class_docstr: Runtime execution mode of DataStream programs. Among other things, this controls task
  scheduling, network shuffle behavior, and time semantics. Some operations will also change
  their record emission behaviour based on the configured execution mode.
  
  :data:`STREAMING`:
  
  The Pipeline will be executed with Streaming Semantics. All tasks will be deployed before
  execution starts, checkpoints will be enabled, and both processing and event time will be
  fully supported.
  
  :data:`BATCH`:
  
  The Pipeline will be executed with Batch Semantics. Tasks will be scheduled gradually based
  on the scheduling region they belong, shuffles between regions will be blocking, watermarks
  are assumed to be "perfect" i.e. no late data, and processing time is assumed to not advance
  during execution.
  
  :data:`AUTOMATIC`:
  
  Flink will set the execution mode to BATCH if all sources are bounded, or STREAMING if there
  is at least one source which is unbounded.
class_docstr_tok: ["Runtime","execution","mode","of","DataStream","programs",".","Among","other","things",",","this","controls","task","scheduling",",","network","shuffle","behavior",",","and","time","semantics",".","Some","operations","will","also","change","their","record","emission","behaviour","based","on","the","configured","execution","mode",".",":","data",":","`","STREAMING","`",":","The","Pipeline","will","be","executed","with","Streaming","Semantics",".","All","tasks","will","be","deployed","before","execution","starts",",","checkpoints","will","be","enabled",",","and","both","processing","and","event","time","will","be","fully","supported",".",":","data",":","`","BATCH","`",":","The","Pipeline","will","be","executed","with","Batch","Semantics",".","Tasks","will","be","scheduled","gradually","based","on","the","scheduling","region","they","belong",",","shuffles","between","regions","will","be","blocking",",","watermarks","are","assumed","to","be","``","perfect","''","i.e",".","no","late","data",",","and","processing","time","is","assumed","to","not","advance","during","execution",".",":","data",":","`","AUTOMATIC","`",":","Flink","will","set","the","execution","mode","to","BATCH","if","all","sources","are","bounded",",","or","STREAMING","if","there","is","at","least","one","source","which","is","unbounded","."]
code: def _from_j_execution_mode(
      j_execution_mode,
  ) -> "RuntimeExecutionMode":
      return RuntimeExecutionMode[j_execution_mode.name()]
  
imports: from enum import Enum
  from pyflink.java_gateway import get_gateway
label_desc: Use image node_id 1 for calling the RuntimeExecutionMode obj's underlying member method code with example usage: obj._from_j_execution_mode(j_execution_mode) and returns: RuntimeExecutionMode