code: def _prepare_jit(
      model, qconfig_dict, inplace=False, quant_type=QuantType.STATIC
  ):
      _check_is_script_module(model)
      _check_forward_method(model)
      if not all(isinstance(x, str) for x in qconfig_dict.keys()):
          raise ValueError(
              "qconfig_dict should only contain names(str) as keys."
          )
      scripted_qconfig_dict = script_qconfig_dict(qconfig_dict)
      model = fuse_conv_bn_jit(model, inplace)
      model_c = torch._C._jit_pass_insert_observers(
          model._c,
          "forward",
          scripted_qconfig_dict,
          inplace,
          quant_type,
      )
      if inplace:
          model._reconstruct(model_c)
      else:
          model = wrap_cpp_module(model_c)
      return model
  
imports: import torch
  from torch.ao.quantization.qconfig import QConfig
  from torch.ao.quantization.quant_type import QuantType
  from torch.jit._recursive import wrap_cpp_module
label_desc: Use image node_id 6 for calling a global function with example usage: _prepare_jit(model, qconfig_dict, inplace, quant_type) and returns: model