class_docstr: Ensures input is between 0 and 1
class_docstr_tok: ["Ensures","input","is","between","0","and","1"]
code: def backward(ctx, grad_output):
      (input_data,) = ctx.saved_tensors
      grad_input = grad_output.clone()
      grad_input[input_data < 0] = 1e-2
      grad_input[input_data > 1] = -1e-2
      return grad_input
  
imports: import time
  import numpy
  import scipy.special
  import torch
  import torch.nn
  from .None import constants
  from .None import syssettings
  from .fginitialize import ChunkDataLoader
label_desc: Use image node_id 2 for calling the ramp obj's underlying member method code with example usage: obj.backward(ctx, grad_output) and returns: grad_input