class_docstr: Transform using the given torch.nn.Module
  
  Parameters
  ----------
  module: torch.nn.Module
      The module for transformation
class_docstr_tok: ["Transform","using","the","given","torch.nn.Module","Parameters","--","--","--","--","--","module",":","torch.nn.Module","The","module","for","transformation"]
code: def __init__(self, module: ch.nn.Module):
      super().__init__()
      self.module = module
  
file_path: ffcv/ffcv/transforms/module.py
imports: import torch
  from numpy.random import permutation, rand
  from typing import Callable, Optional, Tuple
  from ..pipeline.allocation_query import AllocationQuery
  from ..pipeline.operation import Operation
  from ..pipeline.state import State
label_desc: Use image node_id 1 to create a new ModuleWrapper object from inherited base classes: Operation with example: obj = ModuleWrapper(module)