code: def workflow_state_from_dag(
      dag_node: DAGNode,
      input_context: Optional[DAGInputData],
      workflow_id: str,
  ):
      """
      Transform a Ray DAG to a workflow. Map FunctionNode to workflow task with
      the workflow decorator.
  
      Args:
          dag_node: The DAG to be converted to a workflow.
          input_context: The input data that wraps varibles for the input node of the DAG.
          workflow_id: The ID of the workflow.
      """
      if not isinstance(dag_node, FunctionNode):
          raise TypeError(
              "Currently workflow does not support classes as DAG inputs."
          )
  
      state = WorkflowExecutionState()
  
      # TODO(suquark): remove this cyclic importing later by changing the way of
      # task ID assignment.
      from ray.workflow.workflow_access import get_management_actor
  
      mgr = get_management_actor()
      context = workflow_context.get_workflow_task_context()
  
      def _node_visitor(node: Any) -> Any:
          if isinstance(node, FunctionNode):
              bound_options = node._bound_options.copy()
              num_returns = bound_options.get("num_returns", 1)
              if (
                  num_returns is None
              ):  # ray could use `None` as default value
                  num_returns = 1
              if num_returns > 1:
                  raise ValueError(
                      "Workflow task can only have one return."
                  )
  
              workflow_options = bound_options.get("_metadata", {}).get(
                  WORKFLOW_OPTIONS, {}
              )
  
              # If checkpoint option is not specified, inherit checkpoint
              # options from context (i.e. checkpoint options of the outer
              # task). If it is still not specified, it's True by default.
              checkpoint = workflow_options.get("checkpoint", None)
              if checkpoint is None:
                  checkpoint = (
                      context.checkpoint
                      if context is not None
                      else True
                  )
              # When it returns a nested workflow, catch_exception
              # should be passed recursively.
              catch_exceptions = workflow_options.get(
                  "catch_exceptions", None
              )
              if catch_exceptions is None:
                  if (
                      node.get_stable_uuid()
                      == dag_node.get_stable_uuid()
                  ):
                      # 'catch_exception' context should be passed down to
                      # its direct continuation task.
                      # In this case, the direct continuation is the output node.
                      catch_exceptions = (
                          context.catch_exceptions
                          if context is not None
                          else False
                      )
                  else:
                      catch_exceptions = False
  
              # We do not need to check the validness of bound options, because
              # Ray option has already checked them for us.
              max_retries = bound_options.get("max_retries", 3)
              retry_exceptions = bound_options.get(
                  "retry_exceptions", False
              )
  
              task_options = WorkflowTaskRuntimeOptions(
                  task_type=TaskType.FUNCTION,
                  catch_exceptions=catch_exceptions,
                  retry_exceptions=retry_exceptions,
                  max_retries=max_retries,
                  checkpoint=checkpoint,
                  ray_options=bound_options,
              )
  
              workflow_refs: List[WorkflowRef] = []
              with serialization_context.workflow_args_serialization_context(
                  workflow_refs
              ):
                  _func_signature = signature.extract_signature(
                      node._body
                  )
                  flattened_args = signature.flatten_args(
                      _func_signature,
                      node._bound_args,
                      node._bound_kwargs,
                  )
                  # NOTE: When calling 'ray.put', we trigger python object
                  # serialization. Under our serialization context,
                  # Workflows are separated from the arguments,
                  # leaving a placeholder object with all other python objects.
                  # Then we put the placeholder object to object store,
                  # so it won't be mutated later. This guarantees correct
                  # semantics. See "tests/test_variable_mutable.py" as
                  # an example.
                  if client_mode_should_convert():
                      # Handle client mode. The Ray client would serialize and
                      # then deserialize objects in the Ray client server. When
                      # the object is being deserialized, the serialization context
                      # will be missing, resulting in failures. Here we protect the
                      # object from deserialization in client server, and we make sure
                      # the 'real' deserialization happens under the serialization
                      # context later.
                      flattened_args = (
                          _SerializationContextPreservingWrapper(
                              flattened_args
                          )
                      )
                  # Set the owner of the objects to the actor so that even the driver
                  # exits, these objects are still available.
                  input_placeholder: ray.ObjectRef = ray.put(
                      flattened_args, _owner=mgr
                  )
  
              orig_task_id = workflow_options.get("task_id", None)
              if orig_task_id is None:
                  orig_task_id = f"{get_module(node._body)}.{slugify(get_qualname(node._body))}"
  
              task_id = ray.get(
                  mgr.gen_task_id.remote(workflow_id, orig_task_id)
              )
              state.add_dependencies(
                  task_id, [s.task_id for s in workflow_refs]
              )
              state.task_input_args[task_id] = input_placeholder
  
              user_metadata = workflow_options.get("metadata", {})
  
              validate_user_metadata(user_metadata)
              state.tasks[task_id] = Task(
                  task_id=task_id,
                  options=task_options,
                  user_metadata=user_metadata,
                  func_body=node._body,
              )
              return WorkflowRef(task_id)
  
          if isinstance(node, InputAttributeNode):
              return node._execute_impl()  # get data from input node
          if isinstance(node, InputNode):
              return input_context  # replace input node with input data
          if not isinstance(node, DAGNode):
              return node  # return normal objects
          raise TypeError(f"Unsupported DAG node: {node}")
  
      output_workflow_ref = dag_node.apply_recursive(_node_visitor)
      state.output_task_id = output_workflow_ref.task_id
      return state
  
imports: from typing import Any, List, Optional
  import re
  import unicodedata
  import ray
  from ray.workflow.common import WORKFLOW_OPTIONS
  from ray.dag import DAGNode, FunctionNode, InputNode
  from ray.dag.input_node import InputAttributeNode, DAGInputData
  from ray import cloudpickle
  from ray._private import signature
  from ray._private.client_mode_hook import client_mode_should_convert
  from ray.workflow import serialization_context
  from ray.workflow.common import TaskType, WorkflowTaskRuntimeOptions, WorkflowRef, validate_user_metadata
  from ray.workflow import workflow_context
  from ray.workflow.workflow_state import WorkflowExecutionState, Task
label_desc: Use image node_id 4 for calling a global function with example usage: workflow_state_from_dag(dag_node, input_context, workflow_id) and returns: state, WorkflowRef, node, input_context, node