> # assuming preProcessingPipeline
  >>> (train, test) = data.randomSplit([0.8, 0.2])
  >>> preProcessingPipeline
  ...     .fit(test)
  ...     .transform(test)
  ...     .write
  ...     .mode("overwrite")
  ...     .parquet("test_data")
  >>> annotator.setTestDataset("test_data")
  
  Parameters
  ----------
  path : str
      Path to test dataset
  read_as : str, optional
      How to read the resource, by default ReadAs.SPARK
  options : dict, optional
      Options for reading the resource, by default {"format": "csv"}
code: def setTestDataset(
      self, path, read_as=ReadAs.SPARK, options={"format": "parquet"}
  ):
      """Path to a parquet file of a test dataset. If set, it is used to calculate
      statistics on it during training.
  
      The parquet file must be a dataframe that has the same columns as the model that
      is being trained. For example, if the model needs as input `DOCUMENT`, `TOKEN`,
      `WORD_EMBEDDINGS` (Features) and `NAMED_ENTITY` (label) then these columns also
      need to be present while saving the dataframe. The pre-processing steps for the
      training dataframe should also be applied to the test dataframe.
  
      An example on how to create such a parquet file could be:
  
      >>> # assuming preProcessingPipeline
      >>> (train, test) = data.randomSplit([0.8, 0.2])
      >>> preProcessingPipeline
      ...     .fit(test)
      ...     .transform(test)
      ...     .write
      ...     .mode("overwrite")
      ...     .parquet("test_data")
      >>> annotator.setTestDataset("test_data")
  
      Parameters
      ----------
      path : str
          Path to test dataset
      read_as : str, optional
          How to read the resource, by default ReadAs.SPARK
      options : dict, optional
          Options for reading the resource, by default {"format": "csv"}
      """
      return self._set(
          testDataset=ExternalResource(path, read_as, options.copy())
      )
  
file_path: spark-nlp/python/sparknlp/annotator/param/evaluation_dl_params.py
imports: from sparknlp.common import *
  from sparknlp.internal import ParamsGettersSetters
label_desc: Use image node_id 6 for calling the EvaluationDLParams obj's underlying member method code with example usage: obj.setTestDataset(path, read_as, options) and returns: self