Loss.mse_scale: def of bendtorch-core@0.1.0.0
Import: import bendtorch-core@0.1.0.0/src/autograd/loss.bend as Loss
Lines 45-48 of bendtorch-core@0.1.0.0/src/autograd/loss.bend: https://jend.leeeet.dev/read.txt?path=bendtorch-core@0.1.0.0/src/autograd/loss.bend&start_line=45&end_line=48

import Base
import bendtorch-core@0.1.0.0/src/shape/core.bend as Shape

# 2 / N, the factor of PyTorch's mse_loss backward with mean reduction, for
# the N cells of the broadcast shape.
def mse_scale(+shape: Shape.Shape) -> F32:
  F32.div(2.0, count_f32(Shape.logical_numel(Shape.dimensions(shape))))

Uses (6):
  https://jend.leeeet.dev/definition.txt?path=Base/base.bend&name=F32
  https://jend.leeeet.dev/definition.txt?path=Base/base.bend&name=F32.div
  https://jend.leeeet.dev/definition.txt?path=bendtorch-core@0.1.0.0/src/autograd/loss.bend&name=count_f32
  https://jend.leeeet.dev/definition.txt?path=bendtorch-core@0.1.0.0/src/shape/core.bend&name=Shape
  https://jend.leeeet.dev/definition.txt?path=bendtorch-core@0.1.0.0/src/shape/core.bend&name=dimensions
  https://jend.leeeet.dev/definition.txt?path=bendtorch-core@0.1.0.0/src/shape/core.bend&name=logical_numel