Loss.mse_loss: def of bendtorch-core@0.1.0.0
Import: import bendtorch-core@0.1.0.0/src/autograd/loss.bend as Loss
Lines 250-257 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=250&end_line=257

import Base
import bendtorch-core@0.1.0.0/src/tensor/core.bend as Core
import bendtorch-core@0.1.0.0/src/tensor/elementwise.bend as Elementwise

# PyTorch's mse_loss(input, target) with mean reduction: d = input - target
# and e = target - input over the broadcast shape, then the mean of d * d.
# Both inputs come back unchanged, next to the loss and e and d (in that
# order, the order the tape saves them), the two differences the backward
# scales: PyTorch's backward computes
# (2 / N * d) * g for the input and (2 / N * e) * g for the target.
def mse_loss(input: Core.Dynamic, target: Core.Dynamic) -> (Core.Dynamic & Core.Dynamic) & Result<&2, &1, Error, Recorded>:
  mse_first(Elementwise.binary(Elementwise.Sub{}, input, target))

Uses (8):
  https://jend.leeeet.dev/definition.txt?path=Base/base.bend&name=Pair
  https://jend.leeeet.dev/definition.txt?path=Base/base.bend&name=Result
  https://jend.leeeet.dev/definition.txt?path=bendtorch-core@0.1.0.0/src/autograd/loss.bend&name=Error
  https://jend.leeeet.dev/definition.txt?path=bendtorch-core@0.1.0.0/src/autograd/loss.bend&name=Recorded
  https://jend.leeeet.dev/definition.txt?path=bendtorch-core@0.1.0.0/src/autograd/loss.bend&name=mse_first
  https://jend.leeeet.dev/definition.txt?path=bendtorch-core@0.1.0.0/src/tensor/core.bend&name=Dynamic
  https://jend.leeeet.dev/definition.txt?path=bendtorch-core@0.1.0.0/src/tensor/elementwise.bend&name=Operation
  https://jend.leeeet.dev/definition.txt?path=bendtorch-core@0.1.0.0/src/tensor/elementwise.bend&name=binary