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[tool.poetry]
name = "example-project"
version = "0.1.0"
description = ""
authors = ["Test Name <test@gmail.com>"]

[tool.poetry.dependencies]
python = "~3.10"
jax = [
    {version="0.4.21", extras=["cuda11_pip"], platform="linux"},

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struct FilteredWalk{F, T, G}
    filter_set::F
    prune::T
    rebuilder::G
end
FilteredWalk(filter_set; prune = identity, rebuilder = identity) =
    FilteredWalk(filter_set, prune, rebuilder)

function (walk::FilteredWalk)(recurse, x, ys...)
    children, re = Functors.functor(x)

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# This file is machine-generated - editing it directly is not advised

julia_version = "1.7.2"
manifest_format = "2.0"

[[deps.AbstractFFTs]]
deps = ["ChainRulesCore", "LinearAlgebra"]
git-tree-sha1 = "6f1d9bc1c08f9f4a8fa92e3ea3cb50153a1b40d4"
uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
version = "1.1.0"

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# This file is machine-generated - editing it directly is not advised

julia_version = "1.7.2"
manifest_format = "2.0"

[[deps.AbstractFFTs]]
deps = ["ChainRulesCore", "LinearAlgebra"]
git-tree-sha1 = "6f1d9bc1c08f9f4a8fa92e3ea3cb50153a1b40d4"
uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
version = "1.1.0"

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using Random: shuffle!
using Base: @nexprs, @ntuple

function _splitobs(lm::Dict{T,Vector{I}}, at::AbstractFloat) where {T,I<:Integer}
    0 < at < 1 || throw(ArgumentError("the parameter \"at\" must be in interval (0, 1)"))
    n = mapreduce(length, +, values(lm))
    k = length(keys(lm))
    # preallocate the indices vectors
    idx1 = Vector{I}()
    idx2 = Vector{I}()

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MDSRC = $(wildcard ./markdown/*.md)
BIB = ./ref.bib
TEXPATH = ./tex
TEXSRC = main
OUTDIR = ./output

debug:
	@echo $(MDSRC)
	@echo $(MDOUT)

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using DiffEqFlux, OrdinaryDiffEq, Flux, Printf
using Flux.Losses: logitcrossentropy
using Flux.Data: DataLoader
using MLDatasets
using MLDataUtils:  LabelEnc, convertlabel, stratifiedobs
using CUDA
CUDA.allowscalar(false)

function loadmnist(batchsize = bs, train_split = 0.9)
    # Use MLDataUtils LabelEnc for natural onehot conversion

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struct OptionAIdentity
    pool::MaxPool
end
OptionAIdentity(scale::Integer) = OptionAIdentity(MaxPool((scale, scale)))
OptionAIdentity(scale::Tuple{<:Integer, <:Integer}) = OptionAIdentity(MaxPool(scale))
function (op::OptionAIdentity)(x, y)
    z = op.pool(y)
    npadchannels = size(x, 3) - size(z, 3)

    return (npadchannels > 0) ? cat(z, zeros(Float32, size(z, 1), size(z, 2), npadchannels, size(z, 4)); dims = 3) : z

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TREE: bitstream.types.SBitstream.sqrt(x)(SimulationId("bitstream.types.SBitstream", "x"))
TRANSFORM: /Users/darsnack/Code/BitSAD/BitBench/IterativeSVD/IterativeSVD.scala
TREE: var z: bitstream.types.SBitstream = bitstream.types.SBitstream.sqrt(x)(SimulationId("bitstream.types.SBitstream", "x"))
TREE: {
  var z: bitstream.types.SBitstream = bitstream.types.SBitstream.sqrt(x)(SimulationId("bitstream.types.SBitstream", "x"));
  var b: bitstream.types.SBitstream = z.$plus(y)(SimulationId("z", "y"));
  b
}
TREE: ({
  var z: bitstream.types.SBitstream = bitstream.types.SBitstream.sqrt(x)(SimulationId("bitstream.types.SBitstream", "x"));

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package bitstream.macros

import scala.language.experimental.macros
import scala.reflect.macros.blackbox.Context

object Macros {

  def simulatable[T](expr: T): Any = macro simulatable_impl[T]

  def simulatable_impl[T](c: Context)(expr: c.Expr[T]): c.Expr[T] = {