TensorBackward.vjp_sum_dims: def of bendtorch-core@0.1.0.0
Import: import bendtorch-core@0.1.0.0/src/autograd/tensor_backward.bend as TensorBackward
Lines 307-312 of bendtorch-core@0.1.0.0/src/autograd/tensor_backward.bend: https://jend.leeeet.dev/read.txt?path=bendtorch-core@0.1.0.0/src/autograd/tensor_backward.bend&start_line=307&end_line=312

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

# d(sum of a over some axes)/da: g read with the kept shape, the reduced axes
# put back as 1 (PyTorch's unsqueeze when keepdim is False), then broadcast
# to a's shape, as the Sum VJP does.
def vjp_sum_dims(+a: Nat, +input: Shape.Shape, +kept: Shape.Shape, g: Core.Dynamic) -> Pass:
  Core.Packed{+shape, tensor} = g
  unsqueezed(a, input, kept, shape, tensor, Shape.bool_case(Nat.is_eq(Shape.elements(shape), Shape.elements(kept))))

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=Nat.is_eq
  https://jend.leeeet.dev/definition.txt?path=bendtorch-core@0.1.0.0/src/autograd/tensor_backward.bend&name=Pass
  https://jend.leeeet.dev/definition.txt?path=bendtorch-core@0.1.0.0/src/autograd/tensor_backward.bend&name=unsqueezed
  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=bool_case
  https://jend.leeeet.dev/definition.txt?path=bendtorch-core@0.1.0.0/src/shape/core.bend&name=elements
  https://jend.leeeet.dev/definition.txt?path=bendtorch-core@0.1.0.0/src/tensor/core.bend&name=Dynamic