code: def explode_transform(transforms):
      """Returns a flattened list of transform
      Arguments:
          transforms {[list | T.Compose]} -- Contains list of transform to be added
  
      Returns:
          [list] -- [List of transforms]
      """
      out = []
      if transforms is not None:
          if isinstance(transforms, Compose):
              out = copy.deepcopy(transforms.transforms)
          elif isinstance(transforms, list):
              out = copy.deepcopy(transforms)
          else:
              raise Exception(
                  "Transforms should be provided either within a list or a Compose"
              )
      return out
  
imports: import os
  from abc import ABC, abstractmethod
  import logging
  import functools
  from functools import partial
  import numpy
  import torch
  import torch_geometric
  from torch_geometric.transforms import Compose, FixedPoints
  import copy
  from torch_points3d.models import model_interface
  from torch_points3d.core.data_transform import instantiate_transforms, MultiScaleTransform
  from torch_points3d.core.data_transform import instantiate_filters
  from torch_points3d.datasets.batch import SimpleBatch
  from torch_points3d.datasets.multiscale_data import MultiScaleBatch
  from torch_points3d.utils.enums import ConvolutionFormat
  from torch_points3d.utils.config import ConvolutionFormatFactory
  from torch_points3d.utils.colors import COLORS, colored_print
label_desc: Use image node_id 1 for calling a global function with example usage: explode_transform(transforms) and returns: out