code: def optimize(mod, target=None, params=None):
      """Helper function that optimizes a Relay module.
  
      Parameters
      ----------
      mod : :py:class:`~tvm.IRModule`
          The module to build. Using relay.Function is deprecated.
  
      target : None, or any multi-target like object, see Target.canon_multi_target
          For homogeneous compilation, the unique build target.
          For heterogeneous compilation, a dictionary or list of possible build targets.
          Defaults to the current target in the environment if None.
  
      params : dict of str to NDArray
          Input parameters to the graph that do not change
          during inference time. Used for constant folding.
  
      Returns
      -------
      mod : :py:class:`~tvm.IRModule`
          The optimized relay module.
  
      params : dict
          The parameters of the final graph.
      """
      if not isinstance(mod, (IRModule, _function.Function)):
          raise ValueError(
              "Type of input parameter mod must be tvm.IRModule"
          )
  
      if isinstance(mod, _function.Function):
          if params:
              mod = bind_params_by_name(mod, params)
          mod = IRModule.from_expr(mod)
          warnings.warn(
              "Please use input parameter mod (tvm.IRModule) "
              "instead of deprecated parameter func (tvm.relay.function.Function)",
              DeprecationWarning,
          )
  
      raw_targets = Target.canon_multi_target_and_host(
          Target.target_or_current(target)
      )
  
      # If current dispatch context is fallback context (the default root context),
      # then load pre-tuned parameters from TopHub
      if isinstance(
          autotvm.DispatchContext.current, autotvm.FallbackContext
      ):
          tophub_context = autotvm.tophub.context(raw_targets)
      else:
          tophub_context = autotvm.utils.EmptyContext()
  
      with tophub_context:
          bld_mod = BuildModule()
          mod, params = bld_mod.optimize(
              mod, target=raw_targets, params=params
          )
      return mod, params
  
imports: import warnings
  import numpy
  from tvm.ir import IRModule
  from tvm.target import Target
  from ..None import autotvm
  from ..None import nd
  from ..None import register_func
  from ..contrib import graph_executor
  from ..contrib import utils
  from ..runtime import load_module
  from ..runtime.executor import aot_executor
  from ..target import Target
  from .None import _build_module
  from .None import expr
  from .None import function
  from .None import ty
  from .backend import Executor, Runtime
  from .backend import executor_factory
  from .backend import interpreter
  from .backend.utils import mangle_module_name
  from .backend.vm import VMExecutor
  from .transform import InferType
label_desc: Use image node_id 6 for calling a global function with example usage: optimize(mod, target, params) and returns: mod,  params