class_docstr: Benchmark that times performance of numexpr execution.
code: def setup(self):
      self.session = new_session(default=True)
      self.asv_subtasks = []
      for _ in range(100):
          a = mt.arange(1e6)
          b = mt.arange(1e6) * 0.1
          c = mt.sin(a) + mt.arcsinh(a / b)
          subtask_id = new_task_id()
          subtask_chunk_graph = _gen_subtask_chunk_graph(c)
          self.asv_subtasks.append(
              _ASVSubtaskInfo(
                  subtask_id=subtask_id,
                  serialized_subtask_chunk_graph=serialize(
                      subtask_chunk_graph
                  ),
              )
          )
  
          c = a * b - 4.1 * a > 2.5 * b
          subtask_id = new_task_id()
          subtask_chunk_graph = _gen_subtask_chunk_graph(c)
          self.asv_subtasks.append(
              _ASVSubtaskInfo(
                  subtask_id=subtask_id,
                  serialized_subtask_chunk_graph=serialize(
                      subtask_chunk_graph
                  ),
              )
          )
  
file_path: mars/benchmarks/asv_bench/benchmarks/execution.py
imports: import dataclasses
  import unittest.mock
  import mars.tensor
  from mars import new_session
  from mars.core.graph import TileableGraph, TileableGraphBuilder, ChunkGraphBuilder, ChunkGraph
  from mars.serialization import serialize
  from mars.services.task import new_task_id
  from mars.services.task.execution.ray import executor
label_desc: Use image node_id 1 for calling the NumExprExecutionSuite obj's underlying member method code with example usage: obj.setup() without return types