class_docstr: Graph executor factory module.
  This is a module of graph executor factory
  
  Attributes
  ----------
  ir_mod : :py:class:`~tvm.IRModule`
      The IR module to build.
  target : tvm.Target
      The Target used to build this module.
  executor : tvm.relay.backend.Executor
      Internal representation of the Executor
  graph_json_str : the json graph to be deployed in json format output by graph compiler.
      The graph can contain operator(tvm_op) that points to the name of
      PackedFunc in the libmod.
  libmod : tvm.Module
      The module of the corresponding function
  libmod_name: str
      The name of module
  params : dict of str to NDArray
      The parameters of module
  function_metadata : Map of String to FunctionInfo
      This holds a map function names to their information
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code: def __init__(
      self,
      ir_mod,
      target,
      executor,
      graph_json_str,
      libmod,
      libmod_name,
      params,
      function_metadata,
  ):
      assert isinstance(graph_json_str, string_types)
      fcreate = get_global_func("tvm.graph_executor_factory.create")
      args = []
      for k, v in params.items():
          args.append(k)
          args.append(ndarray.array(v))
  
      self.ir_mod = ir_mod
      self.target = target
      self.executor = executor
      self.module = fcreate(graph_json_str, libmod, libmod_name, *args)
      self.graph_json = graph_json_str
      self.lib = libmod
      self.libmod_name = libmod_name
      self.params = params
      self.iter_cnt = 0
      self.function_metadata = function_metadata
  
imports: from abc import abstractmethod
  import warnings
  from ..._ffi.base import string_types
  from ..._ffi.registry import get_global_func
  from ...runtime import ndarray
label_desc: Use image node_id 1 to create a new GraphExecutorFactoryModule object from inherited base classes: ExecutorFactoryModule with example: obj = GraphExecutorFactoryModule(ir_mod, target, executor, graph_json_str, libmod, libmod_name, params, function_metadata)