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: `F.cross_entropy`'s fused activation function applied to model output
docstr_tok: ["`","F.cross_entropy","`","'s","fused","activation","function","applied","to","model","output"]
code: def activation(self, x: Tensor) -> Tensor:
      "`F.cross_entropy`'s fused activation function applied to model output"
      return F.softmax(x, 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 3 for calling the FocalLossFlat obj's underlying member method code with example usage: obj.activation(x) and returns: F