diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 4de17ea..cdd43af 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -27,7 +27,7 @@ jobs:
       matrix:
         python-version: ["3.9", "3.10", "3.11"]
         os: [ubuntu-latest, windows-latest, macOS-latest]
-        backend: [torch, numpy, jax, object]
+        backend: [torch, numpy, jax]
     
     name: Python ${{ matrix.python-version }} - OS ${{ matrix.os }} - Backend ${{ matrix.backend }}
 
@@ -101,15 +101,6 @@ jobs:
         env:
           CASKADE_BACKEND: ${{ matrix.backend }}
 
-      - name: Extra coverage report for object checks
-        if:
-            ${{ matrix.python-version == '3.10' && matrix.os == 'ubuntu-latest' && matrix.backend == 'torch' }}
-        run: |
-          echo "Running extra coverage report for object checks"
-          coverage run --append --source=${{ env.PROJECT_NAME }} -m pytest tests/
-        shell: bash
-        env:
-          CASKADE_BACKEND: object
       - name: Extra coverage report for jax checks
         if:
             ${{ matrix.python-version == '3.10' && matrix.os == 'ubuntu-latest' && matrix.backend == 'torch' }}
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 37a9df1..169f1f9 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,5 +1,5 @@
 ipywidgets
-jupyter-book
+jupyter-book<2.0
 matplotlib
 sphinx
 sphinx_rtd_theme
diff --git a/docs/source/notebooks/AdvancedGuide.ipynb b/docs/source/notebooks/AdvancedGuide.ipynb
index 191bb35..73cd613 100644
--- a/docs/source/notebooks/AdvancedGuide.ipynb
+++ b/docs/source/notebooks/AdvancedGuide.ipynb
@@ -37,29 +37,30 @@
     "class Gaussian(ck.Module):\n",
     "    def __init__(self, name, x0=None, y0=None, q=None, phi=None, sigma=None, I0=None):\n",
     "        super().__init__(name)\n",
-    "        self.x0 = ck.Param(\"x0\", x0) # position\n",
+    "        self.x0 = ck.Param(\"x0\", x0)  # position\n",
     "        self.y0 = ck.Param(\"y0\", y0)\n",
-    "        self.q = ck.Param(\"q\", q) # axis ratio\n",
-    "        self.phi = ck.Param(\"phi\", phi) # orientation\n",
-    "        self.sigma = ck.Param(\"sigma\", sigma) # width\n",
-    "        self.I0 = ck.Param(\"I0\", I0) # intensity\n",
+    "        self.q = ck.Param(\"q\", q)  # axis ratio\n",
+    "        self.phi = ck.Param(\"phi\", phi)  # orientation\n",
+    "        self.sigma = ck.Param(\"sigma\", sigma)  # width\n",
+    "        self.I0 = ck.Param(\"I0\", I0)  # intensity\n",
     "\n",
     "    @ck.forward\n",
     "    def _r(self, x, y, x0=None, y0=None, q=None, phi=None):\n",
     "        x, y = x - x0, y - y0\n",
     "        s, c = torch.sin(phi), torch.cos(phi)\n",
     "        x, y = c * x - s * y, s * x + c * y\n",
-    "        return (x ** 2 + (y * q) ** 2).sqrt()\n",
-    "    \n",
+    "        return (x**2 + (y * q) ** 2).sqrt()\n",
+    "\n",
     "    @ck.forward\n",
     "    def brightness(self, x, y, sigma=None, I0=None):\n",
-    "        return I0 * (-self._r(x, y)**2 / sigma**2).exp()\n",
-    "    \n",
+    "        return I0 * (-self._r(x, y) ** 2 / sigma**2).exp()\n",
+    "\n",
+    "\n",
     "class Combined(ck.Module):\n",
     "    def __init__(self, name, first, second, ratio=0.5):\n",
     "        super().__init__(name)\n",
-    "        self.first = first # Modules are automatically registered\n",
-    "        self.ratio = ck.Param(\"ratio\", ratio, valid=(0,1))\n",
+    "        self.first = first  # Modules are automatically registered\n",
+    "        self.ratio = ck.Param(\"ratio\", ratio, valid=(0, 1))\n",
     "        self.second = second\n",
     "\n",
     "    @ck.forward\n",
@@ -96,18 +97,23 @@
     "        total += k\n",
     "\n",
     "        # Getting values from Param objects\n",
-    "        total += x ** 2 # as arg of function (preferred)\n",
-    "        total += y ** 2 # as kwarg of function (preferred)\n",
-    "        total += self.x.value ** 2 # by attribute (allowed but discouraged)\n",
-    "        total += self.submod.I0.value ** 2 # by attribute of submod (allowed but may indicate inefficient code)\n",
+    "        total += x**2  # as arg of function (preferred)\n",
+    "        total += y**2  # as kwarg of function (preferred)\n",
+    "        total += self.x.value**2  # by attribute (allowed but discouraged)\n",
+    "        total += (\n",
+    "            self.submod.I0.value**2\n",
+    "        )  # by attribute of submod (allowed but may indicate inefficient code)\n",
     "\n",
     "        # Modifying values of Param objects\n",
-    "        x = 3.0 # locally modify param value (allowed)\n",
-    "        total += x ** 2 # use modified value, will not change the param value globally\n",
-    "        total += self.submod.brightness(0,0, sigma=2.0) # call module with modified param value, only affects this call (allowed)\n",
-    "        self.x.value = 4.0 # modify param value globally (explicitly forbidden)\n",
+    "        x = 3.0  # locally modify param value (allowed)\n",
+    "        total += x**2  # use modified value, will not change the param value globally\n",
+    "        total += self.submod.brightness(\n",
+    "            0, 0, sigma=2.0\n",
+    "        )  # call module with modified param value, only affects this call (allowed)\n",
+    "        self.x.value = 4.0  # modify param value globally (explicitly forbidden)\n",
     "        return total\n",
-    "    \n",
+    "\n",
+    "\n",
     "G = Gaussian(\"G\", x0=5, y0=5, q=0.5, phi=0.0, sigma=1.0, I0=1.0)\n",
     "T = TryParam(G)\n",
     "\n",
@@ -120,10 +126,10 @@
     "print(\"x:\", T.x.value)\n",
     "# If a Param is a pointer, and you access the `value` it will try to evaluate the pointer\n",
     "G.sigma = T.x\n",
-    "print(\"sigma:\", G.sigma.value) # Basic pointer to another Param\n",
+    "print(\"sigma:\", G.sigma.value)  # Basic pointer to another Param\n",
     "G.sigma = lambda p: p.x.value * 2.0\n",
     "G.sigma.link(T.x)\n",
-    "print(\"sigma:\", G.sigma.value) # Function pointer"
+    "print(\"sigma:\", G.sigma.value)  # Function pointer"
    ]
   },
   {
@@ -149,35 +155,36 @@
     "display(C.graphviz())\n",
     "\n",
     "# Set individual param to dynamic\n",
-    "G1.x0.to_dynamic() # call function to set dynamic\n",
-    "G1.q = None # set to None to make dynamic\n",
-    "C.to_dynamic() # only sets immediate children to dynamic\n",
+    "G1.x0.to_dynamic()  # call function to set dynamic\n",
+    "G1.q = None  # set to None to make fully dynamic\n",
+    "G1.q.dynamic_value(0.5)  # set with dynamic value\n",
+    "C.to_dynamic()  # only sets immediate children to dynamic\n",
     "print(\"Individual params can be set to dynamic\")\n",
     "display(C.graphviz())\n",
     "\n",
     "# Set all simulator params to be dynamic\n",
-    "C.to_dynamic(local_only=False)\n",
+    "C.to_dynamic(children_only=False)\n",
     "print(\"All params for the entire simulator may be set to dynamic\")\n",
     "display(C.graphviz())\n",
     "\n",
     "# Even when set to dynamic, the params remember their original values\n",
     "print(\"x0:\", G1.x0.value)\n",
-    "G1.x0 = G1.x0.value # Setting value sets to static\n",
-    "G1.q.to_static() # Setting to static, uses the earlier value\n",
+    "G1.x0 = G1.x0.value  # Setting value sets to static\n",
+    "G1.q.to_static()  # Setting to static, uses the earlier value\n",
     "\n",
-    "# Setting any value will make it static\n",
-    "G1.I0 = 10.0 \n",
+    "# Setting a value and make it static\n",
+    "G1.I0.static_value(10.0)\n",
     "print(\"Individual params can be set to static\")\n",
     "display(C.graphviz())\n",
     "\n",
     "# Similarly a whole simulator can be set static\n",
-    "C.to_static(local_only=False)\n",
+    "C.to_static(children_only=False)\n",
     "print(\"All params for the entire simulator may be set to static\")\n",
     "display(C.graphviz())\n",
     "\n",
     "# Use a param list to set multiple params to dynamic\n",
     "paramset1 = ck.NodeList([G1.x0, G1.q, G2.phi, G2.sigma])\n",
-    "paramset1.to_dynamic() # set all params in the list to dynamic\n",
+    "paramset1.to_dynamic()  # set all params in the list to dynamic\n",
     "print(\"Use a NodeList to curate which params are set to dynamic/static\")\n",
     "display(C.graphviz())\n",
     "\n",
@@ -215,16 +222,17 @@
     "\n",
     "    @ck.forward\n",
     "    def test_modify(self):\n",
-    "        init = self.submod.brightness(0,0) # call with original param values\n",
-    "        mod = self.submod.brightness(0,0, sigma=self.newval1) # call with modified param value\n",
+    "        init = self.submod.brightness(0, 0)  # call with original param values\n",
+    "        mod = self.submod.brightness(0, 0, sigma=self.newval1)  # call with modified param value\n",
     "        with ck.OverrideParam(self.submod.sigma, self.newval2):\n",
-    "            othermod = self.submod.brightness(0,0) # call with temporarily modified param value\n",
+    "            othermod = self.submod.brightness(0, 0)  # call with temporarily modified param value\n",
     "        assert init != mod\n",
     "        assert init != othermod\n",
     "        assert mod != othermod\n",
     "        print(\"See, they are all different!\")\n",
     "        return init, mod, othermod\n",
-    "    \n",
+    "\n",
+    "\n",
     "G = Gaussian(\"G\", x0=5, y0=5, q=0.5, phi=0.0, sigma=1.0, I0=1.0)\n",
     "T = TryModify(G)\n",
     "print(T.test_modify())"
@@ -245,9 +253,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "G = Gaussian(\"G\", x0=5, y0=5, q=0.5, phi=0.0, sigma=1.0, I0=1.0) # default in cartesian coordinates\n",
-    "r = ck.Param(\"r\", 1.0) # radius\n",
-    "theta = ck.Param(\"theta\", 0.0) # angle\n",
+    "G = Gaussian(\"G\", x0=5, y0=5, q=0.5, phi=0.0, sigma=1.0, I0=1.0)  # default in cartesian coordinates\n",
+    "r = ck.Param(\"r\", 1.0)  # radius\n",
+    "theta = ck.Param(\"theta\", 0.0)  # angle\n",
     "G.x0 = lambda p: p.r.value * torch.cos(p.theta.value)\n",
     "G.x0.link([r, theta])\n",
     "G.y0 = lambda p: p.r.value * torch.sin(p.theta.value)\n",
@@ -285,7 +293,7 @@
     "G.y0.link([r, theta])\n",
     "\n",
     "# Run the \"MCMC\"\n",
-    "G.save_state(\"gauss_chain.h5\", appendable=True) # save the initial state\n",
+    "G.save_state(\"gauss_chain.h5\", appendable=True)  # save the initial state\n",
     "\n",
     "# Pretend to run a sampling chain\n",
     "for _ in range(100):\n",
@@ -296,7 +304,7 @@
     "    G.sigma.value += np.random.normal(0.1, 0.05)\n",
     "    G.I0.value += np.random.normal(0.01, 0.5)\n",
     "\n",
-    "    G.append_state(\"gauss_chain.h5\") # append the new state"
+    "    G.append_state(\"gauss_chain.h5\")  # append the new state"
    ]
   },
   {
@@ -311,14 +319,14 @@
    "source": [
     "# Now we can read the chain back in\n",
     "fig, axarr = plt.subplots(6, 6, figsize=(12, 12))\n",
-    "with h5py.File(\"gauss_chain.h5\", \"r\") as f: # Load the hdf5 file directly\n",
-    "    print(\"Check value of test_save: \", f[\"G\"].attrs[\"test_save\"]) # access saved attributes\n",
+    "with h5py.File(\"gauss_chain.h5\", \"r\") as f:  # Load the hdf5 file directly\n",
+    "    print(\"Check value of test_save: \", f[\"G\"].attrs[\"test_save\"])  # access saved attributes\n",
     "    for i, ikey in enumerate([\"x0\", \"y0\", \"q\", \"phi\", \"sigma\", \"I0\"]):\n",
-    "        idata = f[\"G\"][ikey][\"value\"] # access values for a given param\n",
+    "        idata = f[\"G\"][ikey][\"value\"]  # access values for a given param\n",
     "        for j, jkey in enumerate([\"x0\", \"y0\", \"q\", \"phi\", \"sigma\", \"I0\"]):\n",
-    "            jdata = f[\"G\"][jkey][\"value\"] # access values for a given param\n",
+    "            jdata = f[\"G\"][jkey][\"value\"]  # access values for a given param\n",
     "            if i < j:\n",
-    "                axarr[i,j].axis(\"off\")\n",
+    "                axarr[i, j].axis(\"off\")\n",
     "                continue\n",
     "            elif i == j:\n",
     "                axarr[i, j].hist(idata, bins=50, color=\"k\")\n",
@@ -344,10 +352,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "G.load_state(\"gauss_chain.h5\", 32) # Load the 32nd state from the chain\n",
+    "G.load_state(\"gauss_chain.h5\", 32)  # Load the 32nd state from the chain\n",
     "\n",
     "print(\"Loaded state 32:\")\n",
-    "print(f\"x0: {G.x0.value.item():.2f}\") \n",
+    "print(f\"x0: {G.x0.value.item():.2f}\")\n",
     "print(f\"y0: {G.y0.value.item():.2f}\")\n",
     "print(f\"q: {G.q.value.item():.2f}\")\n",
     "print(f\"phi: {G.phi.value.item():.2f}\")\n",
@@ -361,7 +369,7 @@
    "source": [
     "## Add meta data to a Param or Module\n",
     "\n",
-    "Sometimes it is very useful to carry along some extra data right next to your params. For example, you may want to keep track of the uncertainty of a param value. The best way to do this is by tacking on attributes to the `meta` container in a `Param`. This is essentially an empty class which you may then build on however you like. Anything you do to this object is guaranteed not to interfere with `caskade` stuff. Similarly, making attributes with the `meta_` prefix is guaranteed not to interfere with `caskade` stuff."
+    "Sometimes it is very useful to carry along some extra data right next to your params. For example, you may want to keep track of the uncertainty of a param value. Since params and modules are objects, you can add attributes how you like! Just don't override an existing attribute or you might cause chaos."
    ]
   },
   {
@@ -370,11 +378,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "p = ck.Param(\"p\", 1.0) \n",
+    "p = ck.Param(\"p\", 1.0)\n",
     "\n",
-    "p.meta.extra_info = 42 # add attribute to meta container (preferred)\n",
-    "p.meta_extra_info = 42 # add attribute with \"meta_\" prefix (allowed)\n",
-    "p.extra_info = 42 # add attribute directly to Param object (allowed but discouraged due to potential conflicts)"
+    "p.extra_info = 42"
    ]
   },
   {
@@ -391,13 +397,14 @@
    "outputs": [],
    "source": [
     "class ParamU(ck.Param):\n",
-    "    def __init__(self, *args, uncertainty = None, **kwargs):\n",
+    "    def __init__(self, *args, uncertainty=None, **kwargs):\n",
     "        super().__init__(*args, **kwargs)\n",
     "        if uncertainty is None:\n",
     "            self.uncertainty = torch.zeros_like(self.value)\n",
     "        else:\n",
     "            self.uncertainty = uncertainty\n",
     "\n",
+    "\n",
     "p = ParamU(\"p\", 1.0)\n",
     "print(f\"p: {p.value} +- {p.uncertainty}\")\n",
     "p2 = ParamU(\"p2\", 2.0, uncertainty=0.1)\n",
@@ -420,13 +427,13 @@
    "outputs": [],
    "source": [
     "# This is the param we plan to use\n",
-    "x = ck.Param(\"x\", torch.arange(10)) # param has 10 elements\n",
+    "x = ck.Param(\"x\", torch.arange(10))  # param has 10 elements\n",
     "print(\"Original x tensor\", x.value)\n",
     "\n",
     "# These are sub params for the broken primary param\n",
-    "x_dynamic = ck.Param(\"x_dynamic\", torch.arange(3)) # want first three elements to be dynamic\n",
+    "x_dynamic = ck.Param(\"x_dynamic\", torch.arange(3))  # want first three elements to be dynamic\n",
     "x_dynamic.to_dynamic()\n",
-    "x_static = ck.Param(\"x_static\", torch.arange(3,10)) # want last seven elements to be static\n",
+    "x_static = ck.Param(\"x_static\", torch.arange(3, 10))  # want last seven elements to be static\n",
     "\n",
     "# This rebuilds the full param from the broken params\n",
     "x.value = lambda p: torch.cat((p.x_dynamic.value, p.x_static.value))\n",
@@ -465,10 +472,10 @@
     "G = Gaussian(\"G\", x0=5, y0=5, q=0.5, phi=0.0, sigma=1.0, I0=1.0)\n",
     "G.sigma.to_dynamic()\n",
     "G.phi.to_dynamic()\n",
-    "x, y = torch.meshgrid(torch.linspace(0,10,100), torch.linspace(0,10,100), indexing=\"ij\")\n",
+    "x, y = torch.meshgrid(torch.linspace(0, 10, 100), torch.linspace(0, 10, 100), indexing=\"ij\")\n",
     "\n",
     "# Batching using vmap                phi                            sigma\n",
-    "params = torch.stack((torch.linspace(0.0, 3.14/2, 5), torch.linspace(0.5, 4.0, 5)), dim=-1)\n",
+    "params = torch.stack((torch.linspace(0.0, 3.14 / 2, 5), torch.linspace(0.5, 4.0, 5)), dim=-1)\n",
     "img = torch.vmap(G.brightness, in_dims=(None, None, 0), out_dims=0)(x, y, params)\n",
     "fig, axarr = plt.subplots(1, 5, figsize=(20, 4))\n",
     "for i, ax in enumerate(axarr):\n",
@@ -479,7 +486,9 @@
     "# Multiple batching with vmap\n",
     "# imagine the brightness function could only take a single value, rather than a grid\n",
     "#                                            batch x y                        batch params\n",
-    "img = torch.vmap(torch.vmap(G.brightness, in_dims=(0,0,None)), in_dims=(None, None, 0))(x.flatten(), y.flatten(), params)\n",
+    "img = torch.vmap(torch.vmap(G.brightness, in_dims=(0, 0, None)), in_dims=(None, None, 0))(\n",
+    "    x.flatten(), y.flatten(), params\n",
+    ")\n",
     "img = img.reshape(5, *x.shape)\n",
     "fig, axarr = plt.subplots(1, 5, figsize=(20, 4))\n",
     "for i, ax in enumerate(axarr):\n",
@@ -506,12 +515,12 @@
     "class GaussianBatched(ck.Module):\n",
     "    def __init__(self, name, x0=None, y0=None, q=None, phi=None, sigma=None, I0=None):\n",
     "        super().__init__(name)\n",
-    "        self.x0 = ck.Param(\"x0\", x0) # position\n",
+    "        self.x0 = ck.Param(\"x0\", x0)  # position\n",
     "        self.y0 = ck.Param(\"y0\", y0)\n",
-    "        self.q = ck.Param(\"q\", q) # axis ratio\n",
-    "        self.phi = ck.Param(\"phi\", phi) # orientation\n",
-    "        self.sigma = ck.Param(\"sigma\", sigma) # width\n",
-    "        self.I0 = ck.Param(\"I0\", I0) # intensity\n",
+    "        self.q = ck.Param(\"q\", q)  # axis ratio\n",
+    "        self.phi = ck.Param(\"phi\", phi)  # orientation\n",
+    "        self.sigma = ck.Param(\"sigma\", sigma)  # width\n",
