code: def parse_input_ndarray(input_dict):
      """Parse input for ndarray and extract array shape for better readability
  
      Parameters
      ----------
      input_dict : dict
           Dictionary of input
  
      Input Dictionary
  
      'inputs': {'weight':
      [[ 2.2122064   0.7740038   1.0434405   1.1839255   1.8917114 ]
       [-1.2347414  -1.771029   -0.45138445  0.57938355 -1.856082  ]
       [-1.9768796  -0.20801921  0.2444218  -0.03716067 -0.48774993]
       [-0.02261727  0.57461417  1.4661262   0.6862904   0.35496104]
       [ 1.0731696   0.12017461 -0.9711102  -0.77569664 -0.7882176 ]]
      <NDArray 5x5 @cpu(0)>, 'grad':
      [[ 0.7417728  -1.4734439  -1.0730928  -1.0424827  -1.3278849 ]
       [-1.4749662  -0.52414197  1.2662556   0.8950642  -0.6015945 ]
       [ 1.2040559  -0.9712193  -0.58256227  0.3717077   0.9300072 ]
       [-1.4225755  -0.5176199   2.0088325   0.2863085   0.5604595 ]
       [ 0.96975976 -0.52853745 -1.88909     0.65479124 -0.45481315]]
      <NDArray 5x5 @cpu(0)>, 'mean':
      [[ 0.32510808 -1.3002341   0.3679345   1.4534262   0.24154152]
       [ 0.47898006  0.96885103 -1.0218245  -0.06812762 -0.31868345]
       [-0.17634277  0.35655284  0.74419165  0.7787424   0.6087823 ]
       [ 1.0741756   0.06642842  0.8486986  -0.8003802  -0.16882208]
       [ 0.93632793  0.357444    0.77932847 -1.0103073  -0.39157307]]
      <NDArray 5x5 @cpu(0)>, 'var':
      [[ 1.3166187  -0.43292624  0.71535987  0.9254156  -0.90495086]
       [-0.074684    0.82254    -1.8785107   0.8858836   1.9118724 ]
       [ 0.33342266  0.11883813 -1.9198899  -0.67558455  1.007749  ]
       [-0.35391203  1.6323917  -0.33354783 -1.7378405   0.7737382 ]
       [ 0.89126545  3.2904532  -1.1976235   1.8938874  -0.5669272 ]]
      <NDArray 5x5 @cpu(0)>, 't': 1, 'wd': 0.1}
  
      Output
      {'inputs': {'weight': '<NDArray 5x5 @cpu(0)>', 'grad': '<NDArray 5x5 @cpu(0)>', 'mean': '<NDArray 5x5 @cpu(0)>', 'var': '<NDArray 5x5 @cpu(0)>', 't': 1, 'wd': 0.1}
      """
      no_new_line_input_dict = dict()
      for key, value in input_dict.items():
          if isinstance(value, nd.NDArray):
              # if value in input is NDArray then extract last line only
              val = str(value).split("\n")[-1]
              no_new_line_input_dict[key] = val
          else:
              no_new_line_input_dict[key] = value
      return no_new_line_input_dict
  
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 4 for calling a global function with example usage: parse_input_ndarray(input_dict) and returns: no_new_line_input_dict