class_docstr: Class implementing custom backward pass for compute_normalization. See compute_normalization.
code: def backward(ctx, grad_output):
      activations, normalization_constants = ctx.saved_tensors
      t = ctx.t
      normalized_activations = activations - normalization_constants
      probabilities = exp_t(normalized_activations, t)
      escorts = probabilities.pow(t)
      escorts = escorts / escorts.sum(dim=-1, keepdim=True)
      grad_input = escorts * grad_output
  
      return grad_input, None, None
  
imports: from typing import Optional
  import torch
  from torch import nn, Tensor
label_desc: Use image node_id 2 for calling the ComputeNormalization obj's underlying member method code with example usage: obj.backward(ctx, grad_output) and returns: grad_input,  None,  None