fix: lint

Author: andygrove

spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala

@@ -208,7 +208,7 @@ case class CometScanRule(session: SparkSession)
       !COMET_SCAN_ALLOW_DISABLED_PARQUET_VECTORIZED_READER.get()) {
       withInfo(
         scanExec,
-        s"Native Parquet scan is incompatible with " +
+        "Native Parquet scan is incompatible with " +
           s"${SQLConf.PARQUET_VECTORIZED_READER_ENABLED.key}=false; set " +
           s"${COMET_SCAN_ALLOW_DISABLED_PARQUET_VECTORIZED_READER.key}=true to opt in")
       return None
@@ -700,7 +700,7 @@ case class CometScanTypeChecker() extends DataTypeSupport with CometTypeShim {
       fallbackReasons: ListBuffer[String]): Boolean = {
     dt match {
       case ShortType if CometConf.COMET_PARQUET_UNSIGNED_SMALL_INT_CHECK.get() =>
-        fallbackReasons += s"Native Parquet scan may not handle unsigned UINT_8 correctly for " +
+        fallbackReasons += "Native Parquet scan may not handle unsigned UINT_8 correctly for " +
           s"$dt. Set ${CometConf.COMET_PARQUET_UNSIGNED_SMALL_INT_CHECK.key}=false to allow " +
           "native execution if your data does not contain unsigned small integers. " +
           CometConf.COMPAT_GUIDE

spark/src/test/scala/org/apache/spark/sql/benchmark/CometArrayExpressionBenchmark.scala

@@ -19,8 +19,6 @@
 
 package org.apache.spark.sql.benchmark
 
-import org.apache.comet.CometConf
-
 // spotless:off
 /**
  * Benchmark to measure performance of Comet array expressions. To run this benchmark: