class_docstr: create a transform with random parameters
  
  Example (on the yaml)
  
  .. code-block:: yaml
  
      transform: RandomParamTransform
          params:
              transform_name: GridSampling3D
              transform_params:
                  size:
                      min: 0.1
                      max: 0.3
                      type: "float"
                  mode:
                      value: "last"
  
  
  We can also draw random numbers for two parameters, integer or float
  
  .. code-block:: yaml
  
      transform: RandomParamTransform
          params:
              transform_name: RandomSphereDropout
              transform_params:
                  radius:
                      min: 1
                      max: 2
                      type: "float"
                  num_sphere:
                      min: 1
                      max: 5
                      type: "int"
  
  
  Parameters
  ----------
  transform_name: string:
      the name of the transform
  transform_options: Omegaconf Dict
      contains the name of a variables as a key and min max type as value to specify the range of the parameters and the type of the parameters or it contains the value "value" to specify a variables (see Example above)
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code: def __call__(self, data):
      self.random_transform = (
          self._instanciate_transform_with_random_params()
      )
      return self.random_transform(data)
  
imports: import sys
  import numpy
  import torch_geometric.transforms
  from .transforms import *
  from .grid_transform import *
  from .sparse_transforms import *
  from .inference_transforms import *
  from .feature_augment import *
  from .features import *
  from .filters import *
  from .precollate import *
  from .prebatchcollate import *
  from omegaconf.dictconfig import DictConfig
  from omegaconf.listconfig import ListConfig
  from omegaconf import OmegaConf
label_desc: Use image node_id 3 for calling the RandomParamTransform obj's underlying member method code with example usage: obj.__call__(data) and returns: self