Loss.row_maxes: def of bendtorch-core@0.1.0.0
Import: import bendtorch-core@0.1.0.0/src/autograd/loss.bend as Loss
Lines 161-166 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=161&end_line=166

import Base
import bendtorch-core@0.1.0.0/src/shape/core.bend as Shape
import bendtorch-core@0.1.0.0/src/tensor/core.bend as Core
import bendtorch-core@0.1.0.0/src/tensor/matmul.bend as Matrix

# The maximum of each row of [rows, columns] logits, a [rows, 1] tensor.
# Each output cell walks its own row, so the cells are built in parallel,
# forked by the work of a row as Matrix.splits forks a matmul. The input
# comes back unchanged.
def row_maxes(input: Core.Dynamic, +target: Shape.Shape, +columns: Nat) -> Core.Dynamic & Result<&2, &1, Core.Error, Core.Dynamic>:
  max_allocated(input, target, columns, Core.new(target, 0.0))

Uses (8):
  https://jend.leeeet.dev/definition.txt?path=Base/base.bend&name=Nat
  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=max_allocated
  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/tensor/core.bend&name=Dynamic
  https://jend.leeeet.dev/definition.txt?path=bendtorch-core@0.1.0.0/src/tensor/core.bend&name=Error
  https://jend.leeeet.dev/definition.txt?path=bendtorch-core@0.1.0.0/src/tensor/core.bend&name=new