+    "        self.I0 = ck.Param(\"I0\", I0)  # intensity\n",
     "\n",
     "    @ck.forward\n",
     "    def _r(self, x, y, x0=None, y0=None, q=None, phi=None):\n",
@@ -522,25 +531,28 @@
     "        x, y = x - x0, y - y0\n",
     "        s, c = torch.sin(phi), torch.cos(phi)\n",
     "        x, y = c * x - s * y, s * x + c * y\n",
-    "        return (x ** 2 + (y * q) ** 2).sqrt()\n",
-    "    \n",
+    "        return (x**2 + (y * q) ** 2).sqrt()\n",
+    "\n",
     "    @ck.forward\n",
     "    def brightness(self, x, y, sigma=None, I0=None):\n",
     "        init_shape = x.shape\n",
     "        B, *_ = sigma.shape\n",
     "        x = x.flatten()\n",
     "        y = y.flatten()\n",
-    "        return (I0.unsqueeze(-1) * (-self._r(x, y)**2 / sigma.unsqueeze(-1)**2).exp()).reshape(B, *init_shape)\n",
-    "    \n",
+    "        return (I0.unsqueeze(-1) * (-self._r(x, y) ** 2 / sigma.unsqueeze(-1) ** 2).exp()).reshape(\n",
+    "            B, *init_shape\n",
+    "        )\n",
+    "\n",
+    "\n",
     "G = GaussianBatched(\"G\", x0=[5], y0=[5], q=[0.5], phi=[0.0], sigma=[1.0], I0=[1.0])\n",
-    "G.to_dynamic() # all params are dynamic\n",
-    "x, y = torch.meshgrid(torch.linspace(0,10,100), torch.linspace(0,10,100), indexing=\"ij\")\n",
+    "G.to_dynamic()  # all params are dynamic\n",
+    "x, y = torch.meshgrid(torch.linspace(0, 10, 100), torch.linspace(0, 10, 100), indexing=\"ij\")\n",
     "\n",
     "# Batching on all dims using batched tensor input\n",
     "params = G.build_params_array()\n",
-    "params = params.repeat(5, 1) # 5 copies of the same params\n",
-    "params[:,3] = torch.linspace(0.0, 3.14/2, 5) # phi\n",
-    "params[:,4] = torch.linspace(0.5, 4.0, 5) # sigma\n",
+    "params = params.repeat(5, 1)  # 5 copies of the same params\n",
+    "params[:, 3] = torch.linspace(0.0, 3.14 / 2, 5)  # phi\n",
+    "params[:, 4] = torch.linspace(0.5, 4.0, 5)  # sigma\n",
     "img = G.brightness(x, y, params=params)\n",
     "fig, axarr = plt.subplots(1, 5, figsize=(20, 4))\n",
     "for i, ax in enumerate(axarr):\n",
@@ -550,8 +562,9 @@
     "\n",
     "# Batching by setting shapes of params, then flat tensor input\n",
     "for param in G.dynamic_params:\n",
-    "    param.shape = (5,) + param.shape # add batch dimension to shape\n",
-    "params = params.T.flatten() # now params is a flat tensor again\n",
+    "    param.value = None\n",
+    "    param.shape = (5,) + param.shape  # add batch dimension to shape\n",
+    "params = params.T.flatten()  # now params is a flat tensor again\n",
     "img = G.brightness(x, y, params=params)\n",
     "fig, axarr = plt.subplots(1, 5, figsize=(20, 4))\n",
     "for i, ax in enumerate(axarr):\n",
@@ -561,12 +574,12 @@
     "\n",
     "# Batching using list input, note that list allows for different shapes, (also true for dictionary params)\n",
     "params = [\n",
-    "    torch.tensor(5), # x0\n",
-    "    torch.tensor(5), # y0\n",
-    "    torch.tensor(0.5), # q\n",
-    "    torch.linspace(0.0, 3.14/2, 5), # phi, batched\n",
-    "    torch.linspace(0.5, 4.0, 5), # sigma, batched\n",
-    "    torch.tensor(1.0) # I0\n",
+    "    torch.tensor(5),  # x0\n",
+    "    torch.tensor(5),  # y0\n",
+    "    torch.tensor(0.5),  # q\n",
+    "    torch.linspace(0.0, 3.14 / 2, 5),  # phi, batched\n",
+    "    torch.linspace(0.5, 4.0, 5),  # sigma, batched\n",
+    "    torch.tensor(1.0),  # I0\n",
     "]\n",
     "img = G.brightness(x, y, params=params)\n",
     "fig, axarr = plt.subplots(1, 5, figsize=(20, 4))\n",
@@ -595,7 +608,7 @@
     "G2 = Gaussian(\"G2\", x0=5, y0=5, q=0.5, phi=0.0, sigma=1.0, I0=1.0)\n",
     "C = Combined(\"C\", G1, G2)\n",
     "\n",
-    "del G2.x0 # remove a param from a module\n",
+    "del G2.x0  # remove a param from a module\n",
     "\n",
     "C.graphviz()"
    ]
@@ -606,7 +619,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "G2.x0 = G1.x0 # assign a param from one module to another\n",
+    "G2.x0 = G1.x0  # assign a param from one module to another\n",
     "C.graphviz()"
    ]
   },
@@ -641,11 +654,13 @@
     "        total += self.x.value\n",
     "        print(f\"second call took {time()-start:.5f} sec\")\n",
     "        return total\n",
-    "    \n",
+    "\n",
+    "\n",
     "def long_function(p):\n",
     "    sleep(2)\n",
     "    return 1.0 + p.y.value\n",
     "\n",
+    "\n",
     "T = TryCallPointer()\n",
     "T.x = long_function\n",
     "T.x.link(T.y)\n",
@@ -670,7 +685,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "PY39",
+   "display_name": "PY312 (3.12.3)",
    "language": "python",
    "name": "python3"
   },
@@ -684,7 +699,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/BeginnersGuide.ipynb b/docs/source/notebooks/BeginnersGuide.ipynb
index 4b8244c..645fbb7 100644
--- a/docs/source/notebooks/BeginnersGuide.ipynb
+++ b/docs/source/notebooks/BeginnersGuide.ipynb
@@ -49,22 +49,22 @@
     "class Gaussian(ck.Module):\n",
     "    def __init__(self, name, x0=None, q=None, phi=None, sigma=None, I0=None):\n",
     "        super().__init__(name)\n",
-    "        self.x0 = ck.Param(\"x0\", x0, shape=(2,)) # position\n",
-    "        self.q = ck.Param(\"q\", q) # axis ratio\n",
-    "        self.phi = ck.Param(\"phi\", phi) # orientation\n",
-    "        self.sigma = ck.Param(\"sigma\", sigma) # width\n",
-    "        self.I0 = ck.Param(\"I0\", I0) # intensity\n",
+    "        self.x0 = ck.Param(\"x0\", x0, shape=(2,))  # position\n",
+    "        self.q = ck.Param(\"q\", q)  # axis ratio\n",
+    "        self.phi = ck.Param(\"phi\", phi)  # orientation\n",
+    "        self.sigma = ck.Param(\"sigma\", sigma)  # width\n",
+    "        self.I0 = ck.Param(\"I0\", I0)  # intensity\n",
     "\n",
     "    @ck.forward\n",
     "    def _r(self, x, y, x0=None, q=None, phi=None):\n",
-    "        x, y = x - x0[...,0], y - x0[...,1]\n",
+    "        x, y = x - x0[..., 0], y - x0[..., 1]\n",
     "        s, c = torch.sin(phi), torch.cos(phi)\n",
     "        x, y = c * x - s * y, s * x + c * y\n",
-    "        return (x ** 2 + (y * q) ** 2).sqrt()\n",
-    "    \n",
+    "        return (x**2 + (y * q) ** 2).sqrt()\n",
+    "\n",
     "    @ck.forward\n",
     "    def brightness(self, x, y, sigma=None, I0=None):\n",
-    "        return I0 * (-self._r(x, y)**2 / sigma**2).exp()"
+    "        return I0 * (-self._r(x, y) ** 2 / sigma**2).exp()"
    ]
   },
   {
@@ -80,9 +80,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "firstsim = Gaussian(\"my first module\", sigma = 0.2, I0 = 1.0)\n",
-    "print(firstsim) # print the graph\n",
-    "firstsim.graphviz() # show the graph"
+    "firstsim = Gaussian(\"my first module\", sigma=0.2, I0=1.0)\n",
+    "print(firstsim)  # print the graph\n",
+    "firstsim.graphviz()  # show the graph"
    ]
   },
   {
@@ -100,16 +100,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "secondsim = Gaussian(\"my second module\", x0=(0,0), q=0.5, phi=3.14/3, sigma=0.2, I0=1.0)\n",
+    "secondsim = Gaussian(\"my second module\", x0=(0, 0), q=0.5, phi=3.14 / 3, sigma=0.2, I0=1.0)\n",
     "x, y = torch.meshgrid(torch.linspace(-1, 1, 100), torch.linspace(-1, 1, 100), indexing=\"ij\")\n",
-    "secondsim.to_dynamic() # all params owned by secondsim are now dynamic\n",
-    "secondsim.sigma.to_static() # sigma is now static\n",
-    "secondsim.I0.to_static() # I0 is now static\n",
-    "params = secondsim.build_params_array() # automatically build a tensor for the dynamic params\n",
+    "secondsim.to_dynamic()  # all params owned by secondsim are now dynamic\n",
+    "secondsim.sigma.to_static()  # sigma is now static\n",
+    "secondsim.I0.to_static()  # I0 is now static\n",
+    "params = secondsim.build_params_array()  # automatically build a tensor for the dynamic params\n",
     "plt.imshow(secondsim.brightness(x, y, params), origin=\"lower\")\n",
     "plt.axis(\"off\")\n",
     "plt.show()\n",
-    "secondsim.graphviz() # show the graph"
+    "secondsim.graphviz()  # show the graph"
    ]
   },
   {
@@ -181,10 +181,10 @@
     "ax[2].imshow(secondsim.brightness(x, y), origin=\"lower\")\n",
     "ax[2].axis(\"off\")\n",
     "ax[2].set_title(\"Static parameters\")\n",
-    "# Set them back to dynamic by setting them to None (works the same as `to_dynamic`)\n",
-    "secondsim.x0 = None\n",
-    "secondsim.q = None\n",
-    "secondsim.phi = None\n",
+    "# Set them back to dynamic\n",
+    "secondsim.x0.to_dynamic()\n",
+    "secondsim.q.to_dynamic()\n",
+    "secondsim.phi.to_dynamic()\n",
     "plt.show()"
    ]
   },
@@ -207,8 +207,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "thirdsim = Gaussian(\"my third module\", phi = 3.14*5/6, q = 0.2, sigma = 0.2, I0 = 0.5)\n",
-    "thirdsim.x0 = secondsim.x0 # now they share the same position"
+    "thirdsim = Gaussian(\"my third module\", phi=3.14 * 5 / 6, q=0.2, sigma=0.2, I0=0.5)\n",
+    "thirdsim.x0 = secondsim.x0  # now they share the same position"
    ]
   },
   {
@@ -245,7 +245,7 @@
     "class Combined(ck.Module):\n",
     "    def __init__(self, name, first, second):\n",
     "        super().__init__(name)\n",
-    "        self.first = first # Modules are automatically registered\n",
+    "        self.first = first  # Modules are automatically registered\n",
     "        self.second = second\n",
     "\n",
     "    @ck.forward\n",
@@ -271,7 +271,7 @@
    "outputs": [],
    "source": [
     "# same params as before since secondsim is all static or pointers to firstsim\n",
-    "plt.imshow(combinedsim.brightness(x, y, params_list), origin=\"lower\")\n",
+    "plt.imshow(combinedsim.brightness(x, y, combinedsim.build_params_array()), origin=\"lower\")\n",
     "plt.axis(\"off\")\n",
     "plt.title(\"Combined brightness\")\n",
     "plt.show()"
@@ -292,14 +292,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "simtime = ck.Param(\"time\") # create a parameter for time\n",
-    "secondsim.x0 = lambda p: (-p.time.value +0.5)*torch.tensor((1,-1))\n",
+    "simtime = ck.Param(\"time\")  # create a parameter for time\n",
+    "secondsim.x0 = lambda p: (-p.time.value + 0.5) * torch.tensor((1, -1))\n",
     "secondsim.x0.link(simtime)\n",
-    "thirdsim.x0 = lambda p: p.time.value*torch.tensor((1,1)) - 0.5\n",
+    "thirdsim.x0 = lambda p: p.time.value * torch.tensor((1, 1)) - 0.5\n",
     "thirdsim.x0.link(simtime)\n",
     "\n",
-    "secondsim.q = 0.5\n",
-    "secondsim.phi = 3.14 / 3\n",
+    "# Use `static_value` to set the value and set to static\n",
+    "# Similarly use `dynamic_value` to set value and set dynamic\n",
+    "secondsim.q.static_value(0.5)\n",
+    "secondsim.phi.static_value(3.14 / 3)\n",
     "\n",
     "combinedsim.graphviz()"
    ]
@@ -319,11 +321,13 @@
     "img = ax.imshow(combinedsim.brightness(x, y, torch.tensor([0.0])), origin=\"lower\", vmin=0, vmax=1.5)\n",
     "ax.set_title(\"Brightness at time 0\")\n",
     "\n",
+    "\n",
     "def update(i):\n",
     "    img.set_data(combinedsim.brightness(x, y, torch.tensor([i / B])))\n",
     "    ax.set_title(f\"Brightness at time {i / B:.2f}\")\n",
     "    return img\n",
     "\n",
+    "\n",
     "ani = animation.FuncAnimation(fig, update, frames=B, interval=60)\n",
     "\n",
     "plt.close()\n",
@@ -347,7 +351,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "batched_params_tensor = torch.linspace(0, 1, 64).reshape(64, 1) # only 1 param \"time\" so last dim is 1\n",
+    "batched_params_tensor = torch.linspace(0, 1, 64).reshape(\n",
+    "    64, 1\n",
+    ")  # only 1 param \"time\" so last dim is 1\n",
     "\n",
     "start = time()\n",
     "result = []\n",
@@ -395,7 +401,11 @@
    "source": [
     "# using PyTorch autograd\n",
     "params_tensor = torch.tensor([0.5])\n",
-    "plt.imshow(torch.func.jacfwd(combinedsim.brightness,argnums=2)(x, y, params_tensor), origin=\"lower\", cmap=\"seismic\")\n",
+    "plt.imshow(\n",
+    "    torch.func.jacfwd(combinedsim.brightness, argnums=2)(x, y, params_tensor),\n",
+    "    origin=\"lower\",\n",
+    "    cmap=\"seismic\",\n",
+    ")\n",
     "plt.axis(\"off\")\n",
     "plt.title(\"gradient of brightness at t=0.5\")\n",
     "plt.show()"
@@ -405,9 +415,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Use `caskade` with numpy, jax, or general python objects\n",
+    "## Use `caskade` with numpy, JAX, or PyTorch\n",
     "\n",
-    "It is possible to use `caskade` with other array like types like numpy and jax. You'll need to set the backend for `caskade` to run things properly. Ideally you should set the environment variable `CASKADE_BACKEND` and then `caskade` will run everything with your desired backend. The options are `torch`, `numpy`, `jax`, and `object`. The `object` option is a bit special, it will not be able to take advantage of array operations (such as constructing the flattened array input) but other options should work (i.e. a list of objects, one for each param). If you have a linux system running bash you can do:\n",
+    "It is possible to use `caskade` with other array like types like numpy and jax. You'll need to set the backend for `caskade` to run things properly. Ideally you should set the environment variable `CASKADE_BACKEND` and then `caskade` will run everything with your desired backend. The options are `torch`, `numpy`, and `jax`. If you have a linux system running bash you can do:\n",
     "```bash\n",
     "export CASKADE_BACKEND=\"numpy\"\n",
     "```\n",
@@ -430,11 +440,6 @@
     "p = ck.Param(\"p\", 1.0)\n",
     "print(\"with jax backend, p type:\", type(p.value))\n",
     "\n",
-    "# object backend\n",
-    "ck.backend.backend = \"object\"\n",
-    "p = ck.Param(\"p\", 1.0)\n",
-    "print(\"with object backend, p type:\", type(p.value))\n",
-    "\n",
     "# torch backend\n",
     "ck.backend.backend = \"torch\"\n",
     "p = ck.Param(\"p\", 1.0)\n",
@@ -445,7 +450,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "And we're done! Those are all the elemental abilities of `caskade`, I hope that by this point you have a sense of the vast possibilities of simulators that can be constructed. This is only the tip of the iceberg for `caskade`, check out the advanced tutorial for much more information about constructing simulators!\n",
+    "And we're done! Those are all the elemental abilities of `caskade`, I hope that by this point you have a sense of the vast possibilities of simulators that can be constructed. This is only the tip of the iceberg for `caskade`, check out the advanced tutorial for much more information about constructing simulators! Or check out [caustics](https://caustics.readthedocs.io/) to see `caskade` in action!\n",
     "\n",
     "\n",
     "Happy science-ing!"
@@ -459,7 +464,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "PY39",
+   "display_name": "PY312 (3.12.3)",
    "language": "python",
    "name": "python3"
   },
@@ -473,7 +478,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/WorkedExample.ipynb b/docs/source/notebooks/WorkedExample.ipynb
index 7f0a3b1..119948b 100644
--- a/docs/source/notebooks/WorkedExample.ipynb
+++ b/docs/source/notebooks/WorkedExample.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "fd48005c",
    "metadata": {},
    "outputs": [],
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "25d15e01",
    "metadata": {
     "tags": [
@@ -52,48 +52,51 @@
     "class Gaussian(ck.Module):\n",
     "    def __init__(self, name, x0=None, y0=None, q=None, phi=None, sigma=None, flux=None):\n",
     "        super().__init__(name)\n",
-    "        self.x0 = ck.Param(\"x0\", x0) # position\n",
+    "        self.x0 = ck.Param(\"x0\", x0)  # position\n",
     "        self.y0 = ck.Param(\"y0\", y0)\n",
-    "        self.q = ck.Param(\"q\", q) # axis ratio\n",
-    "        self.phi = ck.Param(\"phi\", phi) # orientation\n",
-    "        self.sigma = ck.Param(\"sigma\", sigma) # width\n",
-    "        self.flux = ck.Param(\"flux\", flux) # total light\n",
+    "        self.q = ck.Param(\"q\", q)  # axis ratio\n",
+    "        self.phi = ck.Param(\"phi\", phi)  # orientation\n",
+    "        self.sigma = ck.Param(\"sigma\", sigma)  # width\n",
+    "        self.flux = ck.Param(\"flux\", flux)  # total light\n",
     "\n",
     "    @ck.forward\n",
     "    def _r(self, x, y, x0=None, y0=None, q=None, phi=None):\n",
     "        x, y = x - x0, y - y0\n",
     "        s, c = torch.sin(phi), torch.cos(phi)\n",
     "        x, y = c * x - s * y, s * x + c * y\n",
-    "        return (x ** 2 + (y * q) ** 2).sqrt()\n",
-    "    \n",
+    "        return (x**2 + (y * q) ** 2).sqrt()\n",
+    "\n",
     "    @ck.forward\n",
     "    def brightness(self, x, y, sigma=None, flux=None):\n",
-    "        return flux * (-self._r(x, y)**2 / sigma**2).exp() / (2 * torch.pi * sigma**2).sqrt()\n",
-    "    \n",
+    "        return flux * (-self._r(x, y) ** 2 / sigma**2).exp() / (2 * torch.pi * sigma**2).sqrt()\n",
+    "\n",
+    "\n",
     "class Gaussian1D(ck.Module):\n",
     "    def __init__(self, name, t0=None, sigma=None, peak_flux=None):\n",
     "        super().__init__(name)\n",
-    "        self.t0 = ck.Param(\"t0\", t0) # position\n",
-    "        self.sigma = ck.Param(\"sigma\", sigma) # width\n",
-    "        self.peak_flux = ck.Param(\"peak_flux\", peak_flux) # intensity\n",
+    "        self.t0 = ck.Param(\"t0\", t0)  # position\n",
+    "        self.sigma = ck.Param(\"sigma\", sigma)  # width\n",
+    "        self.peak_flux = ck.Param(\"peak_flux\", peak_flux)  # intensity\n",
     "\n",
     "    @ck.forward\n",
     "    def flux(self, t, peak_flux, t0, sigma):\n",
-    "        return peak_flux * (-((t + t0) / sigma)**2).exp()\n",
-    "    \n",
+    "        return peak_flux * (-(((t + t0) / sigma) ** 2)).exp()\n",
+    "\n",
+    "\n",
     "class Combined(ck.Module):\n",
     "    def __init__(self, name, x, y, models):\n",
     "        super().__init__(name)\n",
     "        self.x = x\n",
     "        self.y = y\n",
-    "        self.models = models \n",
+    "        self.models = models\n",
