class_docstr: This class stores pre-processed inputs for workflow task execution.
  Especially, all input workflows to the workflow task will be scheduled,
  and their outputs (ObjectRefs) replace the original workflows.
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docstr: This function resolves the inputs for the code inside
  a workflow task (works on the callee side). For outputs from other
  workflows, we resolve them into object instances inplace.
  
  For each ObjectRef argument, the function returns both the ObjectRef
  and the object instance. If the ObjectRef is a chain of nested
  ObjectRefs, then we resolve it recursively until we get the
  object instance, and we return the *direct* ObjectRef of the
  instance. This function does not resolve ObjectRef
  inside another object (e.g. list of ObjectRefs) to give users some
  flexibility.
  
  Returns:
      Instances of arguments.
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code: def resolve(
      self, store: workflow_storage.WorkflowStorage
  ) -> Tuple[List, Dict]:
      """
      This function resolves the inputs for the code inside
      a workflow task (works on the callee side). For outputs from other
      workflows, we resolve them into object instances inplace.
  
      For each ObjectRef argument, the function returns both the ObjectRef
      and the object instance. If the ObjectRef is a chain of nested
      ObjectRefs, then we resolve it recursively until we get the
      object instance, and we return the *direct* ObjectRef of the
      instance. This function does not resolve ObjectRef
      inside another object (e.g. list of ObjectRefs) to give users some
      flexibility.
  
      Returns:
          Instances of arguments.
      """
      workflow_ref_mapping = []
      for r in self.workflow_refs:
          if r.ref is None:
              workflow_ref_mapping.append(
                  store.load_task_output(r.task_id)
              )
          else:
              workflow_ref_mapping.append(r.ref)
  
      with serialization_context.workflow_args_resolving_context(
          workflow_ref_mapping
      ):
          # reconstruct input arguments under correct serialization context
          flattened_args: List[Any] = ray.get(self.args)
  
      # dereference arguments like Ray remote functions
      flattened_args = [
          ray.get(a) if isinstance(a, ObjectRef) else a
          for a in flattened_args
      ]
      return signature.recover_args(flattened_args)
  
file_path: ray/python/ray/workflow/task_executor.py
imports: import time
  from dataclasses import dataclass
  import logging
  from typing import List, Tuple, Any, Dict, Callable, TYPE_CHECKING
  import ray
  from ray import ObjectRef
  from ray._private import signature
  from ray.dag import DAGNode
  from ray.workflow import workflow_context
  from ray.workflow.workflow_context import get_task_status_info
  from ray.workflow import serialization_context
  from ray.workflow import workflow_storage
  from ray.workflow.common import WorkflowStatus, WorkflowExecutionMetadata, TaskType, TaskID, WorkflowRef, CheckpointMode
  from ray.workflow.workflow_state import WorkflowExecutionState
  from ray.workflow.workflow_state_from_dag import workflow_state_from_dag
label_desc: Use image node_id 1 for calling the _BakedWorkflowInputs obj's underlying member method code with example usage: obj.resolve(store) and returns: signature