docstr: do langchain transformation for the input column,
  and save the transformed values to the output column.
docstr_tok: ["do","langchain","transformation","for","the","input","column",",","and","save","the","transformed","values","to","the","output","column","."]
code: def _transform(self, dataset):
      """
      do langchain transformation for the input column,
      and save the transformed values to the output column.
      """
      # Define the schema for the output of the UDF
      schema = StructType(
          [
              StructField("result", StringType(), True),
              StructField("error_message", StringType(), True),
          ]
      )
  
      @udf(schema)
      def udfFunction(x):
          import openai
  
          if self.running_on_synapse_internal and not self.isSet(
              self.url
          ):
              from synapse.ml.fabric.prerun.openai_prerun import (
                  OpenAIPrerun,
              )
  
              OpenAIPrerun(
                  api_base=self.getUrl()
              ).init_personalized_session(None)
          else:
              openai.api_type = "azure"
              openai.api_key = self.getSubscriptionKey()
              openai.api_base = self.getUrl()
              openai.api_version = self.getApiVersion()
  
          error_messages = {
              openai.error.Timeout: "OpenAI API request timed out, please retry your request after a brief wait and contact us if the issue persists: {}",
              openai.error.APIError: "OpenAI API returned an API Error: {}",
              openai.error.APIConnectionError: "OpenAI API request failed to connect, check your network settings, proxy configuration, SSL certificates, or firewall rules: {}",
              openai.error.InvalidRequestError: "OpenAI API request was invalid: {}",
              openai.error.AuthenticationError: "OpenAI API request was not authorized, please check your API key or token and make sure it is correct and active. You may need to generate a new one from your account dashboard: {}",
              openai.error.PermissionError: "OpenAI API request was not permitted, make sure your API key has the appropriate permissions for the action or model accessed: {}",
              openai.error.RateLimitError: "OpenAI API request exceeded rate limit: {}",
          }
  
          try:
              result = self.getChain().run(x)
              error_message = ""
          except tuple(error_messages.keys()) as e:
              result = ""
              error_message = error_messages[type(e)].format(e)
  
          return result, error_message
  
      outCol = self.getOutputCol()
      errorCol = self.getErrorCol()
      inCol = dataset[self.getInputCol()]
  
      temp_col_name = "result_" + str(self.uid)
  
      return (
          dataset.withColumn(temp_col_name, udfFunction(inCol))
          .withColumn(outCol, col(f"{temp_col_name}.result"))
          .withColumn(errorCol, col(f"{temp_col_name}.error_message"))
          .drop(temp_col_name)
      )
  
imports: import json
  from os import error
  from langchain.chains.loading import load_chain_from_config
  from pyspark import keyword_only
  from pyspark.ml import Transformer
  from pyspark.ml.param.shared import HasInputCol, HasOutputCol, Param
  from pyspark.ml.util import DefaultParamsReadable, DefaultParamsWritable, DefaultParamsReader, DefaultParamsWriter
  from pyspark.sql.functions import udf, col
  from pyspark.sql.types import StructType, StructField, StringType
  from typing import cast, Optional, TypeVar, Type
  from synapse.ml.core.platform import running_on_synapse_internal
label_desc: Use image node_id 15 for calling the LangchainTransformer obj's underlying member method code with example usage: obj._transform(dataset) and returns: dataset, result,  error_message