     "\n",
     "    @ck.forward\n",
     "    def __call__(self):\n",
     "        return sum(model.brightness(self.x, self.y) for model in self.models)\n",
-    "    \n",
+    "\n",
+    "\n",
     "class Noiser(ck.Module):\n",
-    "    def __init__(self, name, model, read_noise=0.1, exp_time = 100):\n",
+    "    def __init__(self, name, model, read_noise=0.1, exp_time=100):\n",
     "        super().__init__(name)\n",
     "        self.model = model\n",
     "        self.read_noise = read_noise\n",
@@ -103,7 +106,7 @@
     "    def __call__(self):\n",
     "        img = self.model()\n",
     "        read_noise = torch.randn_like(img) * self.read_noise\n",
-    "        poisson_noise = torch.randn_like(img) * (img*self.exp_time).sqrt() / self.exp_time\n",
+    "        poisson_noise = torch.randn_like(img) * (img * self.exp_time).sqrt() / self.exp_time\n",
     "        return img + read_noise + poisson_noise"
    ]
   },
@@ -119,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "1323268d",
    "metadata": {},
    "outputs": [],
@@ -128,9 +131,11 @@
     "imgsize = 50\n",
     "sigma_read = 0.1\n",
     "exp_time = 25\n",
-    "imgx, imgy = torch.meshgrid(torch.linspace(-1,1, imgsize), torch.linspace(-1,1, imgsize), indexing='ij')\n",
+    "imgx, imgy = torch.meshgrid(\n",
+    "    torch.linspace(-1, 1, imgsize), torch.linspace(-1, 1, imgsize), indexing=\"ij\"\n",
+    ")\n",
     "SN = Gaussian(\"SN\", x0=-0.35, y0=-0.2, q=1.0, phi=0.0, sigma=0.05)\n",
-    "SN_lightcurve = Gaussian1D(\"lightcurve\", t0=-3.0, sigma=2., peak_flux=0.25)\n",
+    "SN_lightcurve = Gaussian1D(\"lightcurve\", t0=-3.0, sigma=2.0, peak_flux=0.25)\n",
     "time = ck.Param(\"time\")\n",
     "SN.flux = lambda p: p.lightcurve.flux(p.time.value)\n",
     "SN.flux.link((SN_lightcurve, time))\n",
@@ -140,17301 +145,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "e91da26b",
    "metadata": {
     "tags": [
      "hide-input"
     ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<link rel=\"stylesheet\"\n",
-       "href=\"https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css\">\n",
-       "<script language=\"javascript\">\n",
-       "  function isInternetExplorer() {\n",
-       "    ua = navigator.userAgent;\n",
-       "    /* MSIE used to detect old browsers and Trident used to newer ones*/\n",
-       "    return ua.indexOf(\"MSIE \") > -1 || ua.indexOf(\"Trident/\") > -1;\n",
-       "  }\n",
-       "\n",
-       "  /* Define the Animation class */\n",
-       "  function Animation(frames, img_id, slider_id, interval, loop_select_id){\n",
-       "    this.img_id = img_id;\n",
-       "    this.slider_id = slider_id;\n",
-       "    this.loop_select_id = loop_select_id;\n",
-       "    this.interval = interval;\n",
-       "    this.current_frame = 0;\n",
-       "    this.direction = 0;\n",
-       "    this.timer = null;\n",
-       "    this.frames = new Array(frames.length);\n",
-       "\n",
-       "    for (var i=0; i<frames.length; i++)\n",
-       "    {\n",
-       "     this.frames[i] = new Image();\n",
-       "     this.frames[i].src = frames[i];\n",
-       "    }\n",
-       "    var slider = document.getElementById(this.slider_id);\n",
-       "    slider.max = this.frames.length - 1;\n",
-       "    if (isInternetExplorer()) {\n",
-       "        // switch from oninput to onchange because IE <= 11 does not conform\n",
-       "        // with W3C specification. It ignores oninput and onchange behaves\n",
-       "        // like oninput. In contrast, Microsoft Edge behaves correctly.\n",
-       "        slider.setAttribute('onchange', slider.getAttribute('oninput'));\n",
-       "        slider.setAttribute('oninput', null);\n",
-       "    }\n",
-       "    this.set_frame(this.current_frame);\n",
-       "  }\n",
-       "\n",
-       "  Animation.prototype.get_loop_state = function(){\n",
-       "    var button_group = document[this.loop_select_id].state;\n",
-       "    for (var i = 0; i < button_group.length; i++) {\n",
-       "        var button = button_group[i];\n",
-       "        if (button.checked) {\n",
-       "            return button.value;\n",
-       "        }\n",
-       "    }\n",
-       "    return undefined;\n",
-       "  }\n",
-       "\n",
-       "  Animation.prototype.set_frame = function(frame){\n",
-       "    this.current_frame = frame;\n",
-       "    document.getElementById(this.img_id).src =\n",
-       "            this.frames[this.current_frame].src;\n",
-       "    document.getElementById(this.slider_id).value = this.current_frame;\n",
-       "  }\n",
-       "\n",
-       "  Animation.prototype.next_frame = function()\n",
-       "  {\n",
-       "    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n",
-       "  }\n",
-       "\n",
-       "  Animation.prototype.previous_frame = function()\n",
-       "  {\n",
-       "    this.set_frame(Math.max(0, this.current_frame - 1));\n",
-       "  }\n",
-       "\n",
-       "  Animation.prototype.first_frame = function()\n",
-       "  {\n",
-       "    this.set_frame(0);\n",
-       "  }\n",
-       "\n",
-       "  Animation.prototype.last_frame = function()\n",
-       "  {\n",
-       "    this.set_frame(this.frames.length - 1);\n",
-       "  }\n",
-       "\n",
-       "  Animation.prototype.slower = function()\n",
-       "  {\n",
-       "    this.interval /= 0.7;\n",
-       "    if(this.direction > 0){this.play_animation();}\n",
-       "    else if(this.direction < 0){this.reverse_animation();}\n",
-       "  }\n",
-       "\n",
-       "  Animation.prototype.faster = function()\n",
-       "  {\n",
-       "    this.interval *= 0.7;\n",
-       "    if(this.direction > 0){this.play_animation();}\n",
-       "    else if(this.direction < 0){this.reverse_animation();}\n",
-       "  }\n",
-       "\n",
-       "  Animation.prototype.anim_step_forward = function()\n",
-       "  {\n",
-       "    this.current_frame += 1;\n",
-       "    if(this.current_frame < this.frames.length){\n",
-       "      this.set_frame(this.current_frame);\n",
-       "    }else{\n",
-       "      var loop_state = this.get_loop_state();\n",
-       "      if(loop_state == \"loop\"){\n",
-       "        this.first_frame();\n",
-       "      }else if(loop_state == \"reflect\"){\n",
-       "        this.last_frame();\n",
-       "        this.reverse_animation();\n",
-       "      }else{\n",
-       "        this.pause_animation();\n",
-       "        this.last_frame();\n",
-       "      }\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  Animation.prototype.anim_step_reverse = function()\n",
-       "  {\n",
-       "    this.current_frame -= 1;\n",
-       "    if(this.current_frame >= 0){\n",
-       "      this.set_frame(this.current_frame);\n",
-       "    }else{\n",
-       "      var loop_state = this.get_loop_state();\n",
-       "      if(loop_state == \"loop\"){\n",
-       "        this.last_frame();\n",
-       "      }else if(loop_state == \"reflect\"){\n",
-       "        this.first_frame();\n",
-       "        this.play_animation();\n",
-       "      }else{\n",
-       "        this.pause_animation();\n",
-       "        this.first_frame();\n",
-       "      }\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  Animation.prototype.pause_animation = function()\n",
-       "  {\n",
-       "    this.direction = 0;\n",
-       "    if (this.timer){\n",
-       "      clearInterval(this.timer);\n",
-       "      this.timer = null;\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  Animation.prototype.play_animation = function()\n",
-       "  {\n",
-       "    this.pause_animation();\n",
-       "    this.direction = 1;\n",
-       "    var t = this;\n",
-       "    if (!this.timer) this.timer = setInterval(function() {\n",
-       "        t.anim_step_forward();\n",
-       "    }, this.interval);\n",
-       "  }\n",
-       "\n",
-       "  Animation.prototype.reverse_animation = function()\n",
-       "  {\n",
-       "    this.pause_animation();\n",
-       "    this.direction = -1;\n",
-       "    var t = this;\n",
-       "    if (!this.timer) this.timer = setInterval(function() {\n",
-       "        t.anim_step_reverse();\n",
-       "    }, this.interval);\n",
-       "  }\n",
-       "</script>\n",
-       "\n",
-       "<style>\n",
-       ".animation {\n",
-       "    display: inline-block;\n",
-       "    text-align: center;\n",
-       "}\n",
-       "input[type=range].anim-slider {\n",
-       "    width: 374px;\n",
-       "    margin-left: auto;\n",
-       "    margin-right: auto;\n",
-       "}\n",
-       ".anim-buttons {\n",
-       "    margin: 8px 0px;\n",
-       "}\n",
-       ".anim-buttons button {\n",
-       "    padding: 0;\n",
-       "    width: 36px;\n",
-       "}\n",
-       ".anim-state label {\n",
-       "    margin-right: 8px;\n",
-       "}\n",
-       ".anim-state input {\n",
-       "    margin: 0;\n",
-       "    vertical-align: middle;\n",
-       "}\n",
-       "</style>\n",
-       "\n",
-       "<div class=\"animation\">\n",
-       "  <img id=\"_anim_imgb9e3ef1230484e69aa575052c4433cef\">\n",
-       "  <div class=\"anim-controls\">\n",
-       "    <input id=\"_anim_sliderb9e3ef1230484e69aa575052c4433cef\" type=\"range\" class=\"anim-slider\"\n",
-       "           name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\"\n",
-       "           oninput=\"animb9e3ef1230484e69aa575052c4433cef.set_frame(parseInt(this.value));\">\n",
-       "    <div class=\"anim-buttons\">\n",
-       "      <button title=\"Decrease speed\" aria-label=\"Decrease speed\" onclick=\"animb9e3ef1230484e69aa575052c4433cef.slower()\">\n",
-       "          <i class=\"fa fa-minus\"></i></button>\n",
-       "      <button title=\"First frame\" aria-label=\"First frame\" onclick=\"animb9e3ef1230484e69aa575052c4433cef.first_frame()\">\n",
-       "        <i class=\"fa fa-fast-backward\"></i></button>\n",
-       "      <button title=\"Previous frame\" aria-label=\"Previous frame\" onclick=\"animb9e3ef1230484e69aa575052c4433cef.previous_frame()\">\n",
-       "          <i class=\"fa fa-step-backward\"></i></button>\n",
-       "      <button title=\"Play backwards\" aria-label=\"Play backwards\" onclick=\"animb9e3ef1230484e69aa575052c4433cef.reverse_animation()\">\n",
-       "          <i class=\"fa fa-play fa-flip-horizontal\"></i></button>\n",
-       "      <button title=\"Pause\" aria-label=\"Pause\" onclick=\"animb9e3ef1230484e69aa575052c4433cef.pause_animation()\">\n",
-       "          <i class=\"fa fa-pause\"></i></button>\n",
-       "      <button title=\"Play\" aria-label=\"Play\" onclick=\"animb9e3ef1230484e69aa575052c4433cef.play_animation()\">\n",
-       "          <i class=\"fa fa-play\"></i></button>\n",
-       "      <button title=\"Next frame\" aria-label=\"Next frame\" onclick=\"animb9e3ef1230484e69aa575052c4433cef.next_frame()\">\n",
-       "          <i class=\"fa fa-step-forward\"></i></button>\n",
-       "      <button title=\"Last frame\" aria-label=\"Last frame\" onclick=\"animb9e3ef1230484e69aa575052c4433cef.last_frame()\">\n",
-       "          <i class=\"fa fa-fast-forward\"></i></button>\n",
-       "      <button title=\"Increase speed\" aria-label=\"Increase speed\" onclick=\"animb9e3ef1230484e69aa575052c4433cef.faster()\">\n",
-       "          <i class=\"fa fa-plus\"></i></button>\n",
-       "    </div>\n",
-       "    <form title=\"Repetition mode\" aria-label=\"Repetition mode\" action=\"#n\" name=\"_anim_loop_selectb9e3ef1230484e69aa575052c4433cef\"\n",
-       "          class=\"anim-state\">\n",
-       "      <input type=\"radio\" name=\"state\" value=\"once\" id=\"_anim_radio1_b9e3ef1230484e69aa575052c4433cef\"\n",
-       "             >\n",
-       "      <label for=\"_anim_radio1_b9e3ef1230484e69aa575052c4433cef\">Once</label>\n",
-       "      <input type=\"radio\" name=\"state\" value=\"loop\" id=\"_anim_radio2_b9e3ef1230484e69aa575052c4433cef\"\n",
-       "             checked>\n",
-       "      <label for=\"_anim_radio2_b9e3ef1230484e69aa575052c4433cef\">Loop</label>\n",
-       "      <input type=\"radio\" name=\"state\" value=\"reflect\" id=\"_anim_radio3_b9e3ef1230484e69aa575052c4433cef\"\n",
-       "             >\n",
-       "      <label for=\"_anim_radio3_b9e3ef1230484e69aa575052c4433cef\">Reflect</label>\n",
-       "    </form>\n",
-       "  </div>\n",
-       "</div>\n",
-       "\n",
-       "\n",
-       "<script language=\"javascript\">\n",
-       "  /* Instantiate the Animation class. */\n",
-       "  /* The IDs given should match those used in the template above. */\n",
-       "  (function() {\n",
-       "    var img_id = \"_anim_imgb9e3ef1230484e69aa575052c4433cef\";\n",
-       "    var slider_id = \"_anim_sliderb9e3ef1230484e69aa575052c4433cef\";\n",
-       "    var loop_select_id = \"_anim_loop_selectb9e3ef1230484e69aa575052c4433cef\";\n",
-       "    var frames = new Array(64);\n",
-       "    \n",
-       "  frames[0] = \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\\\n",
-       "bGliIHZlcnNpb24zLjkuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8ekN5oAAAACXBIWXMAAA9h\\\n",
-       "AAAPYQGoP6dpAAA6qElEQVR4nO3de3zU1Z3/8fd3cieXCUFN5KZUUFQEKoqkWkWNUldYKVjqrq1A\\\n",
-       "sfaxDa5Ia3+lrbfWitVutbaIdevCdlukhS5q65UHVtytoIilRS1IWywokkiV3CDJZOb8/mCZGsn3\\\n",
-       "88WEZCDn9Xw88njI98z5zpkzQ3znm5k3gXPOCQAAAN6IZXoBAAAA6FkEQAAAAM8QAAEAADxDAAQA\\\n",
-       "APAMARAAAMAzBEAAAADPEAABAAA8QwAEAADwDAEQAADAMwRAAAAAzxAAAQAAPEMABAAA8AwBEAAA\\\n",
-       "wDMEQAAAAM8QAAEAADxDAAQAAPAMARAAAMAzBEAAAADPEAABAAA8QwAEAADwDAEQAADAMwRAAAAA\\\n",
-       "zxAAAQAAPEMABAAA8AwBEAAAwDMEQAAAAM8QAAEAADxDAAQAAPAMARAAAMAzBEAAAADPEAABAAA8\\\n",
-       "QwAEAADwDAEQAADAMwRAAAAAzxAAgV7ujTfeUBAEWrx4cafnfve73z30C4Okrj0/ANBZBEDgMLV4\\\n",
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-       "DNWeAAAAyAA+AwgAAOAZCkAAAADPUAACAAB4hgIQAADAMxSAAAAAnqEABAAA8AwFIAAAgGcoAAEA\\\n",
-       "ADxDAQgAAOAZCkAAAADPUAACAAB4hgIQAADAMxSAAAAAnqEABAAA8AwFIAAAgGcoAAEAADxDAQgA\\\n",
-       "AOAZCkAAAADPUAACAAB4hgIQAADAMxSAAAAAnqEABAAA8AwFIAAAgGcoAAEAADxDAQgAAOAZCkAA\\\n",
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-       "\"\n",
-       "\n",
-       "\n",
-       "    /* set a timeout to make sure all the above elements are created before\n",
-       "       the object is initialized. */\n",
-       "    setTimeout(function() {\n",
-       "        animb9e3ef1230484e69aa575052c4433cef = new Animation(frames, img_id, slider_id, 59.0,\n",
-       "                                 loop_select_id);\n",
-       "    }, 0);\n",
-       "  })()\n",
-       "</script>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "B = 64\n",
     "fig, ax = plt.subplots()\n",
@@ -17442,11 +160,13 @@
     "img = ax.imshow(sim([times[0]]), origin=\"lower\", vmin=0, vmax=1.5)\n",
     "ax.set_title(\"Brightness at time 0\")\n",
     "\n",
+    "\n",
     "def update(i):\n",
     "    img.set_data(sim([times[i]]).detach().numpy())\n",
     "    ax.set_title(f\"Brightness at time {times[i]:.2f}\")\n",
     "    return img\n",
     "\n",
+    "\n",
     "ani = animation.FuncAnimation(fig, update, frames=B, interval=60)\n",
     "\n",
     "plt.close()\n",
@@ -17457,31 +177,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "205a7a3c",
    "metadata": {
     "tags": [
      "hide-input"
     ]
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "times = torch.linspace(0,6,Nobs)\n",
+    "times = torch.linspace(0, 6, Nobs)\n",
     "data = torch.zeros((Nobs, imgsize, imgsize))\n",
     "true_LC = SN_lightcurve.flux(times).detach().numpy()\n",
     "torch.manual_seed(1123)  # For reproducibility\n",
-    "noise_sim = Noiser(\"noise_sim\", sim, read_noise=sigma_read, exp_time= exp_time)\n",
+    "noise_sim = Noiser(\"noise_sim\", sim, read_noise=sigma_read, exp_time=exp_time)\n",
     "fig, axarr = plt.subplots(1, 5, figsize=(15, 3))\n",
     "fig.suptitle(\"Mock Observations\")\n",
     "for i, t in enumerate(times):\n",
@@ -17507,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "65bfaf98",
    "metadata": {},
    "outputs": [],
@@ -17519,259 +228,35 @@
     "        self.data = data\n",
     "        self.sigma_read = sigma_read\n",
     "        self.exp_time = exp_time\n",
-    "        \n",
+    "\n",
     "    @ck.forward\n",
     "    def residuals(self):\n",
     "        model_output = self.model()\n",
-    "        variance = self.sigma_read**2 + model_output / self.exp_time \n",
+    "        variance = self.sigma_read**2 + model_output / self.exp_time\n",
     "        sigma = variance.sqrt()\n",
     "        residuals = (self.data - model_output) / sigma\n",
     "        return residuals, sigma\n",
     "\n",
     "    @ck.forward\n",
-    "    def __call__(self): # log likelihood\n",
+    "    def __call__(self):  # log likelihood\n",
     "        residuals, sigma = self.residuals()\n",
-    "        return -0.5 * (residuals ** 2).sum() - sigma.log().sum()"
+    "        return -0.5 * (residuals**2).sum() - sigma.log().sum()"
    ]
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-   "execution_count": 7,
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+   "outputs": [],
    "source": [
     "# Model\n",
     "SNmodel = Gaussian(\"SN\", x0=-0.35, y0=-0.2, q=1.0, phi=0.0, sigma=0.05, flux=0.2)\n",
-    "SNmodel.x0.to_dynamic() # \"unknown\" parameters\n",
+    "SNmodel.x0.to_dynamic()  # \"unknown\" parameters\n",
     "SNmodel.y0.to_dynamic()\n",
     "SNmodel.flux.to_dynamic()\n",
     "Galaxymodel = Gaussian(\"Galaxy\", x0=0.2, y0=0.2, q=0.6, phi=0.5, sigma=0.3, flux=1.0)\n",
-    "Galaxymodel.to_dynamic() # \"unknown\" parameters\n",
+    "Galaxymodel.to_dynamic()  # \"unknown\" parameters\n",
     "firstmodel = Combined(\"firstmodel\", imgx, imgy, [SNmodel, Galaxymodel])\n",
     "likelihood = Likelihood(\"likelihood\", firstmodel, data[0], sigma_read=sigma_read, exp_time=exp_time)\n",
     "likelihood.graphviz()"
@@ -17793,14 +278,16 @@
