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 generate_code(self) -> Callable:
      def apply_module(inp, _):
          res = self.module(inp)
          return res
  
      return apply_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 2 for calling the ModuleWrapper obj's underlying member method code with example usage: obj.generate_code() and returns: apply_module, res