class_docstr: An Auxiliary class for dynamic shape benchmarks
  
  Pre-computes input with random shapes and also
  modifies the compute method so in each call the
  fuser sees a different input tensor shape
code: def load_inputs(self):
      for i in range(self.SAMPLE_SIZE - 1):
          self.instantiate_input()
  
imports: import contextlib
  import json
  import os
  import time
  import numpy
  import torch
  from .None import tensor_engine
label_desc: Use image node_id 7 for calling the DynamicShape obj's underlying member method code with example usage: obj.load_inputs() without return types