     "\n",
     "    # Fit the model\n",
     "    x0 = likelihood.build_params_array()\n",
-    "    x0 += torch.randn_like(x0) * x0 * 0.05  # Add some noise to the initial guess since we cant start at the true values\n",
-    "    res = minimize(lambda x: -likelihood(torch.tensor(x)).numpy(), x0, method='Nelder-Mead')\n",
+    "    x0 += (\n",
+    "        torch.randn_like(x0) * x0 * 0.05\n",
+    "    )  # Add some noise to the initial guess since we cant start at the true values\n",
+    "    res = minimize(lambda x: -likelihood(torch.tensor(x)).numpy(), x0, method=\"Nelder-Mead\")\n",
     "    light_curve_flux.append(res.x[2])\n",
     "\n",
     "    # Get uncertainty using inverse Hessian\n",
     "    hess = -hessian(likelihood, torch.tensor(res.x), strict=True)\n",
     "    hess_inv = torch.linalg.inv(hess)\n",
-    "    light_curve_sigma.append(hess_inv[2,2].abs().sqrt().item())\n",
+    "    light_curve_sigma.append(hess_inv[2, 2].abs().sqrt().item())\n",
     "\n",
     "    # Store model images and residuals\n",
     "    model_images.append(firstmodel(torch.tensor(res.x)).detach().numpy())\n",
@@ -17809,25 +296,14 @@
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+   "execution_count": null,
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",
-      "text/plain": [
-       "<Figure size 1500x600 with 10 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, axarr = plt.subplots(2, 5, figsize=(15, 6))\n",
     "fig.suptitle(\"Fit Model and Residuals\")\n",
@@ -17842,32 +318,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "5fa4555b",
    "metadata": {
     "tags": [
      "hide-input"
     ]
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plt.errorbar(times.numpy(), light_curve_flux, yerr=light_curve_sigma, fmt='o', label='Estimated flux')\n",
-    "plt.plot(times.numpy(), true_LC, 'o', label='True flux')\n",
-    "plt.xlabel('Time')\n",
-    "plt.ylabel('SN flux')\n",
-    "plt.ylim(0,None)\n",
-    "plt.title('Estimated SN flux over time')\n",
+    "plt.errorbar(\n",
+    "    times.numpy(), light_curve_flux, yerr=light_curve_sigma, fmt=\"o\", label=\"Estimated flux\"\n",
+    ")\n",
+    "plt.plot(times.numpy(), true_LC, \"o\", label=\"True flux\")\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"SN flux\")\n",
+    "plt.ylim(0, None)\n",
+    "plt.title(\"Estimated SN flux over time\")\n",
     "plt.legend()\n",
     "plt.show()"
    ]
@@ -17894,779 +361,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "9f6fc590",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
-       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
-       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
-       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
-       "<!-- Generated by graphviz version 2.43.0 (0)\n",
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-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# One galaxy model since it does not change across images\n",
     "Galaxymodel = Gaussian(f\"Galaxy{i}\", x0=0.2, y0=0.2, q=0.6, phi=0.5, sigma=0.3, flux=1.0)\n",
-    "Galaxymodel.to_dynamic() # \"unknown\" parameters\n",
+    "Galaxymodel.to_dynamic()  # \"unknown\" parameters\n",
     "\n",
     "imgmodels = []\n",
     "for i in range(Nobs):\n",
     "    SNmodel = Gaussian(f\"SN{i}\", x0=-0.35, y0=-0.2, q=1.0, phi=0.0, sigma=0.05, flux=0.2)\n",
-    "    SNmodel.x0.to_dynamic() # \"unknown\" parameters\n",
+    "    SNmodel.x0.to_dynamic()  # \"unknown\" parameters\n",
     "    SNmodel.y0.to_dynamic()\n",
     "    SNmodel.flux.to_dynamic()\n",
     "    imgmodel = Combined(f\"image{i}\", imgx, imgy, [SNmodel, Galaxymodel])\n",
     "    imgmodels.append(imgmodel)\n",
-    "    if i > 0: # SN position doesnt change across images\n",
+    "    if i > 0:  # SN position doesnt change across images\n",
     "        SNmodel.x0 = imgmodels[0].models[0].x0\n",
     "        SNmodel.y0 = imgmodels[0].models[0].y0\n",
     "\n",
+    "\n",
     "class Stack(ck.Module):\n",
     "    def __init__(self, name, models):\n",
     "        super().__init__(name)\n",
@@ -18675,6 +391,8 @@
     "    @ck.forward\n",
     "    def __call__(self):\n",
     "        return torch.stack([model() for model in self.models], dim=0)\n",
+    "\n",
+    "\n",
     "secondmodel = Stack(\"secondmodel\", imgmodels)\n",
     "likelihood2 = Likelihood(\"likelihood2\", secondmodel, data, sigma_read=sigma_read, exp_time=exp_time)\n",
     "likelihood2.graphviz()"
@@ -18682,38 +400,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "f76fd88b",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Fit light curve\n",
     "x0 = likelihood2.build_params_array()\n",
-    "x0 += torch.randn_like(x0) * x0 * 0.05  # Add some noise to the initial guess since we cant start at the true values\n",
-    "res = minimize(lambda x: -likelihood2(torch.tensor(x)).numpy(), x0, method='Nelder-Mead')"
+    "x0 += (\n",
+    "    torch.randn_like(x0) * x0 * 0.05\n",
+    ")  # Add some noise to the initial guess since we cant start at the true values\n",
+    "res = minimize(lambda x: -likelihood2(torch.tensor(x)).numpy(), x0, method=\"Nelder-Mead\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "8c9a4bf5",
    "metadata": {
     "tags": [
      "hide-input"
     ]
    },
-   "outputs": [
-    {
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",
-      "text/plain": [
-       "<Figure size 1500x600 with 10 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, axarr = plt.subplots(2, 5, figsize=(15, 6))\n",
     "fig.suptitle(\"Fit Model and Residuals (Joint Model)\")\n",
@@ -18737,13 +446,13 @@
    "source": [
     "# extract light curve\n",
     "likelihood2.fill_dynamic_values(torch.tensor(res.x))\n",
-    "likelihood2.to_static(local_only=False)\n",
+    "likelihood2.to_static(children_only=False)\n",
     "light_curve_flux = []\n",
     "light_curve_sigma = []\n",
     "for model in secondmodel.models:\n",
     "    light_curve_flux.append(model.models[0].flux.value.item())\n",
     "    model.models[0].flux.to_dynamic()\n",
-    "    \n",
+    "\n",
     "# Compute uncertainty using inverse Hessian\n",
     "hess = -hessian(likelihood2, likelihood2.build_params_array(), strict=True)\n",
     "hess_inv = torch.linalg.inv(hess)  # Invert the Hessian to get the covariance matrix\n",
@@ -18752,42 +461,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "13aa5bf2",
    "metadata": {
     "tags": [
      "hide-input"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Estimated light curve fluxes: [0.03493320569396019, 0.13849201798439026, 0.2536572813987732, 0.15727443993091583, 0.013588673435151577]\n",
-      "Estimated light curve uncertainties: [nan nan nan nan nan]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"Estimated light curve fluxes:\", light_curve_flux)\n",
     "print(\"Estimated light curve uncertainties:\", light_curve_sigma)\n",
-    "plt.errorbar(times.numpy(), light_curve_flux, yerr=light_curve_sigma, fmt='o', label='Estimated flux')\n",
-    "plt.plot(times.numpy(), true_LC, 'o', label='True flux')\n",
-    "plt.xlabel('Time')\n",
-    "plt.ylabel('SN flux')\n",
-    "plt.ylim(0,None)\n",
-    "plt.title('Estimated SN flux over time (Joint Model)')\n",
+    "plt.errorbar(\n",
+    "    times.numpy(), light_curve_flux, yerr=light_curve_sigma, fmt=\"o\", label=\"Estimated flux\"\n",
+    ")\n",
+    "plt.plot(times.numpy(), true_LC, \"o\", label=\"True flux\")\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"SN flux\")\n",
+    "plt.ylim(0, None)\n",
+    "plt.title(\"Estimated SN flux over time (Joint Model)\")\n",
     "plt.legend()\n",
     "plt.show()"
    ]
@@ -18812,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "ff152b92",
    "metadata": {
     "tags": [
@@ -18829,834 +521,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "97fc1572",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-     "execution_count": 17,
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+   "outputs": [],
    "source": [
     "# Add light curve model to control the fluxes\n",
     "lightcurvemodel = Gaussian1D(\"lightcurvemodel\", t0=-3.0, sigma=2.5, peak_flux=0.25)\n",
@@ -19678,38 +546,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "3bd783dd",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Fit light curve\n",
     "x0 = likelihood2.build_params_array()\n",
-    "x0 += torch.randn_like(x0) * x0 * 0.05  # Add some noise to the initial guess since we cant start at the true values\n",
-    "res = minimize(lambda x: -likelihood2(torch.tensor(x)).numpy(), x0, method='Nelder-Mead')"
+    "x0 += (\n",
+    "    torch.randn_like(x0) * x0 * 0.05\n",
+    ")  # Add some noise to the initial guess since we cant start at the true values\n",
+    "res = minimize(lambda x: -likelihood2(torch.tensor(x)).numpy(), x0, method=\"Nelder-Mead\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "c4395a23",
    "metadata": {
     "tags": [
      "hide-input"
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-   "outputs": [
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",
-      "text/plain": [
-       "<Figure size 1500x600 with 10 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, axarr = plt.subplots(2, 5, figsize=(15, 6))\n",
     "fig.suptitle(\"Fit Model and Residuals (Joint Functional Model)\")\n",
@@ -19726,7 +585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "15485c7d",
    "metadata": {},
    "outputs": [],
@@ -19741,40 +600,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "845b0526",
    "metadata": {
     "tags": [
      "hide-input"
     ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Estimated light curve fluxes: [0.02484535053372383, 0.14358696341514587, 0.2610667049884796, 0.14933258295059204, 0.026873502880334854]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"Estimated light curve fluxes:\", light_curve_flux)\n",
-    "plt.plot(times.numpy(), light_curve_flux, 'o', label='Estimated flux', markersize = 6)\n",
-    "plt.plot(times.numpy(), true_LC, 'o', label='True flux')\n",
-    "plt.xlabel('Time')\n",
-    "plt.ylabel('SN flux')\n",
-    "plt.ylim(0,None)\n",
-    "plt.title('Estimated SN flux over time (Joint Functional Model)')\n",
+    "plt.plot(times.numpy(), light_curve_flux, \"o\", label=\"Estimated flux\", markersize=6)\n",
+    "plt.plot(times.numpy(), true_LC, \"o\", label=\"True flux\")\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"SN flux\")\n",
+    "plt.ylim(0, None)\n",
+    "plt.title(\"Estimated SN flux over time (Joint Functional Model)\")\n",
     "plt.legend()\n",
     "plt.show()"
    ]
@@ -19789,20 +630,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "a8225e90",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Light Curve t0: -3.03 ± 0.09 vs -3.00 (true)\n",
-      "Light Curve sigma: 1.97 ± 0.12 vs 2.00 (true)\n",
-      "Light Curve peak flux: 0.261 ± 0.018 vs 0.250 (true)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "likelihood2.to_static(False)\n",
     "lightcurvemodel.to_dynamic()\n",
@@ -19810,36 +641,31 @@
     "hess = -hessian(likelihood2, fit_vals, strict=True)\n",
     "hess_inv = torch.linalg.inv(hess)  # Invert the Hessian to get the covariance matrix\n",
     "light_curve_sigma = torch.sqrt(torch.diag(hess_inv).abs()).numpy()\n",
-    "print(f\"Light Curve t0: {fit_vals[0].item():.2f} ± {light_curve_sigma[0]:.2f} vs {SN_lightcurve.t0.value.item():.2f} (true)\")\n",
-    "print(f\"Light Curve sigma: {fit_vals[1].item():.2f} ± {light_curve_sigma[1]:.2f} vs {SN_lightcurve.sigma.value.item():.2f} (true)\")\n",
-    "print(f\"Light Curve peak flux: {fit_vals[2].item():.3f} ± {light_curve_sigma[2]:.3f} vs {SN_lightcurve.peak_flux.value.item():.3f} (true)\")"
+    "print(\n",
+    "    f\"Light Curve t0: {fit_vals[0].item():.2f} ± {light_curve_sigma[0]:.2f} vs {SN_lightcurve.t0.value.item():.2f} (true)\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"Light Curve sigma: {fit_vals[1].item():.2f} ± {light_curve_sigma[1]:.2f} vs {SN_lightcurve.sigma.value.item():.2f} (true)\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"Light Curve peak flux: {fit_vals[2].item():.3f} ± {light_curve_sigma[2]:.3f} vs {SN_lightcurve.peak_flux.value.item():.3f} (true)\"\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "0119a4df",
    "metadata": {
     "tags": [
      "hide-input"
     ]
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots()\n",
-    "ax.axvline(SN_lightcurve.sigma.value.item(), color='r', linestyle='--', label='True values')\n",
-    "ax.axhline(SN_lightcurve.peak_flux.value.item(), color='r', linestyle='--')\n",
+    "ax.axvline(SN_lightcurve.sigma.value.item(), color=\"r\", linestyle=\"--\", label=\"True values\")\n",
+    "ax.axhline(SN_lightcurve.peak_flux.value.item(), color=\"r\", linestyle=\"--\")\n",
     "ax.set_xlabel(\"Sigma\")\n",
     "ax.set_ylabel(\"Peak Flux\")\n",
     "lambda_, v = np.linalg.eig(hess_inv[1:, 1:])\n",
@@ -19856,7 +682,7 @@
     "        alpha=0.6,\n",
     "    )\n",
     "    ax.add_artist(ellipse)\n",
-    "plt.plot([],[], c=\"k\", label=\"Likelihood Contours\")\n",
+    "plt.plot([], [], c=\"k\", label=\"Likelihood Contours\")\n",
     "ax.set_xlim(fit_vals[1].item() - lambda_[0] * 3, fit_vals[1].item() + lambda_[0] * 3)\n",
     "ax.set_ylim(fit_vals[2].item() - lambda_[1] * 3, fit_vals[2].item() + lambda_[1] * 3)\n",
     "ax.set_title(\"Light Curve Parameter Uncertainty (Hessian)\")\n",
@@ -19866,7 +692,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "eca2d7d1",
    "metadata": {
     "tags": [
@@ -19893,26 +719,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "fb6b8546",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "burn-in\n",
-      "production\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "vsim = torch.vmap(likelihood2)\n",
     "\n",
+    "\n",
     "# Log-likelihood function\n",
     "def density(x):\n",
     "    return vsim(torch.as_tensor(x, dtype=torch.float32)).numpy()\n",
     "\n",
+    "\n",
     "x0 = likelihood2.build_params_array()\n",
     "nwalkers = 32\n",
     "ndim = len(x0)\n",
@@ -19929,32 +748,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "id": "75fc7f6a",
    "metadata": {
     "tags": [
      "hide-input"
     ]
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1200 with 121 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "SN_lightcurve.to_dynamic()\n",
     "Galaxy.to_dynamic()\n",
-    "true_values = [SN.x0.value.item(), SN.y0.value.item()] + list(SN_lightcurve.build_params_array().numpy()) + list(Galaxy.build_params_array().numpy())\n",
+    "true_values = (\n",
+    "    [SN.x0.value.item(), SN.y0.value.item()]\n",
+    "    + list(SN_lightcurve.build_params_array().numpy())\n",
+    "    + list(Galaxy.build_params_array().numpy())\n",
+    ")\n",
     "chain_mh = sampler.get_chain(flat=True)\n",
     "fig, axarr = plt.subplots(ndim, ndim, figsize=(12, 12))\n",
-    "plt.subplots_adjust(hspace=0., wspace=0.)\n",
+    "plt.subplots_adjust(hspace=0.0, wspace=0.0)\n",
     "labels = list(p.name for p in likelihood2.dynamic_params)\n",
     "labels[0] = \"SN x0\"\n",
     "labels[1] = \"SN y0\"\n",
@@ -19965,21 +777,21 @@
     "        if j > i:\n",
     "            axarr[i, j].axis(\"off\")\n",
     "            continue\n",
-    "        axarr[i, j].axvline(true_values[j], color='r', label='True value', linewidth=0.5)\n",
-    "        axarr[i,j].set_xlim(chain_mh[:, j].min(), chain_mh[:, j].max())\n",
+    "        axarr[i, j].axvline(true_values[j], color=\"r\", label=\"True value\", linewidth=0.5)\n",
+    "        axarr[i, j].set_xlim(chain_mh[:, j].min(), chain_mh[:, j].max())\n",
     "        if i == j:\n",
     "            axarr[i, j].hist(chain_mh[:, i], bins=30, density=True)\n",
     "        else:\n",
     "            axarr[i, j].scatter(chain_mh[:, j][::25], chain_mh[:, i][::25], s=1, alpha=0.5)\n",
-    "            axarr[i,j].axhline(true_values[i], color='r', label='True value', linewidth=0.5)\n",
-    "            axarr[i,j].set_ylim(chain_mh[:, i].min(), chain_mh[:, i].max())\n",
+    "            axarr[i, j].axhline(true_values[i], color=\"r\", label=\"True value\", linewidth=0.5)\n",
+    "            axarr[i, j].set_ylim(chain_mh[:, i].min(), chain_mh[:, i].max())\n",
     "\n",
     "        if j == 0:\n",
     "            axarr[i, j].set_ylabel(f\"{labels[i]}\")\n",
-    "        if i == ndim-1:\n",
+    "        if i == ndim - 1:\n",
     "            axarr[i, j].set_xlabel(f\"{labels[j]}\")\n",
-    "        axarr[i,j].set_xticks([])\n",
-    "        axarr[i,j].set_yticks([])\n",
+    "        axarr[i, j].set_xticks([])\n",
+    "        axarr[i, j].set_yticks([])\n",
     "plt.show()"
    ]
   },
@@ -20008,7 +820,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "PY39",
+   "display_name": "PY312 (3.12.3)",
    "language": "python",
    "name": "python3"
   },
@@ -20022,7 +834,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/src/caskade/__init__.py b/src/caskade/__init__.py
index 25a8430..097af53 100644
--- a/src/caskade/__init__.py
+++ b/src/caskade/__init__.py
@@ -5,7 +5,7 @@
 from .context import ActiveContext, ValidContext, OverrideParam
 from .decorators import forward, active_cache
 from .module import Module
-from .param import Param, dynamic
+from .param import Param
 from .collection import NodeCollection, NodeList, NodeTuple
 from .tests import test
 from .errors import (
@@ -34,7 +34,6 @@
     "ArrayLike",
     "Module",
     "Param",
-    "dynamic",
     "NodeCollection",
     "NodeList",
     "NodeTuple",
diff --git a/src/caskade/backend.py b/src/caskade/backend.py
index 5ff8394..1cb71d4 100644
--- a/src/caskade/backend.py
+++ b/src/caskade/backend.py
@@ -36,9 +36,6 @@ def _load_backend(self, backend):
         elif backend == "numpy":
             self.setup_numpy()
             return importlib.import_module("numpy")
-        elif backend == "object":
-            self.setup_object()
-            return None
         else:
             raise ValueError(f"Unsupported backend: {backend}")
 
