class_docstr: Same as CrossEntropyLossFlat but with focal paramter, `gamma`. Focal loss is introduced by Lin et al. 
  https://arxiv.org/pdf/1708.02002.pdf. Note the class weighting factor in the paper, alpha, can be 
  implemented through pytorch `weight` argument passed through to F.cross_entropy.
docstr: Converts model output to target format
docstr_tok: ["Converts","model","output","to","target","format"]
code: def decodes(self, x: Tensor) -> Tensor:
      "Converts model output to target format"
      return x.argmax(dim=self.axis)
  
imports: from __future__ import annotations
  from .imports import *
  from .torch_imports import *
  from .torch_core import *
  from .layers import *
label_desc: Use image node_id 2 for calling the FocalLossFlat obj's underlying member method code with example usage: obj.decodes(x) and returns: x