TensorBackward.vjp_slice: def of bendtorch-core@0.1.0.0
Import: import bendtorch-core@0.1.0.0/src/autograd/tensor_backward.bend as TensorBackward
Lines 650-656 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=650&end_line=656

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/slice.bend as Slice

# d(a slice of a)/da: g read with the kept shape, the slice's output with the
# axis kept (PyTorch's unsqueeze for select), then scattered into +0.0 words
# of a's shape (PyTorch's slice_backward): each input cell the slice read
# gets the gradient word of the output cell it went to.
def vjp_slice(+a: Nat, +input: Shape.Shape, +kept: Shape.Shape, +plan: Slice.Plan, g: Core.Dynamic) -> Pass:
  Core.Packed{+shape, tensor} = g
  slice_unsqueezed(a, input, kept, plan, shape, tensor, Shape.bool_case(Nat.is_eq(Shape.elements(shape), Shape.elements(kept))))

Uses (9):
  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=slice_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
  https://jend.leeeet.dev/definition.txt?path=bendtorch-core@0.1.0.0/src/tensor/slice.bend&name=Plan