@@ -82,18 +79,6 @@ def setup_numpy(self):
         self.logit = self._logit_numpy
         self.sigmoid = self._sigmoid_numpy
 
-    def setup_object(self):
-        self.make_array = self._make_array_object
-        self._array_type = self._array_type_object
-        self.concatenate = None
-        self.copy = None
-        self.detach = None
-        self.tolist = None
-        self.view = None
-        self.as_array = self._as_array_object
-        self.to = None
-        self.to_numpy = self._to_numpy_object
-
     @property
     def array_type(self):
         return self._array_type()
@@ -107,9 +92,6 @@ def _make_array_jax(self, array, dtype=None, **kwargs):
     def _make_array_numpy(self, array, dtype=None, **kwargs):
         return self.module.array(array, dtype=dtype)
 
-    def _make_array_object(self, array, **kwargs):
-        return array
-
     def _array_type_torch(self):
         return self.module.Tensor
 
@@ -119,9 +101,6 @@ def _array_type_jax(self):
     def _array_type_numpy(self):
         return self.module.ndarray
 
-    def _array_type_object(self):
-        return object
-
     def _concatenate_torch(self, arrays, axis=0):
         return self.module.cat(arrays, dim=axis)
 
@@ -167,9 +146,6 @@ def _as_array_jax(self, array, dtype=None, **kwargs):
     def _as_array_numpy(self, array, dtype=None, **kwargs):
         return self.module.asarray(array, dtype=dtype)
 
-    def _as_array_object(self, array, **kwargs):
-        return array
-
     def _to_torch(self, array, dtype=None, device=None):
         return array.to(dtype=dtype, device=device)
 
@@ -188,9 +164,6 @@ def _to_numpy_jax(self, array):
     def _to_numpy_numpy(self, array):
         return array
 
-    def _to_numpy_object(self, array):
-        return np.array(array)
-
     def any(self, array):
         return self.module.any(array)
 
diff --git a/src/caskade/base.py b/src/caskade/base.py
index bc27881..c617a4a 100644
--- a/src/caskade/base.py
+++ b/src/caskade/base.py
@@ -73,7 +73,7 @@ def __init__(
         self._children = {}
         self._parents = set()
         self._active = False
-        self._type = "node"
+        self.node_type = "node"
         self.description = description
         self.meta = meta()
         self.saveattrs = set()
@@ -85,11 +85,11 @@ def name(self) -> str:
         return self._name
 
     @property
-    def children(self) -> dict:
+    def children(self) -> dict[str, "Node"]:
         return self._children
 
     @property
-    def parents(self) -> set:
+    def parents(self) -> set["Node"]:
         return self._parents
 
     def _link(self, key: str, child: "Node"):
@@ -113,8 +113,8 @@ def _link(self, key: str, child: "Node"):
                 f"Linking {child.name} to {self.name} would create a cycle in the graph"
             )
 
-        self._children[key] = child
-        child._parents.add(self)
+        self.children[key] = child
+        child.parents.add(self)
         self.update_graph()
 
     def link(self, key: Union[str, tuple, "Node"], child: Optional[Union["Node", tuple]] = None):
@@ -163,9 +163,9 @@ def link(self, key: Union[str, tuple, "Node"], child: Optional[Union["Node", tup
     def _unlink(self, key: str):
         if self.active:
             raise GraphError(f"Cannot link/unlink nodes while the graph is active ({self.name})")
-        self._children[key]._parents.remove(self)
-        self._children[key].update_graph()
-        del self._children[key]
+        self.children[key].parents.remove(self)
+        self.children[key].update_graph()
+        del self.children[key]
         self.update_graph()
 
     def unlink(self, key: Union[str, "Node", list, tuple]):
@@ -181,20 +181,30 @@ def unlink(self, key: Union[str, "Node", list, tuple]):
             return
         self.__delattr__(key)
 
-    def topological_ordering(
-        self, with_type: Optional[str] = None, with_isinstance: Optional[object] = None
-    ) -> tuple["Node"]:
-        """Return a topological ordering of the graph below the current node."""
-        ordering = [self]
-        for node in self.children.values():
-            for subnode in node.topological_ordering():
-                if subnode not in ordering:
-                    ordering.append(subnode)
-        if with_type is not None:
-            ordering = filter(lambda n: with_type in n._type, ordering)
-        if with_isinstance is not None:
-            ordering = filter(lambda n: isinstance(n, with_isinstance), ordering)
-        return tuple(ordering)
+    def topological_ordering(self) -> tuple["Node"]:
+        """
+        Return a topological ordering of the graph below the current node.
+        Uses Iterative Deepening DFS (Post-Order) to resolve dependencies.
+        """
+        visited = set()
+        stack = []
+
+        def visit(node: Node):
+            if node in visited:
+                return
+            visited.add(node)
+
+            # Visit all children first
+            for child in reversed(node.children.values()):
+                visit(child)
+
+            # Add node to stack only after all children are processed
+            stack.append(node)
+
+        visit(self)
+
+        # Reverse the stack to get Parent -> Child ordering
+        return tuple(reversed(stack))
 
     def update_graph(self):
         """Triggers a call to all parents that the graph below them has been
@@ -210,14 +220,14 @@ def active(self) -> bool:
     @active.setter
     def active(self, value: bool):
         # Avoid unnecessary updates
-        if self._active == value:
+        if self._active is value:
             return
 
         # Set self active level
         self._active = value
 
         # Propagate active level to children
-        for child in self._children.values():
+        for child in self.children.values():
             child.active = value
 
     def to(self, device=None, dtype=None):
@@ -306,9 +316,6 @@ def save_state(self, saveto: Union[str, "File"], appendable: bool = False):
             Defaults to False.
 
         """
-        if appendable and backend.backend == "object":
-            raise BackendError("Cannot make appendable HDF5 files with the 'object' backend")
-
         if isinstance(saveto, str):
             if saveto.endswith(".h5") or saveto.endswith(".hdf5"):
                 with h5py.File(saveto, "w") as h5file:
@@ -351,9 +358,6 @@ def _append_state_hdf5(self, h5group):
 
     def append_state(self, saveto: Union[str, "File"]):
         """Append the state of the node and its children to an existing HDF5 file."""
-        if backend.backend == "object":
-            raise BackendError("Cannot append to HDF5 files with the 'object' backend")
-
         if isinstance(saveto, str):
             if saveto.endswith(".h5") or saveto.endswith(".hdf5"):
                 with h5py.File(saveto, "a") as h5file:
@@ -425,10 +429,10 @@ def graphviz(self, top_down: bool = True, saveto: Optional[str] = None) -> "grap
 
         components = set()
 
-        def add_node(node, dot):
+        def add_node(node: Node, dot):
             if node in components:
                 return
-            dot.attr("node", **node.graphviz_types[node._type])
+            dot.attr("node", **node.graphviz_types[node.node_type])
             dot.node(str(id(node)), repr(node))
             components.add(node)
 
@@ -448,7 +452,7 @@ def add_node(node, dot):
 
     @property
     def node_str(self):
-        return f"{self.name}|{self._type}"
+        return f"{self.name}|{self.node_type}"
 
     def graph_dict(self) -> dict[str, dict]:
         """Return a dictionary representation of the graph below the current
@@ -488,7 +492,7 @@ def __hash__(self) -> int:
     def __setattr__(self, key: str, value: Any):
         """Intercept attribute setting to update parameters and graph links."""
         if isinstance(value, Node):
-            # check for trying setting an attr with its own setter, allow the setter to handle throwing errors (e.g. value, and dynamic_value)
+            # check for trying setting an attr with its own setter, allow the setter to handle throwing errors (e.g. value)
             if not hasattr(getattr(type(self), key, None), "fset"):
                 self._link(key, value)
 
diff --git a/src/caskade/collection.py b/src/caskade/collection.py
index 15e3f57..7c1b2ab 100644
--- a/src/caskade/collection.py
+++ b/src/caskade/collection.py
@@ -45,7 +45,7 @@ class NodeTuple(NodeCollection, tuple):
     def __init__(self, iterable=None, name=None):
         tuple.__init__(iterable)
         Node.__init__(self, name=name)
-        self._type = "ntuple"
+        self.node_type = "ntuple"
 
         for node in self:
             if not isinstance(node, Node):
@@ -68,7 +68,7 @@ class NodeList(NodeCollection, list):
     def __init__(self, iterable=(), name=None):
         list.__init__(self, iterable)
         Node.__init__(self, name)
-        self._type = "nlist"
+        self.node_type = "nlist"
 
         self._link_nodes()
 
diff --git a/src/caskade/context.py b/src/caskade/context.py
index 356b543..c4d3c21 100644
--- a/src/caskade/context.py
+++ b/src/caskade/context.py
@@ -15,7 +15,7 @@ def __init__(self, module: Module, active: bool = True):
     def __enter__(self):
         self.outer_active = self.module.active
         if self.outer_active and not self.active:
-            self.outer_params = list(p.value for p in self.module.dynamic_params)
+            self.state = list(p._value for p in self.module.all_params)
             self.module.clear_state()
         self.module.active = self.active
 
@@ -24,7 +24,8 @@ def __exit__(self, exc_type, exc_value, traceback):
             self.module.clear_state()
         self.module.active = self.outer_active
         if self.outer_active and not self.active:
-            self.module.fill_params(self.outer_params)
+            for p, s in zip(self.module.all_params, self.state):
+                p._value = s
 
 
 class ValidContext:
@@ -50,7 +51,7 @@ class OverrideParam:
     OverrideParam will the parameter be set to the new value.
     """
 
-    def __init__(self, param, value):
+    def __init__(self, param: Param, value):
         self.param = param
         self.value = value
 
diff --git a/src/caskade/module.py b/src/caskade/module.py
index 723251a..0fee9f9 100644
--- a/src/caskade/module.py
+++ b/src/caskade/module.py
@@ -12,7 +12,6 @@
     FillDynamicParamsArrayError,
     FillDynamicParamsSequenceError,
     FillDynamicParamsMappingError,
-    BackendError,
 )
 
 
@@ -63,6 +62,7 @@ def otherfun(self, x, c = None):
     _special_tuples = (
         "dynamic_params",
         "pointer_params",
+        "static_params",
         "dynamic_modules",
     )  # These tuples will not be converted to NodeTuple objects
     graphviz_types = {"module": {"style": "solid", "color": "black", "shape": "ellipse"}}
@@ -70,82 +70,82 @@ def otherfun(self, x, c = None):
     def __init__(self, name: Optional[str] = None, **kwargs):
         super().__init__(name=name, **kwargs)
         self.dynamic_params = ()
-        self.all_dynamic_value = True
         self.pointer_params = ()
-        self.local_dynamic_params = {}
-        self._type = "module"
+        self.child_dynamic_params = {}
+        self.node_type = "module"
         self.valid_context = False
         self.clear_state_hooks = set()
 
+    @property
+    def all_params(self):
+        return self.static_params + self.dynamic_params + self.pointer_params
+
     def update_graph(self):
         """Maintain a tuple of dynamic and live parameters at all points lower
         in the DAG."""
-        self.dynamic_params = tuple(self.topological_ordering("dynamic"))
-        self.all_dynamic_value = all("value" in p._type for p in self.dynamic_params)
-        self.pointer_params = tuple(self.topological_ordering("pointer"))
-        self.local_dynamic_params = dict(
+        T = self.topological_ordering()
+        self.dynamic_params = tuple(filter(lambda n: isinstance(n, Param) and n.dynamic, T))
+        self.pointer_params = tuple(filter(lambda n: isinstance(n, Param) and n.pointer, T))
+        self.static_params = tuple(filter(lambda n: isinstance(n, Param) and n.static, T))
+        self.child_dynamic_params = dict(
             (k, p) for k, p in self.children.items() if isinstance(p, Param) and p.dynamic
         )
         self.dynamic_modules = tuple(
-            m for m in self.topological_ordering(with_isinstance=Module) if m.dynamic
+            m for m in filter(lambda n: isinstance(n, Module), T) if m.dynamic
         )
         super().update_graph()
 
+    def param_order(self):
+        return ", ".join(
+            tuple(f"{next(iter(p.parents)).name}: {p.name}" for p in self.dynamic_params)
+        )
+
     @property
     def dynamic(self) -> bool:
         """Return True if the module has dynamic parameters as direct children."""
-        return len(self.local_dynamic_params) > 0
+        return len(self.child_dynamic_params) > 0
 
     @property
     def static(self) -> bool:
         return not self.dynamic
 
-    def to_dynamic(self, local_only=True, ignore_pointer=True, **kwargs):
+    def to_dynamic(self, children_only=True, **kwargs):
         """Change all parameters to dynamic parameters. If the parameter has a
-        value, this will be stored in the ``dynamic_value`` attribute.
+        value, this will become a dynamic value parameter.
 
         Parameters
         ----------
-        local_only: (bool, optional)
-            If True, only convert the local parameters that are children of this
-            module. If False, convert all parameters in the graph below this
-            module. Defaults to True.
-        ignore_pointer: (bool, optional)
-            If True, do not convert any parameters that are pointers. Defaults
-            to True.
+        children_only: (bool, optional)
+            If True, only convert the children of this module to dynamic. If False,
+            convert all parameters in the graph below this module. Defaults to True.
         """
-        if local_only:
+        if children_only:
             for c in self.children.values():
-                if isinstance(c, Param) and not (ignore_pointer and c.pointer):
+                if isinstance(c, Param) and not c.pointer:
                     c.to_dynamic()
         else:
-            for n in self.topological_ordering(with_isinstance=Param):
-                if not (ignore_pointer and n.pointer):
-                    n.to_dynamic()
+            for node in self.topological_ordering():
+                if isinstance(node, Param) and not node.pointer:
+                    node.to_dynamic()
 
-    def to_static(self, local_only=True, ignore_pointer=True, **kwargs):
+    def to_static(self, children_only=True, **kwargs):
         """Change all parameters to static parameters. This only works if the
-        parameter has a ``dynamic_value`` set, or if the pointer can be
-        evaluated.
+        parameter has a ``dynamic value`` set to become the static value.
 
