class_docstr: Wraps the AOT executor runtime.Module.
  
  This is a thin wrapper of the underlying TVM module.
  you can also directly call set_input, run, and get_output
  of underlying module functions
  
  Parameters
  ----------
  module : tvm.runtime.Module
      The internal tvm module that holds the implemented model functions.
  
  Attributes
  ----------
  module : tvm.runtime.Module
      The internal tvm module that holds the implemented model functions.
  
  Examples
  --------
  
  .. code-block:: python
  
      import tvm
      from tvm import relay
      from tvm.contrib import graph_executor
  
      # build the library using graph executor
      lib = relay.build(...)
      lib.export_library("compiled_lib.so")
      # load it back as a runtime
      lib: tvm.runtime.Module = tvm.runtime.load_module("compiled_lib.so")
      # Call the library factory function for default and create
      # a new runtime.Module, wrap with aot module.
      gmod = tvm.runtime.executor.AotModule(lib["default"](dev))
      # use the aot  module.
      gmod.set_input("x", data)
      gmod.run()
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code: def __init__(self, module):
      self.module = module
      self._set_input = module["set_input"]
      self._run = module["run"]
      self._get_output = module["get_output"]
      self._get_input = module["get_input"]
      self._get_num_outputs = module["get_num_outputs"]
      self._get_input_index = module["get_input_index"]
      self._get_num_inputs = module["get_num_inputs"]
      self._get_input_name = module["get_input_name"]
  
file_path: tvm/python/tvm/runtime/executor/aot_executor.py
imports: import numpy
label_desc: Use image node_id 1 to create a new AotModule object from inherited base classes: object with example: obj = AotModule(module)