class_docstr: Filters input based on arbitrary feature vectors. Uses a permutohedral
  lattice data structure to efficiently approximate n-dimensional gaussian
  filtering. Complexity is broadly independent of kernel size. Most applicable
  to higher filter dimensions and larger kernel sizes.
  
  See:
      https://graphics.stanford.edu/papers/permutohedral/
  
  Args:
      input: input tensor to be filtered.
      features: feature tensor used to filter the input.
      sigmas: the standard deviations of each feature in the filter.
  
  Returns:
      output (torch.Tensor): output tensor.
class_docstr_tok: ["Filters","input","based","on","arbitrary","feature","vectors",".","Uses","a","permutohedral","lattice","data","structure","to","efficiently","approximate","n-dimensional","gaussian","filtering",".","Complexity","is","broadly","independent","of","kernel","size",".","Most","applicable","to","higher","filter","dimensions","and","larger","kernel","sizes",".","See",":","https",":","\/\/graphics.stanford.edu\/papers\/permutohedral\/","Args",":","input",":","input","tensor","to","be","filtered",".","features",":","feature","tensor","used","to","filter","the","input",".","sigmas",":","the","standard","deviations","of","each","feature","in","the","filter",".","Returns",":","output","(","torch.Tensor",")",":","output","tensor","."]
code: def backward(ctx, grad_output):
      raise NotImplementedError(
          "PHLFilter does not currently support Backpropagation"
      )
  
imports: from __future__ import annotations
  import torch
  from monai.utils.module import optional_import
label_desc: Use image node_id 2 for calling the PHLFilter obj's underlying member method code with example usage: obj.backward(ctx, grad_output) without return types