         Parameters
         ----------
-        local_only: (bool, optional)
-            If True, only convert the local parameters that are children of this
-            module. If False, convert all parameters in the graph below this
-            module. Defaults to True.
-        ignore_pointer: (bool, optional)
-            If True, do not convert any parameters that are pointers. Defaults
-            to True.
+        children_only: (bool, optional)
+            If True, only convert children of this module. If False, convert
+            all parameters in the graph below this module. Defaults to True.
         """
-        if local_only:
+        if children_only:
             for c in self.children.values():
-                if isinstance(c, Param) and not (ignore_pointer and c.pointer):
+                if isinstance(c, Param) and not c.pointer:
                     c.to_static()
         else:
-            for n in self.topological_ordering(with_isinstance=Param):
-                if not (ignore_pointer and n.pointer):
-                    n.to_static()
+            for node in self.topological_ordering():
+                if isinstance(node, Param) and not node.pointer:
+                    node.to_static()
 
     @property
     def valid_context(self) -> bool:
@@ -155,18 +155,17 @@ def valid_context(self) -> bool:
     @valid_context.setter
     def valid_context(self, value: bool):
         """Set the valid context of the module."""
-        if not isinstance(value, bool):
-            raise TypeError(f"Valid context must be a boolean, got {type(value)}")
         self._valid_context = value
-        for node in self.topological_ordering(with_isinstance=Module):
-            node._valid_context = value
+        for node in self.topological_ordering():
+            if isinstance(node, Module):
+                node._valid_context = value
 
     def _fill_dict(self, node, params, dynamic_values=False):
 
         for key in params:
             if key in node.children and isinstance(node[key], Param) and node[key].dynamic:
                 if dynamic_values:
-                    node[key].dynamic_value = params[key]
+                    node[key].dynamic_value(params[key])
                 else:
                     node[key]._value = params[key]
             elif (
@@ -175,7 +174,9 @@ def _fill_dict(self, node, params, dynamic_values=False):
                 and node[key].dynamic
                 and not isinstance(params[key], dict)
             ):
-                node[key]._fill_values(params[key], local=True, dynamic_values=dynamic_values)
+                node[key]._fill_values(
+                    params[key], children_only=True, dynamic_values=dynamic_values
+                )
             elif key in node.children and isinstance(node[key], Node) and node[key].dynamic:
                 self._fill_dict(node[key], params[key], dynamic_values=dynamic_values)
             else:
@@ -184,7 +185,7 @@ def _fill_dict(self, node, params, dynamic_values=False):
                 )
 
     def _fill_values(
-        self, params: Union[ArrayLike, Sequence, Mapping], local=False, dynamic_values=False
+        self, params: Union[ArrayLike, Sequence, Mapping], children_only=False, dynamic_values=False
     ):
         """
         Fill the dynamic parameters of the module with the input values from
@@ -207,9 +208,11 @@ def _fill_values(
             error eventually if a value is missing.
         """
 
-        dynamic_params = self.local_dynamic_params.values() if local else self.dynamic_params
+        dynamic_params = (
+            self.child_dynamic_params.values() if children_only else self.dynamic_params
+        )
 
-        if isinstance(params, backend.array_type) and backend.backend != "object":
+        if isinstance(params, backend.array_type):
             if params.shape[-1] == 0:
                 return  # No parameters to fill
             # check for batch dimension
@@ -226,7 +229,7 @@ def _fill_values(
                 try:
                     val = backend.view(params[..., pos : pos + size], B + param.shape)
                     if dynamic_values:
-                        param.dynamic_value = val
+                        param.dynamic_value(val)
                     else:
                         param._value = val
                 except (RuntimeError, IndexError, ValueError, TypeError):
@@ -241,12 +244,12 @@ def _fill_values(
             elif len(params) == len(dynamic_params):
                 for param, value in zip(dynamic_params, params):
                     if dynamic_values:
-                        param.dynamic_value = value
+                        param.dynamic_value(value)
                     else:
                         param._value = value
             elif len(params) == len(self.dynamic_modules):
                 for module, value in zip(self.dynamic_modules, params):
-                    module._fill_values(value, local=True, dynamic_values=dynamic_values)
+                    module._fill_values(value, children_only=True, dynamic_values=dynamic_values)
             else:
                 raise FillDynamicParamsSequenceError(
                     self.name, params, dynamic_params, self.dynamic_modules
@@ -254,11 +257,6 @@ def _fill_values(
         elif isinstance(params, Mapping):
             self._fill_dict(self, params, dynamic_values=dynamic_values)
         else:
-            try:
-                if params.dtype is not None and backend.backend == "object":
-                    raise BackendError("Cannot use ArrayLike operations when backend is 'object'")
-            except AttributeError:
-                pass
             raise TypeError(
                 f"Input params type {type(params)} not supported. Should be {backend.array_type.__name__}, Sequence, or Mapping."
             )
@@ -297,7 +295,7 @@ def clear_state(self):
         if not self.active:
             raise ActiveStateError(f"Module {self.name} must be active to clear state")
 
-        for param in self.dynamic_params + self.pointer_params:
+        for param in self.all_params:
             param._value = None
 
         for hook in list(self.clear_state_hooks):
@@ -332,40 +330,31 @@ def fill_dynamic_values(self, params: Union[ArrayLike, Sequence, Mapping]):
 
     def _check_dynamic_values(self, params_type: str = "ArrayLike"):
         """Check if all dynamic values are set."""
-        if not self.all_dynamic_value:
-            bad_params = []
-            for param in self.dynamic_params:
-                if "value" not in param._type:
-                    bad_params.append(param.name)
+        bad_params = []
+        for param in self.dynamic_params:
+            if "value" not in param.node_type:
+                bad_params.append(param.name)
+        if len(bad_params) > 0:
             raise ParamConfigurationError(
-                f"{self.name} Param(s) {bad_params} have no dynamic value, so the params {params_type} cannot be built. Set the `dynamic_value` attribute to use this feature."
+                f"{self.name} Param(s) {bad_params} have no dynamic value, so the params {params_type} cannot be built. Set to a dynamic value to use this feature."
             )
 
     def build_params_array(self) -> ArrayLike:
         """Return an input array-like object for this module's @forward methods by filling with dynamic values."""
-        if backend.backend == "object":
-            raise BackendError("Cannot use ArrayLike operations when backend is 'object'")
         self._check_dynamic_values("ArrayLike")
         x = []
-        is_batched = None
+        batch_shape = None
         for param in self.dynamic_params:
-            if len(param.value.shape) - len(param.shape) == 1:  # is batched
-                B, *_ = param.value.shape
-                x.append(backend.copy(param.value).reshape(B, -1))
-                if is_batched is None:
-                    is_batched = True
-                elif not is_batched:
-                    raise ParamConfigurationError(
-                        "Cannot mix batched and non-batched parameters when building params array!"
-                    )
-            else:
-                x.append(backend.copy(param.value).flatten())
-                if is_batched is None:
-                    is_batched = False
-                elif is_batched:
-                    raise ParamConfigurationError(
-                        "Cannot mix batched and non-batched parameters when building params array!"
-                    )
+            B = param.batch_shape
+            if batch_shape is None:
+                batch_shape = B
+            elif batch_shape != B:
+                raise ParamConfigurationError(
+                    f"Batch dimensions must be the same for all params. Got {B} for {param.name} when previous batch shape was {batch_shape}"
+                )
+
+            x.append(backend.copy(param.value).reshape(B + (-1,)))
+
         if len(x) == 0:
             return backend.make_array([])
         x = backend.concatenate(x, axis=-1)
@@ -406,12 +395,12 @@ def build_params_dict(self) -> dict[str, Union[dict, ArrayLike]]:
         return x
 
     def to_valid(
-        self, params: Union[ArrayLike, Sequence, Mapping], local=False
+        self, params: Union[ArrayLike, Sequence, Mapping], children_only=False
     ) -> Union[ArrayLike, Sequence, Mapping]:
         """Convert input params to valid params."""
-        if backend.backend == "object":
-            return params
-        dynamic_params = self.local_dynamic_params.values() if local else self.dynamic_params
+        dynamic_params = (
+            self.child_dynamic_params.values() if children_only else self.dynamic_params
+        )
         if isinstance(params, backend.array_type):
             valid_params = []  # backend.zeros_like(params)
             batch = len(params.shape) > 1
@@ -437,7 +426,7 @@ def to_valid(
                     valid_params.append(param.to_valid(value))
             elif len(params) == len(self.dynamic_modules):
                 for module, value in zip(self.dynamic_modules, params):
-                    valid_params.append(module.to_valid(value, local=True))
+                    valid_params.append(module.to_valid(value, children_only=True))
             else:
                 raise FillDynamicParamsSequenceError(
                     self.name, params, dynamic_params, self.dynamic_modules
@@ -446,7 +435,7 @@ def to_valid(
             valid_params = {}
             for key in params:
                 if key in self.children and isinstance(self[key], Module) and self[key].dynamic:
-                    valid_params[key] = self[key].to_valid(params[key], local=True)
+                    valid_params[key] = self[key].to_valid(params[key], children_only=True)
                 elif key in self.children and isinstance(self[key], Param) and self[key].dynamic:
                     valid_params[key] = self[key].to_valid(params[key])
                 else:
@@ -460,13 +449,13 @@ def to_valid(
         return valid_params
 
     def from_valid(
-        self, valid_params: Union[ArrayLike, Sequence, Mapping], local=False
+        self, valid_params: Union[ArrayLike, Sequence, Mapping], children_only=False
     ) -> Union[ArrayLike, Sequence, Mapping]:
         """Convert valid params to input params."""
-        if backend.backend == "object":
-            return valid_params
 
-        dynamic_params = self.local_dynamic_params.values() if local else self.dynamic_params
+        dynamic_params = (
+            self.child_dynamic_params.values() if children_only else self.dynamic_params
+        )
 
         if isinstance(valid_params, backend.array_type):
             params = []  # backend.zeros_like(valid_params)
@@ -493,7 +482,7 @@ def from_valid(
                     params.append(param.from_valid(value))
             elif len(valid_params) == len(self.dynamic_modules):
                 for module, value in zip(self.dynamic_modules, valid_params):
-                    params.append(module.from_valid(value, local=True))
+                    params.append(module.from_valid(value, children_only=True))
             else:
                 raise FillDynamicParamsSequenceError(
                     self.name, valid_params, dynamic_params, self.dynamic_modules
@@ -502,7 +491,7 @@ def from_valid(
             params = {}
             for key in valid_params:
                 if key in self.children and isinstance(self[key], Module) and self[key].dynamic:
-                    params[key] = self[key].from_valid(valid_params[key], local=True)
+                    params[key] = self[key].from_valid(valid_params[key], children_only=True)
                 elif key in self.children and isinstance(self[key], Param) and self[key].dynamic:
                     params[key] = self[key].from_valid(valid_params[key])
                 else:
diff --git a/src/caskade/param.py b/src/caskade/param.py
index cf8b29a..868b2db 100644
--- a/src/caskade/param.py
+++ b/src/caskade/param.py
@@ -1,10 +1,10 @@
 from typing import Optional, Union, Callable, Any
 from warnings import warn
 import traceback
-from dataclasses import dataclass
 from math import prod
 
 from numpy import ndarray
+import numpy as np
 
 from .backend import backend, ArrayLike
 from .base import Node
@@ -12,27 +12,14 @@
 from .warnings import InvalidValueWarning
 
 
-@dataclass
-class dynamic:
-    """Basic wrapper for an input to a ``Param`` object to indicate that the
-    value should be placed as a dynamic_value so that the ``Param`` is dynamic
-    instead of static.
-
-    Usage: ``dynamic(value)``
-
-    Example:
-
-    .. code-block:: python
-
-        class Test(Module):
-            def __init__(self, a):
-                self.a = Param("a", a)
-
-        t = Test(dynamic(1.0))
-        print(t.a.dynamic) # True
-    """
-
-    value: Any = None
+def valid_shape(shape, value_shape, batched):
+    if shape is None:  # no shape to compare
+        return True
+    if value_shape == shape:  # shapes match
+        return True
+    if batched and value_shape[len(value_shape) - len(shape) :] == shape:  # endswith
+        return True
+    return False
 
 
 class Param(Node):
@@ -41,19 +28,19 @@ class Param(Node):
 
     The ``Param`` object is used to represent a parameter in the graph. During
     runtime this will represent a value which can be used in various
-    calculations. The ``Param`` object can be set to a constant value (``static``);
-    ``None`` meaning the value is to be provided at runtime (``dynamic``); another
-    ``Param`` object meaning it will take on that  value at runtime (``pointer``);
-    or a function of other ``Param`` objects to be computed at runtime (also
-    ``pointer``, see user guides). These options allow users to flexibly set the
-    behavior of the simulator.
+    calculations. The ``Param`` object can be set to a constant value
+    (``static``); ``None`` meaning the value is to be provided at runtime
+    (``dynamic``); another ``Param`` object meaning it will take on that  value
+    at runtime (``pointer``); or a function of other ``Param`` objects to be
+    computed at runtime (also ``pointer``, see user guides). These options allow
+    users to flexibly set the behavior of the simulator.
 
     Examples
     --------
     Example making some ``Param`` objects::
 
         p1 = Param("test", (1.0, 2.0)) # constant value, length 2 vector
-        p2 = Param("p2", None, (2,2)) # dynamic 2x2 matrix value
+        p2 =Param("p2", None, (2,2)) # dynamic 2x2 matrix value
         p3 = Param("p3", p1) # pointer to another parameter
         p4 = Param("p4", lambda p: p.children["other"].value * 2) # arbitrary function of another parameter
         p5 = Param("p5", valid=(0.0,2*pi), units="radians", cyclic=True) # parameter with metadata
@@ -67,22 +54,25 @@ class Param(Node):
     shape: (Optional[tuple[int, ...]], optional)
         The shape of the parameter. Defaults to () meaning scalar.
     cyclic: (bool, optional)
-        Whether the parameter is cyclic, such as a rotation from 0 to 2pi.
-        Defaults to False.
+        Whether the parameter is cyclic, imposing periodic boundary conditions.
+        Such as a rotation from 0 to 2pi. Defaults to False.
     valid: (Optional[tuple[Union[ArrayLike, float, int, None]]], optional)
         The valid range of the parameter. Defaults to None meaning all of -inf
         to inf is valid.
     units: (Optional[str], optional)
         The units of the parameter. Defaults to None.
-    dynamic_value: (Optional[Union[ArrayLike, float, int]], optional)
-        Allows the parameter to store a value while still dynamic (think of it
-        as a default value).
+    dynamic: (bool, optional)
+        Force param to be dynamic if True. If a value is provided and param is dynamic
+        then it has a default value at call time.
+    batched (bool, optional):
+        If True, the param is assumed batched and the shape may now take the form
+        (*B, *D) where *D is the shape of the value.
     dtype: (Optional[Any], optional)
         The data type of the parameter. Defaults to None meaning the data type
         will be inferred from the value.
     device: (Optional[Any], optional)
-        The device of the parameter. Defaults to None meaning the device will
-        be inferred from the value.
+        The device of the parameter. Defaults to None meaning the device will be
+        inferred from the value.
     """
 
     graphviz_types = {
@@ -96,140 +86,152 @@ def __init__(
         self,
         name: str,
         value: Optional[Union[ArrayLike, float, int]] = None,
-        shape: Optional[tuple[int, ...]] = (),
+        shape: Optional[tuple[int, ...]] = None,
         cyclic: bool = False,
         valid: Optional[tuple[Union[ArrayLike, float, int, None]]] = None,
         units: Optional[str] = None,
-        dynamic_value: Optional[Union[ArrayLike, float, int]] = None,
+        dynamic: bool = False,
+        batched: bool = False,
         dtype: Optional[Any] = None,
         device: Optional[Any] = None,
         **kwargs,
     ):
+        self._node_type = "node"
         super().__init__(name=name, **kwargs)
-        if value is not None and dynamic_value is not None:
-            raise ParamConfigurationError("Cannot set both value and dynamic value")
-        if isinstance(value, dynamic):
-            dynamic_value = value.value
-            value = None
-        elif isinstance(dynamic_value, dynamic):
-            dynamic_value = dynamic_value.value
-        elif value is None and dynamic_value is None and backend.backend != "object":
+        self._shape = None
+        self._value = None
+        self.__value = None
+        self._valid = (None, None)
+        if value is None:
             if shape is None:
-                raise ParamConfigurationError("Either value or shape must be provided")
+                shape = ()
             if not isinstance(shape, (tuple, list)):
                 raise ParamConfigurationError("Shape must be a tuple")
             self.shape = tuple(shape)
-        elif (
-            not isinstance(value, (Param, Callable))
-            and value is not None
-            and backend.backend != "object"
-        ):
+        elif not isinstance(value, (Param, Callable)) and value is not None:
             value = backend.as_array(value, dtype=dtype, device=device)
-            if not (shape == () or shape is None or shape == value.shape):
+            if not valid_shape(shape, value.shape, batched):
                 raise ParamConfigurationError(
                     f"Shape {shape} does not match value shape {value.shape}"
                 )
-        elif (
-            not isinstance(dynamic_value, (Param, Callable))
-            and dynamic_value is not None
-            and backend.backend != "object"
-        ):
-            dynamic_value = backend.as_array(dynamic_value, dtype=dtype, device=device)
-            if not (shape == () or shape is None or shape == dynamic_value.shape):
-                raise ParamConfigurationError(
-                    f"Shape {shape} does not match dynamic value shape {dynamic_value.shape}"
-                )
-        self._type = "null"
         self._dtype = dtype
         self._device = device
-        self.value = value
-        if not hasattr(self, "_dynamic_value"):
-            self.dynamic_value = dynamic_value
-        self.cyclic = cyclic
+        self._cyclic = cyclic
+        self.batched = batched
+        self.shape = shape
+        if dynamic:
+            self.dynamic_value(value)
+        else:
+            self.value = value
         self.valid = valid
         self.units = units
 
     @property
     def dynamic(self) -> bool:
-        return "dynamic" in self._type
-
-    @dynamic.setter
-    def dynamic(self, dynamic: bool):
-        if dynamic:
-            self.to_dynamic()
-        else:
-            self.to_static()
+        return "dynamic" in self.node_type
 
     @property
     def pointer(self) -> bool:
-        return "pointer" in self._type
+        return "pointer" in self.node_type
 
     @property
     def static(self) -> bool:
-        return "static" in self._type
+        return "static" in self.node_type
 
