class_docstr: Base class for functions using straight through gradient estimators.
  
  This class approximates the gradient with the identity function.
class_docstr_tok: ["Base","class","for","functions","using","straight","through","gradient","estimators",".","This","class","approximates","the","gradient","with","the","identity","function","."]
docstr: Use a straight-through estimator the gradient.
  
  This uses the identity function.
  
  Args:
      grad_output: A tensor of gradients.
  
  Returns:
      The provided tensor.
docstr_tok: ["Use","a","straight-through","estimator","the","gradient",".","This","uses","the","identity","function",".","Args",":","grad_output",":","A","tensor","of","gradients",".","Returns",":","The","provided","tensor","."]
code: def backward(ctx, grad_output: Tensor) -> Tensor:
      r"""Use a straight-through estimator the gradient.
  
      This uses the identity function.
  
      Args:
          grad_output: A tensor of gradients.
  
      Returns:
          The provided tensor.
      """
      return grad_output
  
file_path: botorch/botorch/utils/rounding.py
imports: from __future__ import annotations
  import torch
  from torch import Tensor
  from torch.autograd import Function
  from torch.nn.functional import one_hot
label_desc: Use image node_id 1 for calling the IdentitySTEFunction obj's underlying member method code with example usage: obj.backward(ctx, grad_output) and returns: grad_output