class_docstr: A class that stores the results of tvmc.run and provides helper utilities.
class_docstr_tok: ["A","class","that","stores","the","results","of","tvmc.run","and","provides","helper","utilities","."]
docstr: Create a convenience wrapper around the output of tvmc.run
  
  Parameters
  ----------
  outputs : dict
      Outputs dictionary mapping the name of the output to its numpy value.
  times : BenchmarkResult
      The execution times measured by the time evaluator in seconds to produce outputs.
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code: def __init__(
      self, outputs: Dict[str, np.ndarray], times: BenchmarkResult
  ):
      """Create a convenience wrapper around the output of tvmc.run
  
      Parameters
      ----------
      outputs : dict
          Outputs dictionary mapping the name of the output to its numpy value.
      times : BenchmarkResult
          The execution times measured by the time evaluator in seconds to produce outputs.
      """
      self.outputs = outputs
      self.times = times
  
imports: import os
  import tarfile
  import json
  from typing import Optional, Union, Dict, Callable, TextIO
  from pathlib import Path
  import numpy
  import tvm
  import tvm.contrib.cc
  from tvm import relay
  from tvm.contrib import utils
  from tvm.driver.tvmc import TVMCException
  from tvm.relay.backend.executor_factory import GraphExecutorFactoryModule
  from tvm.runtime.module import BenchmarkResult
  from tvm.runtime.vm import Executable
label_desc: Use image node_id 1 to create a new TVMCResult object from inherited base classes: object with example: obj = TVMCResult(outputs, times)