-    @static.setter
-    def static(self, static: bool):
-        if static:
-            self.to_static()
-        else:
-            self.to_dynamic()
+    @property
+    def node_type(self):
+        return self._node_type
+
+    @node_type.setter
+    def node_type(self, value):
+        pre_type = self.node_type
+        if value == "dynamic" and self.__value is not None:
+            value = "dynamic value"
+        self._node_type = value
+        if pre_type != self.node_type:
+            self.update_graph()
 
     def to_dynamic(self, **kwargs):
         """Change this parameter to a dynamic parameter. If the parameter has a
-        value, this will be stored in the ``dynamic_value`` attribute."""
-        if self.dynamic:
-            return
+        value, this will become a "dynamic value" parameter."""
         if self.pointer:
             try:
-                eval_pointer = self._pointer_func(self)
-                self.dynamic_value = eval_pointer
-            except Exception as e:
-                self.value = None
-            return
-        self.dynamic_value = self.value
+                self.__value = self.__value(self)
+            except:
+                self.__value = None
+        self.node_type = "dynamic"
 
     def to_static(self, **kwargs):
         """Change this parameter to a static parameter. This only works if the
-        parameter has a ``dynamic_value`` set, or if the pointer can be
+        parameter has a dynamic value set, or if the pointer can be
         evaluated."""
         if self.static:
             return
         if self.pointer:
             try:
-                eval_pointer = self._pointer_func(self)
-                self.value = eval_pointer
-            except Exception as e:
+                self.__value = self.__value(self)
+            except:
                 raise ParamTypeError(
                     f"Cannot set pointer parameter {self.name} to static with `to_static`. Pointer could not be evaluated because of: \n"
                     + traceback.format_exc()
                 )
-
-            return
-        if self.dynamic_value is None:
+        if self.__value is None:
             raise ParamTypeError(
-                f"Cannot set dynamic parameter {self.name} to static when no `dynamic_value` is set"
+                f"Cannot set dynamic parameter {self.name} to static when no dynamic value is set. Try using `static_value(value)` to provide a value and set to static."
             )
-        self.value = self.dynamic_value
+        self.node_type = "static"
 
     @property
     def shape(self) -> Optional[tuple[int, ...]]:
-        if backend.backend == "object":
-            return None
-        if self.pointer and self.value is not None:
-            return tuple(self.value.shape)
+        try:
+            value = self.value
+        except:
+            value = None
+        if value is not None and (self.pointer or self._shape is None):
+            return tuple(value.shape)
         return self._shape
 
     @shape.setter
     def shape(self, shape):
-        if backend.backend == "object":
-            raise ParamTypeError("Cannot set shape of parameter with backend 'object'")
         if self.pointer:
             raise ParamTypeError(f"Cannot set shape of parameter {self.name} with type 'pointer'")
         if shape is None:
             self._shape = None
             return
-        self._shape = tuple(shape)
+        shape = tuple(shape)
+        value = self.value
+        if value is not None:
+            print(shape, value.shape, self.batched)
+        if value is not None and not valid_shape(shape, value.shape, self.batched):
+            raise ValueError(f"Shape {shape} does not match the shape of the value {value.shape}")
+        self._shape = shape
+
+    @property
+    def batch_shape(self) -> tuple[int]:
+        if not self.batched:
+            return ()
+        vshape = self.value.shape
+        return tuple(vshape[: len(vshape) - len(self.shape)])
+
+    @property
+    def batched(self) -> bool:
+        return self._batched
+
+    @batched.setter
+    def batched(self, value: bool):
+        self._batched = value
+        if not value:
+            try:
+                value = self.value
+                self.shape = value.shape
+            except:
+                pass
+
+    def _shape_from_value(self, value_shape):
+        if self._shape is None:
+            self._shape = value_shape
+        if not valid_shape(self._shape, value_shape, self.batched):
+            self._shape = value_shape
 
     @property
     def dtype(self) -> Optional[str]:
@@ -249,56 +251,80 @@ def device(self) -> Optional[str]:
                 pass
         return self._device
 
-    @property
-    def dynamic_value(self) -> Union[ArrayLike, None]:
-        return self._dynamic_value
+    def static_value(self, value):
+        # While active no value can be set
+        if self.active:
+            raise ActiveStateError(
+                f"Cannot set static value of parameter {self.name} while active."
+            )
+
+        # Catch cases where input is invalid
+        if value is None:
+            raise ParamTypeError("Cannot set to static with value of None")
+        if isinstance(value, Param) or callable(value):
+            raise ParamTypeError(
+                f"Cannot set static value to pointer ({self.name}). Try setting `pointer_func(func)` or `pointer_func(param)` to create a pointer."
+            )
+
+        value = backend.as_array(value, dtype=self._dtype, device=self._device)
+        self.__value = value
+        self.node_type = "static"
+        self._shape_from_value(tuple(value.shape))
+        self.is_valid()
 
-    @dynamic_value.setter
     def dynamic_value(self, value):
         # While active no value can be set
         if self.active:
             raise ActiveStateError(
-                f"Cannot set dynamic value of parameter {self.name} while active"
+                f"Cannot set dynamic value of parameter {self.name} while active."
             )
 
         # No dynamic value
         if value is None:
-            self._dynamic_value = None
+            self.__value = None
+            self.node_type = "dynamic"
             return
 
         # Catch cases where input is invalid
         if isinstance(value, Param) or callable(value):
             raise ParamTypeError(f"Cannot set dynamic value to pointer ({self.name})")
 
-        # unlink if pointer, dynamic_value cannot be a pointer
-        if self.pointer:
-            for child in tuple(self.children.values()):
-                self.unlink(child)
-
         # Set to dynamic value
-        self._type = "dynamic value"
-        self._pointer_func = None
         value = backend.as_array(value, dtype=self._dtype, device=self._device)
-        self._shape = tuple(value.shape) if backend.backend != "object" else None
-        self._dynamic_value = value
-        self._value = None
-        try:
-            self.valid = self._valid  # re-check valid range
-        except AttributeError:
-            pass
+        self.__value = value
+        self.node_type = "dynamic"
+        self._shape_from_value(tuple(value.shape))
+        self.is_valid()
+
+    def pointer_func(self, value: Union["Param", Callable]):
+        # While active no value can be set
+        if self.active:
+            raise ActiveStateError(
+                f"Cannot set pointer function of parameter {self.name} while active"
+            )
 
-        self.update_graph()
+        if isinstance(value, Param):
+            self.link(value)
+            p_name = value.name
+            value = lambda p: p[p_name].value
+        elif not callable(value):
+            raise ParamTypeError(f"Pointer function must be a Param or callable ({self.name})")
+        elif hasattr(value, "params"):
+            self.link(value.params)
+        self.__value = value
+        self._shape = None
+        self.node_type = "pointer"
 
     @property
     def value(self) -> Union[ArrayLike, None]:
-        if self.pointer and self._value is None:
+        if self._value is not None:
+            return self._value
+        if self.pointer:
+            value = self.__value(self)
             if self.active:
-                self._value = self._pointer_func(self)
-            else:
-                return self._pointer_func(self)
-        if self._value is None:
-            return self._dynamic_value
-        return self._value
+                self._value = value
+            return value
+        return self.__value
 
     @value.setter
     def value(self, value):
@@ -306,43 +332,14 @@ def value(self, value):
         if self.active:
             raise ActiveStateError(f"Cannot set value of parameter {self.name} while active")
 
-        # unlink if pointer to avoid floating references
-        if self.pointer:
-            for child in tuple(self.children.values()):
-                self.unlink(child)
-
         if value is None:
-            if hasattr(self, "_value") and self._value is not None:
-                self.dynamic_value = self._value
-                return
-            self._type = "dynamic"
-            self._pointer_func = None
-            self._value = None
-        elif isinstance(value, Param):
-            self._type = "pointer"
-            self.link(value)
-            self._pointer_func = lambda p: p[value.name].value
-            self._shape = None
-            self._value = None
-            self._dynamic_value = None
-        elif callable(value):
-            self._type = "pointer"
-            self._shape = None
-            self._pointer_func = value
-            self._value = None
-            self._dynamic_value = None
+            self.dynamic_value(None)
+        elif isinstance(value, Param) or callable(value):
+            self.pointer_func(value)
+        elif self.dynamic:
+            self.dynamic_value(value)
         else:
-            self._type = "static"
-            value = backend.as_array(value, dtype=self._dtype, device=self._device)
-            self._shape = tuple(value.shape) if backend.backend != "object" else None
-            self._value = value
-            self._dynamic_value = None
-            try:
-                self.valid = self._valid  # re-check valid range
-            except AttributeError:
-                pass
-
-        self.update_graph()
+            self.static_value(value)
 
     @property
     def npvalue(self) -> ndarray:
@@ -359,8 +356,6 @@ def to(self, device=None, dtype=None) -> "Param":
         dtype: (Optional[torch.dtype], optional)
             The desired data type. Defaults to None.
         """
-        if backend.backend == "object":
-            return self
         if device is not None:
             self._device = device
         else:
@@ -370,10 +365,8 @@ def to(self, device=None, dtype=None) -> "Param":
         else:
             dtype = self.dtype
         super().to(device=device, dtype=dtype)
-        if self.static:
-            self._value = backend.to(self._value, device=device, dtype=dtype)
-        if self._dynamic_value is not None:
-            self._dynamic_value = backend.to(self._dynamic_value, device=device, dtype=dtype)
+        if not self.pointer and self.__value is not None:
+            self.__value = backend.to(self.__value, device=device, dtype=dtype)
         valid = self.valid
         if valid[0] is not None:
             valid = (backend.to(valid[0], device=device, dtype=dtype), valid[1])
@@ -390,10 +383,7 @@ def cyclic(self) -> bool:
     @cyclic.setter
     def cyclic(self, cyclic: bool):
         self._cyclic = cyclic
-        try:
-            self.valid = self._valid
-        except AttributeError:
-            pass
+        self.valid = self.valid
 
     def _save_state_hdf5(self, h5group, appendable: bool = False, _done_save: set = None):
         super()._save_state_hdf5(h5group, appendable=appendable, _done_save=_done_save)
@@ -417,6 +407,7 @@ def _save_state_hdf5(self, h5group, appendable: bool = False, _done_save: set =
                     "value",
                     data=value,
                 )
+            self._h5group["value"].attrs["node_type"] = self.node_type
             self._h5group["value"].attrs["appendable"] = appendable
             self._h5group["value"].attrs["cyclic"] = self.cyclic
             if self.valid[0] is not None:
@@ -451,11 +442,16 @@ def _load_state_hdf5(self, h5group, index: int = -1, _done_load: set = None):
         if not self.pointer:
             if isinstance(h5group["value"][()], bytes):
                 assert h5group["value"][()] == b"None"
-                self.value = None
+                value = None
             elif h5group["value"].attrs["appendable"]:
-                self.value = h5group["value"][index]
+                value = h5group["value"][index]
             else:
-                self.value = h5group["value"][()]
+                value = h5group["value"][()]
+
+            if "static" in h5group["value"].attrs["node_type"]:
+                self.static_value(value)
+            elif "dynamic" in h5group["value"].attrs["node_type"]:
+                self.dynamic_value(value)
         self.units = h5group["value"].attrs["units"]
         if "valid_left" in h5group["value"].attrs:
             self.valid = (
@@ -475,11 +471,6 @@ def valid(self) -> tuple[Optional[ArrayLike], Optional[ArrayLike]]:
 
     @valid.setter
     def valid(self, valid: tuple[Union[ArrayLike, float, int, None]]):
-
-        if backend.backend == "object":
-            self._valid = (None, None)
-            return
-
         if valid is None:
             valid = (None, None)
 
@@ -487,34 +478,20 @@ def valid(self, valid: tuple[Union[ArrayLike, float, int, None]]):
             raise ParamConfigurationError(f"Valid must be a tuple ({self.name})")
         if len(valid) != 2:
             raise ParamConfigurationError(f"Valid must be a tuple of length 2 ({self.name})")
+        if self.cyclic and (valid[0] is None or valid[1] is None):
+            raise ParamConfigurationError(f"valid must be set for cyclic parameter ({self.name})")
 
         if valid[0] is None and valid[1] is None:
-            if self.cyclic:
-                raise ParamConfigurationError(
-                    f"Cannot set valid to None for cyclic parameter ({self.name})"
-                )
             self.to_valid = self._to_valid_base
             self.from_valid = self._from_valid_base
         elif valid[0] is None:
-            if self.cyclic:
-                raise ParamConfigurationError(
-                    f"Cannot set left valid to None for cyclic parameter ({self.name})"
-                )
             self.to_valid = self._to_valid_rightvalid
             self.from_valid = self._from_valid_rightvalid
             valid = (None, backend.as_array(valid[1], dtype=self.dtype, device=self.device))
-            if not self.pointer and self.value is not None and backend.any(self.value > valid[1]):
-                warn(InvalidValueWarning(self.name, self.value, valid))
         elif valid[1] is None:
-            if self.cyclic:
-                raise ParamConfigurationError(
-                    f"Cannot set right valid to None for cyclic parameter ({self.name})"
-                )
             self.to_valid = self._to_valid_leftvalid
             self.from_valid = self._from_valid_leftvalid
             valid = (backend.as_array(valid[0], dtype=self.dtype, device=self.device), None)
-            if not self.pointer and self.value is not None and backend.any(self.value < valid[0]):
-                warn(InvalidValueWarning(self.name, self.value, valid))
         else:
             if self.cyclic:
                 self.to_valid = self._to_valid_cyclic
@@ -528,17 +505,26 @@ def valid(self, valid: tuple[Union[ArrayLike, float, int, None]]):
             )
             if backend.any(valid[0] >= valid[1]):
                 raise ParamConfigurationError(
-                    f"Valid range (valid[1] - valid[0]) must be positive ({self.name})"
+                    f"Valid range (valid[1] - valid[0]) must be strictly positive ({self.name})"
                 )
-            if (
-                not self.pointer
-                and self.value is not None
-                and not self.cyclic
-                and (backend.any(self.value < valid[0]) or backend.any(self.value > valid[1]))
-            ):
-                warn(InvalidValueWarning(self.name, self.value, valid))
 
         self._valid = valid
+        self.is_valid()
+
+    def is_valid(self, value=None) -> bool:
+        if self.cyclic or self.pointer:
+            return True
+        if value is None:
+            value = self.value
+        if value is None:
+            return True
+        if self.valid[0] is not None and backend.any(self.value < self.valid[0]):
+            warn(InvalidValueWarning(self.name, value, self.valid))
+            return False
+        elif self.valid[1] is not None and backend.any(self.value > self.valid[1]):
+            warn(InvalidValueWarning(self.name, value, self.valid))
+            return False
+        return True
 
     def _to_valid_base(self, value: ArrayLike) -> ArrayLike:
         return value
@@ -584,12 +570,24 @@ def node_str(self) -> str:
         """
         Returns a string representation of the node for graph visualization.
         """
-        if (self.static or self._type == "dynamic value") and backend.backend != "object":
-            if max(1, prod(self.value.shape)) == 1:
-                return f"{self.name}|{self._type}: {self.npvalue.item():.3g}"
+        if self.pointer:
+            try:
+                value = self.value
+            except:
+                value = None
+        else:
+            value = self.value
+        if value is not None:
+            value = backend.to_numpy(value)
+
+            if max(1, prod(value.shape)) == 1:
+                return f"{self.name}|{self.node_type}: {value.item():.3g}"
+            elif prod(value.shape) <= 4:
+                value = str(np.char.mod("%.3g", value).tolist()).replace("'", "")
+                return f"{self.name}|{self.node_type}: {value}"
             else:
-                return f"{self.name}|{self._type}: {self.shape}"
-        return f"{self.name}|{self._type}"
+                return f"{self.name}|{self.node_type}: {self.shape}"
+        return f"{self.name}|{self.node_type}"
 
     def __repr__(self) -> str:
         return self.name
diff --git a/src/caskade/tests.py b/src/caskade/tests.py
index 30935f3..6626e25 100644
--- a/src/caskade/tests.py
+++ b/src/caskade/tests.py
@@ -35,8 +35,6 @@ def __call__(self, d=None, e=None, f=None):
     main1.c = main1.b
     sub1.f = main1.c
 
-    if backend.backend == "object":
-        return
     b_value = backend.make_array(3.0)
     res = main1.testfun(1.0, params=[b_value])
     assert res.item() == 13.0
diff --git a/tests/test_base.py b/tests/test_base.py
index 5618f43..1d6494c 100644
--- a/tests/test_base.py
+++ b/tests/test_base.py
@@ -11,7 +11,7 @@ def test_creation():
     assert node._children == {}
     assert node._parents == set()
     assert node._active == False
-    assert node._type == "node"
+    assert node.node_type == "node"
 
     with pytest.raises(AttributeError):
         node.name = "newname"
@@ -96,12 +96,9 @@ def test_topological_ordering():
     ordering = node1.topological_ordering()
     assert ordering == (node1, node2, node4, node5, node3, node6)
 
-    ordering = node1.topological_ordering(with_type="node")
+    ordering = node1.topological_ordering()
     assert ordering == (node1, node2, node4, node5, node3, node6)
 
-    ordering = node1.topological_ordering(with_type="dynamic")
-    assert ordering == ()
-
     graph = node1.graphviz()
     assert graph is not None, "should return a graphviz object"
 
diff --git a/tests/test_context.py b/tests/test_context.py
index 58b7dc9..f098140 100644
--- a/tests/test_context.py
+++ b/tests/test_context.py
@@ -1,5 +1,4 @@
 from caskade import Module, Param, forward, ActiveContext, OverrideParam, backend, active_cache
-import numpy as np
 
 
 def test_active_context():
@@ -16,8 +15,6 @@ def testfunc(self, a, b, c):
             return a + b + c
 
     testsim = TestSim()
-    if backend.backend == "object":
-        return
     params1 = backend.make_array([2.0, 3.0])
     params2 = backend.make_array([4.0, 5.0])
     with ActiveContext(testsim):
@@ -40,9 +37,6 @@ def testfunc(self, a, b, c):
 
 def test_override_param():
 
-    if backend.backend == "object":
-        return
-
     class TestSim(Module):
         def __init__(self):
             super().__init__()
@@ -70,10 +64,8 @@ def testfunc(self):
     assert testsim.testfunc(backend.make_array([5.0])).item() == 27.0
     assert testsim.a.value.item() == 3.0
 
-def test_active_cache():
-    if backend.backend == "object":
-        return
 
+def test_active_cache():
     class TestSim(Module):
         def __init__(self):
             super().__init__()
@@ -83,16 +75,21 @@ def __init__(self):
         @forward
         def testcache(self, x, a):
             return x + a
-        
+
         @active_cache
         def testonlycache(self, x):
             return 2 * x
-        
+
         @forward
         def testfunc(self):
-            return self.testcache(1.0) + self.testcache(2.0) + self.testonlycache(3.0) + self.testonlycache(4.0)
+            return (
+                self.testcache(1.0)
+                + self.testcache(2.0)
+                + self.testonlycache(3.0)
+                + self.testonlycache(4.0)
+            )
 
     testsim = TestSim()
     assert testsim.testfunc().item() == 20.0
     assert testsim.testonlycache(5.0) == 10.0
-    assert testsim.testonlycache(6.0) == 12.0
\ No newline at end of file
+    assert testsim.testonlycache(6.0) == 12.0
diff --git a/tests/test_forward.py b/tests/test_forward.py
index 5d27cbc..71feaa6 100644
--- a/tests/test_forward.py
+++ b/tests/test_forward.py
@@ -53,9 +53,6 @@ def __call__(self, d=None, e=None, live_c=None):
     with pytest.raises(FillDynamicParamsError):
         main1.testfun()
 
