diff --git a/scripts/jax_likelihood_functions/imaging/mge_pytree.py b/scripts/jax_likelihood_functions/imaging/mge_pytree.py
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+++ b/scripts/jax_likelihood_functions/imaging/mge_pytree.py
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+"""
+Path A PoC: jit-wrap ``analysis.fit_from`` for MGE
+==================================================
+
+Exercises Path A of the ``fit-imaging-pytree`` task (issue
+https://github.com/PyAutoLabs/PyAutoLens/issues/444). We take the same MGE
+parametric lens + MGE source model from ``mge.py`` and wrap
+``analysis.fit_from`` in ``jax.jit`` so the returned ``FitImaging`` (and every
+nested autoarray / galaxy / tracer type it carries) has to round-trip through
+JAX's pytree machinery.
+
+The first failure we hit on each run is the next type that needs pytree
+registration. This script is the iteration driver for that work.
+
+Success criteria for this PoC (not all satisfied yet — step-by-step):
+  - ``jax.jit(analysis.fit_from)(instance)`` returns a ``FitImaging``.
+  - ``fit.log_likelihood`` is a ``jax.Array`` matching the NumPy-path scalar.
+"""
+
+from os import path
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+
+import autofit as af
+import autolens as al
+
+# Register PyAutoFit priors, Model, Collection, ModelInstance as pytrees.
+from autofit.jax.pytrees import enable_pytrees, register_model
+
+enable_pytrees()
+
+
+"""
+__Dataset__
+"""
+dataset_path = path.join("dataset", "imaging", "jax_test")
+
+if al.util.dataset.should_simulate(dataset_path):
+    import subprocess
+    import sys
+
+    subprocess.run(
+        [sys.executable, "scripts/jax_likelihood_functions/imaging/simulator.py"],
+        check=True,
+    )
+
+dataset = al.Imaging.from_fits(
+    data_path=path.join(dataset_path, "data.fits"),
+    psf_path=path.join(dataset_path, "psf.fits"),
+    noise_map_path=path.join(dataset_path, "noise_map.fits"),
+    pixel_scales=0.2,
+)
+
+mask_radius = 3.5
+
+mask = al.Mask2D.circular(
+    shape_native=dataset.shape_native,
+    pixel_scales=dataset.pixel_scales,
+    radius=mask_radius,
+)
+
+dataset = dataset.apply_mask(mask=mask)
+dataset = dataset.apply_over_sampling(over_sample_size_lp=4)
+
+over_sample_size = al.util.over_sample.over_sample_size_via_radial_bins_from(
+    grid=dataset.grid,
+    sub_size_list=[4, 2, 1],
+    radial_list=[0.3, 0.6],
+    centre_list=[(0.0, 0.0)],
+)
+dataset = dataset.apply_over_sampling(over_sample_size_lp=over_sample_size)
+
+
+"""
+__Model__ (same MGE + NFWSph + ExternalShear lens, MGE source as ``mge.py``)
+"""
+bulge = al.model_util.mge_model_from(
+    mask_radius=mask_radius, total_gaussians=20, centre_prior_is_uniform=True
+)
+
+mass = af.Model(al.mp.NFWSph)
+
+total_gaussians = 3
+mask_radius = 3.0
+log10_sigma_list = np.linspace(-2, np.log10(mask_radius), total_gaussians)
+
+centre_0 = af.UniformPrior(lower_limit=-0.1, upper_limit=0.1)
+centre_1 = af.UniformPrior(lower_limit=-0.1, upper_limit=0.1)
+
+gaussian_list = af.Collection(
+    af.Model(al.lmp_linear.GaussianGradient) for _ in range(total_gaussians)
+)
+for i, gaussian in enumerate(gaussian_list):
+    gaussian.centre.centre_0 = centre_0
+    gaussian.centre.centre_1 = centre_1
+    gaussian.ell_comps = gaussian_list[0].ell_comps
+    gaussian.sigma = 10 ** log10_sigma_list[i]
+    gaussian.mass_to_light_ratio = 10.0
+    gaussian.mass_to_light_gradient = 1.0
+
+bulge_gaussian_list = list(gaussian_list)
+
+bulge = af.Model(
+    al.lp_basis.Basis,
+    profile_list=bulge_gaussian_list,
+)
+
+shear = af.Model(al.mp.ExternalShear)
+
+lens = af.Model(al.Galaxy, redshift=0.5, bulge=bulge, mass=mass, shear=shear)
+
+bulge_source = al.model_util.mge_model_from(
+    mask_radius=mask_radius, total_gaussians=20, centre_prior_is_uniform=False
+)
+
+source = af.Model(al.Galaxy, redshift=1.0, bulge=bulge_source)
+
+model = af.Collection(galaxies=af.Collection(lens=lens, source=source))
+
+
+"""
+__Analysis__ — on the JAX path so ``fit_from`` builds with ``xp=jnp``.
+"""
+analysis = al.AnalysisImaging(dataset=dataset, use_jax=True)
+
+
+"""
+Walk the model and register every user class (``Galaxy``, light/mass profiles,
+etc.) with ``jax.tree_util`` so ``ModelInstance`` can flatten to leaves.
+"""
+register_model(model)
+
+instance = model.instance_from_prior_medians()
+
+
+"""
+__NumPy reference scalar__ (for cross-check once the JIT path returns).
+"""
+analysis_np = al.AnalysisImaging(dataset=dataset, use_jax=False)
+fit_np = analysis_np.fit_from(instance=instance)
+print("NumPy fit.log_likelihood:", float(fit_np.log_likelihood))
+
+
+"""
+__Path A: jit-wrap ``analysis.fit_from``__
+
+The first run exposes whichever type in the ``FitImaging`` closure is not yet
+pytree-registered. Subsequent commits register each offender until the jit
+succeeds and returns a ``FitImaging`` with ``jax.Array`` leaves.
+"""
+fit_jit_fn = jax.jit(analysis.fit_from)
+fit = fit_jit_fn(instance)
+
+print("JIT fit type:", type(fit).__name__)
+print("JIT fit.log_likelihood:", fit.log_likelihood)
+assert isinstance(fit.log_likelihood, jnp.ndarray), (
+    f"expected jax.Array, got {type(fit.log_likelihood)}"
+)
+np.testing.assert_allclose(
+    float(fit.log_likelihood), float(fit_np.log_likelihood), rtol=1e-4
+)
+
+print("PASS: jit(fit_from) round-trip matches NumPy scalar.")