class_docstr: Same as `nn.BCEWithLogitsLoss`, but flattens input and target.
docstr: `nn.BCEWithLogitsLoss`'s fused activation function applied to model output
docstr_tok: ["`","nn.BCEWithLogitsLoss","`","'s","fused","activation","function","applied","to","model","output"]
code: def activation(self, x: Tensor) -> Tensor:
      "`nn.BCEWithLogitsLoss`'s fused activation function applied to model output"
      return torch.sigmoid(x)
  
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 BCEWithLogitsLossFlat obj's underlying member method code with example usage: obj.activation(x) and returns: torch