-    if backend.backend == "object":
-        return
-
     # List as params
     params = [
         backend.module.ones((2, 2)),
@@ -212,6 +209,7 @@ def __call__(self, d=None, e=None, live_c=None):
     sub1.d = backend.make_array(3.0)
     sub1.e = backend.make_array(4.0)
     sub1.f = backend.make_array(1.0)
+    main1.to_static(False)
     result = main1.testfun(1.0)
     assert result.shape == (2, 2)
     result = main1.testfun(1.0, [])
diff --git a/tests/test_integration.py b/tests/test_integration.py
index fff95ab..474440f 100644
--- a/tests/test_integration.py
+++ b/tests/test_integration.py
@@ -1,5 +1,3 @@
-import torch
-
 from caskade import Module, Param, forward, backend
 
 
@@ -30,14 +28,12 @@ def __init__(self, d, e, f):
         def __call__(self, d=None, e=None, f=None):
             return d + e + f
 
-    sub1 = TestSubSim(d=1.0, e=lambda s: s.children["flink"].value, f=None)
+    sub1 = TestSubSim(d=1.0, e=lambda s: s.flink.value, f=None)
     sub1.e.link("flink", sub1.f)
     main1 = TestSim(a=2.0, b=None, c=None, c_shape=(), m1=sub1)
     main1.c = main1.b
     sub1.f = main1.c
 
-    if backend.backend == "object":
-        return
     main1.to(dtype=backend.module.float32)
 
     b_value = backend.make_array(3.0)
@@ -91,13 +87,12 @@ def myutilityfunction(self, z, u=None):
             return u * z
 
     util = MyUtilitySim("util")
-    #                      u for MyUtilitySim
-    params = [backend.make_array(1.0)]
+    util.u = 1.0
     actions = []
     for i in range(3):
         actions.append(MyActionSim(f"action_{i}", util))
-        #                     a for MyActionSim, b for MyActionSim
-        params = params + [backend.make_array(i), backend.make_array(i + 1)]
+        actions[-1].a = i
+        actions[-1].b = i + 1
 
     main = MyMainSim("main", util, actions)
 
@@ -106,13 +101,12 @@ def myutilityfunction(self, z, u=None):
     main.d_param.link("c", main.c_param)
 
     #                      c for MyMainSim
-    params = params + [backend.make_array(3.0)]
+    main.c_param = 3.0
 
-    if backend.backend == "object":
-        return
-    assert main.mymainfunction(1.0, params).item() == 558.0
+    assert main.mymainfunction(1.0, main.build_params_array()).item() == 558.0
 
     main.c_param = [[1, 2], [1, 3]]  # test print param with shape
     print(main)
+    print(main.param_order())
     graph = main.graphviz()
     assert graph is not None, "should return a graphviz object"
diff --git a/tests/test_module.py b/tests/test_module.py
index 43867de..505eeea 100644
--- a/tests/test_module.py
+++ b/tests/test_module.py
@@ -10,7 +10,6 @@
     InvalidValueWarning,
     forward,
     backend,
-    BackendError,
     ValidContext,
 )
 
@@ -85,8 +84,6 @@ def big_test(self):
             return self.m1.test() + self.m2.test()
 
     c1 = CombineModules("c1", m1, m2)
-    if backend.backend == "object":
-        return
     assert c1.big_test([backend.make_array(1.0)]).item() == 4.0, "Shared parameter not working"
 
 
@@ -98,7 +95,7 @@ def __init__(self, a, b_shape, c, m1):
             super().__init__("test_sim")
             self.a = Param("a", a)
             self.b = Param("b", None, b_shape)
-            self.c = Param("c", dynamic_value=c)
+            self.c = Param("c", value=c, dynamic=True)
             self.m1 = m1
 
         @forward
@@ -109,9 +106,9 @@ def testfun(self, x, a=None, b=None, c=None):
     class TestSubSim(Module):
         def __init__(self, d=None, e=None, f=None):
             super().__init__()
-            self.d = Param("d", dynamic_value=d)
+            self.d = Param("d", value=d, dynamic=True)
             self.e = Param("e", e)
-            self.f = Param("f", dynamic_value=f, valid=(0, 10))
+            self.f = Param("f", value=f, dynamic=True, valid=(0, 10))
 
         @forward
         def __call__(self, d=None, e=None, live_c=None):
@@ -120,18 +117,8 @@ def __call__(self, d=None, e=None, live_c=None):
     sub1 = TestSubSim(d=2.0, e=2.5, f=None)
     main1 = TestSim(a=1.0, b_shape=(2,), c=4.0, m1=sub1)
 
-    assert not main1.all_dynamic_value
-    main1.b = backend.make_array([1.0, 2.0])
-    if backend.backend == "object":
-        with pytest.raises(BackendError):
-            main1.testfun(np.array([1.0, 2.0]), np.ones(3))
-        with pytest.raises(BackendError):
-            main1.build_params_array()
-        x = main1.to_valid(np.array([1, 2, 3]))
-        assert x[1] == 2.0
-        x = main1.from_valid(x)
-        assert x[1] == 2.0
-        return
+    main1.b.static_value(backend.make_array([1.0, 2.0]))
+
     # Try to get auto params when not all dynamic values available
     with pytest.raises(ParamConfigurationError):
         p00 = main1.build_params_array()
@@ -141,11 +128,9 @@ def __call__(self, d=None, e=None, live_c=None):
         p00 = main1.build_params_dict()
     with pytest.raises(ParamConfigurationError):
         p00 = sub1.build_params_dict()
-    sub1.f.dynamic_value = 3.0
-    assert main1.all_dynamic_value
+    sub1.f.dynamic_value(3.0)
 
     # Check dynamic value
-    assert main1.c.dynamic_value.item() == 4.0
     assert main1.c.value.item() == 4.0
     assert main1.c._value is None
 
@@ -198,7 +183,7 @@ def __call__(self, d=None, e=None, live_c=None):
 
     # Check invalid dynamic value
     with pytest.warns(InvalidValueWarning):
-        sub1.f.dynamic_value = 11.0
+        sub1.f.dynamic_value(11.0)
 
     # All static make params
     main1.c.to_static()
@@ -232,30 +217,30 @@ def __call__(self, d=None, e=None, live_c=None):
 
 
 def test_batched_build_params_array():
-    if backend.backend == "object":
-        return
     M = Module("M")
     M.p1 = Param("p1")
     M.p2 = Param("p2")
 
-    M.p1.dynamic_value = [1.0, 2.0]
+    M.p1.dynamic_value([1.0, 2.0])
+    M.p1.batched = True
     M.p1.shape = ()
-    M.p2.dynamic_value = [3.0, 4.0]
+    M.p2.dynamic_value([3.0, 4.0])
+    M.p2.batched = True
     M.p2.shape = ()
 
     a = M.build_params_array()
     assert a.shape == (2, 2)
 
     with pytest.raises(ParamConfigurationError):
-        M.p1.dynamic_value = [1.0, 2.0]
+        M.p1.dynamic_value([1.0, 2.0])
         M.p1.shape = (2,)
-        M.p2.dynamic_value = [3.0, 4.0]
+        M.p2.dynamic_value([3.0, 4.0])
         M.p2.shape = ()
         M.build_params_array()
     with pytest.raises(ParamConfigurationError):
-        M.p1.dynamic_value = [1.0, 2.0]
+        M.p1.dynamic_value([1.0, 2.0])
         M.p1.shape = ()
-        M.p2.dynamic_value = [1.0, 2.0]
+        M.p2.dynamic_value([1.0, 2.0])
         M.p2.shape = (2,)
         M.build_params_array()
 
@@ -301,8 +286,6 @@ def test_module_and_collection():
 
 
 def test_valid():
-    if backend.backend == "object":
-        return
     M = Module("M")
     p1 = Param("p1", 1.0, valid=(0, None))
     M.p1 = p1
@@ -340,6 +323,3 @@ def test_valid():
         assert np.isclose(M.p2.value[1].item(), 1.5)
         assert np.isclose(M.m2.p3.value[0][1].item(), 1.1)
         assert np.isclose(M.m2.m3.p2.value[1].item(), 1.5)
-
-    with pytest.raises(TypeError):
-        M.valid_context = None
diff --git a/tests/test_param.py b/tests/test_param.py
index 3991180..8ab5bfa 100644
--- a/tests/test_param.py
+++ b/tests/test_param.py
@@ -9,7 +9,6 @@
     GraphError,
     InvalidValueWarning,
     LinkToAttributeError,
-    dynamic,
     backend,
 )
 
@@ -25,20 +24,14 @@ def test_param_creation():
     # Name and value
     p2 = Param("test", 1.0)
     assert p2.name == "test"
-    if backend.backend == "object":
-        with pytest.raises(ParamTypeError):
-            p2.shape = (1, 2, 3)
-        assert p2.shape is None
-        p2 = p2.to()
-        return
     assert p2.value.item() == 1.0
     p3 = Param("test", backend.module.ones((1, 2, 3)))
-    p33 = Param("test", dynamic_value=backend.module.ones((1, 2, 3)))
+    p33 = Param("test", value=backend.module.ones((1, 2, 3)), dynamic=True)
     assert backend.all(p3.value == p33.value)
-    p33v2 = Param("test", dynamic(backend.module.ones((3, 2, 1))))
+    p33v2 = Param("test", backend.module.ones((3, 2, 1)), dynamic=True)
     assert p33v2.dynamic
     assert p33v2.value.shape == (3, 2, 1)
-    p33v3 = Param("test", dynamic_value=dynamic(backend.module.ones((3, 2, 1))))
+    p33v3 = Param("test", value=backend.module.ones((3, 2, 1)), dynamic=True)
     assert p33v3.dynamic
     assert p33v3.value.shape == (3, 2, 1)
 
@@ -48,13 +41,13 @@ def test_param_creation():
         p3.value = 1.0
     with pytest.raises(ActiveStateError):
         p33.active = True
-        p33.dynamic_value = 1.0
+        p33.dynamic_value(1.0)
 
     # Missmatch value and shape
     with pytest.raises(ParamConfigurationError):
         p4 = Param("test", 1.0, shape=(1, 2, 3))
     with pytest.raises(ParamConfigurationError):
-        p44 = Param("test", dynamic_value=1.0, shape=(1, 2, 3))
+        p44 = Param("test", value=1.0, dynamic=True, shape=(1, 2, 3))
 
     # Cant set shape of pointer or function
     p5 = Param("test", p3)
@@ -67,10 +60,6 @@ def test_param_creation():
     with pytest.raises(ParamTypeError):
         p6.shape = (1, 2, 3)
 
-    # Missing value and shape
-    with pytest.raises(ParamConfigurationError):
-        p7 = Param("test", None, None)
-
     # Shape is not a tuple
     with pytest.raises(ParamConfigurationError):
         p8 = Param("test", None, 7)
@@ -92,40 +81,28 @@ def test_param_creation():
 
     # Invalid dynamic value
     with pytest.raises(ParamTypeError):
-        p10 = Param("test", dynamic_value=p9)
+        p10 = Param("test", value=p9, dynamic=True)
     with pytest.raises(ParamTypeError):
-        p11 = Param("test", dynamic_value=lambda p: p.other.value * 2)
-    with pytest.raises(ParamConfigurationError):
-        p12 = Param("test", value=1.0, dynamic_value=1.0)
+        p11 = Param("test", value=lambda p: p.other.value * 2, dynamic=True)
 
     # Set dynamic from other states
     p13 = Param("test", 1.0)  # static
-    p13.dynamic_value = 2.0
+    p13.dynamic_value(2.0)
     assert p13.value.item() == 2.0
     assert p13.dynamic
     p14 = Param("test")  # dynamic
-    p14.dynamic_value = 1.0
+    p14.dynamic_value(1.0)
     assert p14.value.item() == 1.0
     p15 = Param("test", p14)  # pointer
-    p15.dynamic_value = 2.0
+    p15.dynamic_value(2.0)
     assert p15.value.item() == 2.0
     p16 = Param("test", 1.0)  # static
-    p16.value = None
-    assert p16.dynamic
-    assert p16.dynamic_value.item() == 1.0
-    p16.dynamic = False
-    assert p16.static
-    p16.dynamic = True
-    assert p16.dynamic
-    p16.static = True
-    assert p16.static
-    p16.static = False
+    p16.to_dynamic()
     assert p16.dynamic
+    assert p16.value.item() == 1.0
 
 
 def test_param_to():
-    if backend.backend == "object":
-        return
     if backend.backend == "jax":
         device = backend.jax.devices()[0]
         backend.jax.config.update("jax_enable_x64", True)
@@ -136,13 +113,11 @@ def test_param_to():
     p = Param("test", 1.0, valid=(0, 2))
     p = p.to(dtype=backend.module.float64, device=device)
     # dynamic value
-    p = Param("test", dynamic_value=1.0, valid=(0, 2))
+    p = Param("test", value=1.0, dynamic=True, valid=(0, 2))
     p = p.to(dtype=backend.module.float64, device=device)
 
 
 def test_params_sticky_to():
-    if backend.backend == "object":
-        return
     if backend.backend == "jax":
         device = backend.jax.devices()[0]
         backend.jax.config.update("jax_enable_x64", True)
@@ -154,11 +129,11 @@ def test_params_sticky_to():
     p.value = 2.0  # value cast to float64
     assert p.value.dtype == backend.module.float64
     # dynamic value
-    p = Param("test", dynamic_value=1.0, dtype=backend.module.float32)
+    p = Param("test", value=1.0, dynamic=True, dtype=backend.module.float32)
     assert p.value.dtype == backend.module.float32
     p = p.to(dtype=backend.module.float64, device=device)
     assert p.value.dtype == backend.module.float64
-    p.dynamic_value = np.array([1.0, 2.0, 3.0], dtype=np.float32)
+    p.dynamic_value(np.array([1.0, 2.0, 3.0], dtype=np.float32))
     assert p.value.dtype == backend.module.float64
     # neither dtype or value set
     p = Param("test", valid=(0, 2))
@@ -182,30 +157,73 @@ def test_value_setter():
 
     # dynamic
     p = Param("test")
-    assert p._type == "dynamic"
+    assert p.node_type == "dynamic"
 
     # static
-    p.value = 1.0
-    assert p._type == "static"
-    if backend.backend == "object":
-        return
+    p.static_value(1.0)
+    assert p.node_type == "static"
     assert p.value.item() == 1.0
 
     p = Param("testshape", shape=(2,))
     p.value = [1.0, 2.0]
 
     # pointer
-    other = Param("testother", 2.0)
+    other = Param("other", 2.0)
     p.value = other
-    assert p._type == "pointer"
+    assert p.node_type == "pointer"
     assert p.shape == other.shape
 
     # function
-    p.value = lambda p: p.other.value * 2
-    p.link("other", other)
-    assert p._type == "pointer"
+    def test_times_2(p):
+        return p.other.value * 2
+
+    test_times_2.params = (other,)
+    p.value = test_times_2
+    assert p.node_type == "pointer"
     assert p.value.item() == 4.0
 
+    # Invalid pointer
+    with pytest.raises(ParamTypeError):
+        p.pointer_func(1.0)
+    with pytest.raises(ParamTypeError):
+        p.pointer_func(None)
+
+    # Invalid static value
+    with pytest.raises(ParamTypeError):
+        p.static_value(None)
+
+    with pytest.raises(ParamTypeError):
+        p.static_value(lambda p: p.other.value)
+
+    # Cannot update while active
+    p.active = True
+    with pytest.raises(ActiveStateError):
+        p.dynamic_value(1.0)
+    with pytest.raises(ActiveStateError):
+        p.static_value(1.0)
+    with pytest.raises(ActiveStateError):
+        p.pointer_func(lambda p: p.other.value)
+
+
+def test_param_shape():
+    p = Param("p", [1, 2])
+    assert p.shape == (2,)
+
+    with pytest.raises(ValueError):
+        p.shape = (3, 2)
+
+    p.value = np.ones((3, 2))
+
+    with pytest.raises(ValueError):
+        p.shape = (2,)
+    p.batched = True
+    p.shape = (2,)
+    assert p.batch_shape == (3,)
+
+    p.value = lambda p: p.other.value
+    p.batched = False
+    assert p.shape is None
+
 
 def test_to_dynamic_static():
 
@@ -215,15 +233,13 @@ def test_to_dynamic_static():
     p = Param("test")
     p.to_dynamic()  # from dynamic
     assert p.dynamic
-    p.dynamic_value = 1.0
+    p.dynamic_value(1.0)
     assert p.dynamic
     p.to_dynamic()  # from dynamic with dynamic value
     assert p.dynamic
     p.value = 2.0
     p.to_dynamic()  # from static
     assert p.dynamic
-    if backend.backend == "object":
-        return
     assert p.value.item() == 2.0
     p.value = lambda p: p["other"].value * 2
     p.to_dynamic()  # from pointer, fails
@@ -242,7 +258,7 @@ def test_to_dynamic_static():
     p = Param("test")
     with pytest.raises(ParamTypeError):
         p.to_static()  # from dynamic, fails
-    p.dynamic_value = 2.0
+    p.dynamic_value(2.0)
     p.to_static()  # from dynamic with dynamic value
     assert p.static
     assert p.value.item() == 2.0
@@ -262,8 +278,6 @@ def test_units():
 
 def test_valid():
     p = Param("test", valid=None)
-    if backend.backend == "object":
-        return
 
     v = backend.make_array(0.5)
     assert p.to_valid(v) == v, "valid value should not change"
@@ -326,3 +340,12 @@ def test_valid():
         p.valid = (0, None)
     with pytest.warns(InvalidValueWarning):
         p.valid = (None, -2)
+
+
+def test_node_str():
+    p = Param("p", 1.0)
+    assert p.node_str == "p|static: 1"
+    p = Param("p", [1.0, 2.0])
+    assert p.node_str == "p|static: [1, 2]"
+    p = Param("p", [1.0, 2.0, 3.0, 4.0, 5.0])
+    assert p.node_str == "p|static: (5,)"
diff --git a/tests/test_save.py b/tests/test_save.py
index 31dc966..6a0d889 100644
--- a/tests/test_save.py
+++ b/tests/test_save.py
@@ -61,12 +61,6 @@ def _make_files_and_test(usefileobject=False):
                 main.save_state(f, appendable=False)
         else:
             main.save_state("test_save_notappend.h5", appendable=False)
-    if backend.backend == "object":
-        with pytest.raises(BackendError):
-            main.save_state("test_save_badappend.h5", appendable=True)
-        with pytest.raises(BackendError):
-            main.append_state("test_save_badappend.h5")
-        return
 
     # bad file
     with pytest.raises(NotImplementedError):
@@ -157,9 +151,6 @@ def test_save_append_load(usefileobject):
     # Load not appendable
     _load_not_appendable_and_test(usefileobject=usefileobject)
 
-    if backend.backend == "object":
-        return
-
     # Load appendable
     _load_appendable_and_test(usefileobject=usefileobject)