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 inputs(self, val):
      self._input_samples.append(val)
  
imports: import contextlib
  import json
  import os
  import time
  import numpy
  import torch
  from .None import tensor_engine
label_desc: Use image node_id 3 for calling the DynamicShape obj's underlying member method code with example usage: obj.inputs(val) without return types