def TreeEq
bendtorch-core@0.1.0.0 · src/autograd/reduce_replayed_proof.bend:730
import bendtorch-core@0.1.0.0/src/autograd/reduce_replayed_proof.bend as ReduceReplayedProof
import Base
import bendtorch-core@0.1.0.0/src/shape/core.bend as Shape
import bendtorch-core@0.1.0.0/src/storage/LAWS.bend as StorageLaws
import bendtorch-core@0.1.0.0/src/storage/api.bend as Api
730def TreeEq(+plan: Shape.BroadcastPlan, +rows: List<&2, Shape.Row>,
731 +rows_exact: {rows == Shape.index_table(Shape.extents(Shape.left_shape(plan)), Shape.extents(Shape.right_shape(plan)), Shape.extents(Shape.output_shape(plan))) : List<&2, Shape.Row>},
732 +shape: Shape.Shape, +fill: F32, +ops: List<&2, StorageLaws.Op>, +h: List<&2, Api.Effect>, +d: Nat, +f: F32, +count: Nat, +depth: Nat, +base: Nat,
733 -result: Array<F32> & (Array<F32> & Unit)) -> Type:
734 {Pair.fst(Array<F32>, Array<F32> & Unit, result) == out_spec(plan, Shape.Indexed{rows, rows_exact}, shape, fill, ops, count, depth, base) : Array<F32>} &
735 {Pair.snd(Array<F32>, Array<F32> & Unit, result) == (Api.arr(h, d, f), Unit{}) : Array<F32> & Unit}