How to use the netket.variational.Vmc function in NetKet

# Graph Operator
op = nk.operator.GraphOperator(hi, siteops=site_operator, bondops=bond_operator)

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

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

# Optimizer
opt = nk.optimizer.AdaMax()

# Stochastic reconfiguration
gs = nk.variational.Vmc(
    hamiltonian=op,
    sampler=sa,
    optimizer=opt,
    n_samples=1000,
    diag_shift=0.1,
    method="Gd",
)

gs.run(output_prefix="test", n_iter=30000)

),
    nk.layer.Lncosh(input_size=4 * 2 * L),
)

# FFNN Machine
ma = nk.machine.FFNN(hi, layers)
ma.init_random_parameters(seed=1234, sigma=0.1)

# Sampler
sa = nk.sampler.MetropolisHamiltonian(machine=ma, hamiltonian=ha)

# Optimizer
op = nk.optimizer.Sgd(learning_rate=0.01)

# Variational Monte Carlo
gs = nk.variational.Vmc(
    hamiltonian=ha, sampler=sa, optimizer=op, n_samples=1000, diag_shift=0.01
)

gs.run(output_prefix="ffnn_test", n_iter=300, save_params_every=10)

# Bose Hubbard Hamiltonian
ha = nk.operator.BoseHubbard(U=4.0, hilbert=hi)

# Jastrow Machine with Symmetry
ma = nk.machine.JastrowSymm(hilbert=hi)
ma.init_random_parameters(seed=1234, sigma=0.01)

# Sampler
sa = nk.sampler.MetropolisHamiltonian(machine=ma, hamiltonian=ha)

# Stochastic gradient descent optimization
op = nk.optimizer.Sgd(learning_rate=0.1)

# Variational Monte Carlo
vmc = nk.variational.Vmc(
    hamiltonian=ha,
    sampler=sa,
    optimizer=op,
    n_samples=1000,
    diag_shift=5e-3,
    use_iterative=False,
    method="Sr",
)

vmc.run(output_prefix="test", n_iter=4000)

# 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)

# Optimizer
op = nk.optimizer.Sgd(learning_rate=0.1)

# Stochastic reconfiguration
gs = nk.variational.Vmc(
    hamiltonian=ha,
    sampler=sa,
    optimizer=op,
    n_samples=1000,
    method="Sr",
    diag_shift=0.1,
)

gs.run(output_prefix="test", n_iter=300)

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

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

# Sampler
sa = nk.sampler.MetropolisLocal(machine=ma)
sa.seed(SEED)

# Optimizer
op = nk.optimizer.Sgd(learning_rate=0.1)

# Variational Monte Carlo
vmc = nk.variational.Vmc(
    hamiltonian=ha,
    sampler=sa,
    optimizer=op,
    n_samples=1000,
    diag_shift=0.1,
    method="Sr",
)

vmc.run(output_prefix="test", n_iter=300, save_params_every=10)

rands = np.random.randint(v.shape[1], size=(v.shape[0], 2))

    for i in range(v.shape[0]):
        iss = rands[i, 0]
        jss = rands[i, 1]

        vnew[i, iss], vnew[i, jss] = vnew[i, jss], vnew[i, iss]


sa = nk.sampler.MetropolisHastings(ma, exchange_kernel, n_chains=16)

# Optimizer
op = nk.optimizer.Sgd(learning_rate=0.05)

# Stochastic reconfiguration
gs = nk.variational.Vmc(
    hamiltonian=ha,
    sampler=sa,
    optimizer=op,
    n_samples=1000,
    diag_shift=0.1,
    method="Sr",
)

gs.run(output_prefix="test", n_iter=300)