code: def _prepare_op_inputs(inputs, run_backward, dtype, ctx, module):
      mx.random.seed(41)
      kwargs_list = []
      if module == "mxnet.numpy_extension" or module == "mxnet.numpy":
          PARAMS_TYPE = PARAMS_OF_TYPE_NP_ARRAY
          get_array_fn = get_mx_np_ndarray
      else:
          PARAMS_TYPE = PARAMS_OF_TYPE_NDARRAY
          get_array_fn = get_mx_ndarray
  
      for inp in inputs:
          kwargs = {}
          for key, value in inp.items():
              if key in PARAMS_TYPE:
                  kwargs[key] = get_array_fn(
                      ctx=ctx,
                      in_tensor=value,
                      dtype=dtype,
                      initializer=nd.normal,
                      attach_grad=run_backward,
                  )
              else:
                  kwargs[key] = value
          kwargs_list.append(kwargs)
      return kwargs_list
  
file_path: mxnet/benchmark/opperf/utils/benchmark_utils.py
imports: import logging
  import inspect
  import mxnet
  from mxnet import nd
  from mxnet import np
  from .ndarray_utils import get_mx_ndarray, nd_forward_and_profile, nd_forward_backward_and_profile
  from .common_utils import merge_map_list
  from .op_registry_utils import prepare_op_inputs
  from benchmark.opperf.rules.default_params import PARAMS_OF_TYPE_NDARRAY, PARAMS_OF_TYPE_NP_ARRAY
  from .profiler_utils import cpp_profile, python_profile
label_desc: Use image node_id 1 for calling a global function with example usage: _prepare_op_inputs(inputs, run_backward, dtype, ctx, module) and returns: kwargs_list