How to use the netket.graph.Hypercube function in NetKet

import netket as nk
import networkx as nx
import igraph as ig
import math

nxg = nx.star_graph(10)
graphs = [
    nk.graph.Hypercube(length=10, n_dim=1, pbc=True),
    nk.graph.Hypercube(length=4, n_dim=2, pbc=True),
    nk.graph.Hypercube(length=5, n_dim=1, pbc=False),
    nk.graph.CustomGraph(nxg.edges()),
    nk.graph.Lattice(
        basis_vectors=[[1.0, 0.0], [1.0 / 2.0, math.sqrt(3) / 2.0]],
        extent=[10, 10],
        pbc=[0, 0],
        atoms_coord=[[0, 0]],
    ),
    nk.graph.Lattice(
        basis_vectors=[[1.5, math.sqrt(3) / 2.0], [0, math.sqrt(3)]],
        extent=[3, 5],
        atoms_coord=[[0, 0], [1, 0]],
    ),
    nk.graph.Lattice(
        basis_vectors=[[2.0, 0.0], [1.0, math.sqrt(3)]],
        extent=[4, 4],
        atoms_coord=[[0, 0], [1.0 / 2.0, math.sqrt(3) / 2.0], [1.0, 0.0]],

def _setup_vmc():
    g = nk.graph.Hypercube(length=8, n_dim=1)
    hi = nk.hilbert.Spin(s=0.5, graph=g)

    ma = nk.machine.RbmSpin(hilbert=hi, alpha=1)
    ma.init_random_parameters(seed=SEED, sigma=0.01)

    ha = nk.operator.Ising(hi, h=1.0)
    sa = nk.sampler.MetropolisLocal(machine=ma)
    sa.seed(SEED)
    op = nk.optimizer.Sgd(learning_rate=0.1)

    vmc = nk.variational.Vmc(
        hamiltonian=ha, sampler=sa, optimizer=op, n_samples=500, diag_shift=0.01
    )

    # Add custom observable
    X = [[0, 1], [1, 0]]

def _setup():
    g = nk.graph.Hypercube(3, 2)
    hi = nk.hilbert.Spin(g, 0.5)

    ham = nk.operator.Heisenberg(hi)

    ma = nk.machine.RbmSpin(hi, alpha=2)
    ma.init_random_parameters()

    return hi, ham, ma

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import cProfile


import netket as nk
import numpy as np

from netket.operator import local_values

# 1D Lattice
g = nk.graph.Hypercube(length=20, n_dim=1, pbc=True)

# Hilbert space of spins on the graph
hi = nk.hilbert.Spin(s=0.5, graph=g)

# Ising spin hamiltonian
ha = nk.operator.Ising(h=1.0, hilbert=hi)

# RBM Spin Machine
ma = nk.machine.RbmSpin(alpha=1, hilbert=hi)
ma.init_random_parameters(seed=1234, sigma=0.01)

# Metropolis Local Sampling
sa = nk.sampler.MetropolisLocal(machine=ma, n_chains=8)

n_samples = 1000
samples = np.zeros((n_samples, sa.sample_shape[0], sa.sample_shape[1]))

import numpy as np
import netket as nk

# Exact ground state energy of AKLT model is zero by construction, see above references.

Sz = [[1, 0, 0], [0, 0, 0], [0, 0, -1]]
Sup = [[0, np.sqrt(2), 0], [0, 0, np.sqrt(2)], [0, 0, 0]]
Sdn = [[0, 0, 0], [np.sqrt(2), 0, 0], [0, np.sqrt(2), 0]]

# Heisenberg term
heisenberg = 0.5 * (np.kron(Sup, Sdn) + np.kron(Sdn, Sup)) + np.kron(Sz, Sz)
# AKLT two-site projector
P2_AKLT = 0.5 * heisenberg + np.dot(heisenberg, heisenberg) / 6.0 + np.identity(9) / 3.0

# 1D Lattice
g = nk.graph.Hypercube(length=10, n_dim=1, pbc=True)

# Hilbert space of spin-1s on the graph
hi = nk.hilbert.Spin(s=1, graph=g)

# AKLT model Hamiltonian as graph
ha = nk.operator.GraphOperator(hilbert=hi, bondops=[P2_AKLT.tolist()])

# Perform Lanczos Exact Diagonalization to get lowest three eigenvalues
res = nk.exact.lanczos_ed(ha, first_n=3, compute_eigenvectors=True)

# Print eigenvalues
print("eigenvalues:", res.eigenvalues)

# Compute energy of ground state
print("ground state energy:", res.mean(ha, 0))

def run_netket(args):
    g = nk.graph.Hypercube(length=args.input_size, n_dim=1)
    hi = nk.hilbert.Spin(s=0.5, total_sz=0, graph=g)
    ha = nk.operator.Heisenberg(hilbert=hi)
    middle_layer = (nk.layer.ConvolutionalHypercube(length=args.input_size,
                                                    n_dim=1,
                                                    input_channels=args.width,
                                                    output_channels=args.width,
                                                    kernel_length=args.kernel_size),
                     nk.layer.Lncosh(input_size=args.width * args.input_size))
    middle_layers = middle_layer * (args.depth - 1)
    first_layer = (nk.layer.ConvolutionalHypercube(length=args.input_size,
                                                   n_dim=1,
                                                   input_channels=1,
                                                   output_channels=args.width,
                                                   kernel_length=args.kernel_size),
                 nk.layer.Lncosh(input_size=args.width * args.input_size),)
    ma = nk.machine.FFNN(hi, first_layer + middle_layers)

def generate(N, n_basis=20, n_shots=1000, seed=1234):
    g = gr.Hypercube(length=N, n_dim=1, pbc=False)
    hi = hs.Spin(g, s=0.5)
    ha = op.Ising(hilbert=hi, h=1)
    res = exact.lanczos_ed(ha, first_n=1, compute_eigenvectors=True)

    psi = res.eigenvectors[0]

    rotations = []
    training_samples = []
    training_bases = []

    np.random.seed(seed)

    for m in range(n_basis):
        basis = np.random.choice(
            list("XYZ"), size=N, p=[1.0 / N, 1.0 / N, (N - 2.0) / N]
        )

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#    http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import netket as nk

# 1D Lattice
g = nk.graph.Hypercube(length=20, n_dim=1, pbc=True)

# Hilbert space of spins on the graph
# with total Sz equal to 0
hi = nk.hilbert.Spin(s=0.5, graph=g, total_sz=0)

# Heisenberg hamiltonian
ha = nk.operator.Heisenberg(hilbert=hi)

# Symmetric RBM Spin Machine
ma = nk.machine.JastrowSymm(hilbert=hi)
ma.init_random_parameters(seed=1234, sigma=0.01)

# Metropolis Exchange Sampling
# Notice that this sampler exchanges two neighboring sites
# thus preservers the total magnetization
sa = nk.sampler.MetropolisExchange(machine=ma, graph=g)