How to use the pennylane.numpy function in PennyLane

    _operation_map['Kerr'] = lambda *x, **y: np.identity(2)

except AttributeError:
            # get the equivalent pennylane-forest operation class
            op = getattr(plf, gate)

        # the list of wires to apply the operation to
        w = list(range(op.num_wires))

        if op.par_domain == "A":
            # the parameter is an array
            if gate == "QubitUnitary":
                p = [U]
                w = [0]
                expected_out = apply_unitary(U, 3)
            elif gate == "BasisState":
                p = [np.array([1, 1, 1])]
                expected_out = np.array([0, 0, 0, 0, 0, 0, 0, 1])
        else:
            p = [0.432423, 2, 0.324][: op.num_params]
            fn = test_operation_map[gate]

            if callable(fn):
                # if the default.qubit is an operation accepting parameters,
                # initialise it using the parameters generated above.
                O = fn(*p)
            else:
                # otherwise, the operation is simply an array.
                O = fn

            # calculate the expected output
            expected_out = apply_unitary(O, 3)

        dev.apply(gate, wires=w, par=p)

def circuit_Ymi():
            qml.RX(np.pi/2, wires=qubit)
            return qml.expval(qml.PauliY(qubit))

def gradient(params):
            """Returns the gradient of the above circuit"""
            da = -np.sin(params[0]) * np.cos(params[1])
            db = -np.cos(params[0]) * np.sin(params[1])
            return np.array([da, db])

qnode = qml.QNode(circuit, gaussian_device_4modes)

        # execution test
        qnode(weights)
        queue = qnode.queue

        # Test that gates appear in the right order for each layer:
        # BS-R-S-BS-R-D-K
        for l in range(depth):
            gates = [qml.Beamsplitter, qml.Rotation, qml.Squeezing,
                     qml.Beamsplitter, qml.Rotation, qml.Displacement]

            # count the position of each group of gates in the layer
            num_gates_per_type = [0, 6, 4, 4, 6, 4, 4, 4]
            s = np.cumsum(num_gates_per_type)
            gc = l*sum(num_gates_per_type)+np.array(list(zip(s[:-1], s[1:])))

            # loop through expected gates
            for idx, g in enumerate(gates):
                # loop through where these gates should be in the queue
                for opidx, op in enumerate(queue[gc[idx, 0]:gc[idx, 1]]):
                    # check that op in queue is correct gate
                    assert isinstance(op, g)

                    # test that the parameters are correct
                    res_params = op.parameters

                    if idx == 0:
                        # first BS
                        exp_params = [weights[0][l][opidx], weights[1][l][opidx]]
                    elif idx == 1:

##############################################################################
# Exploring the barren plateau problem with PennyLane
# ---------------------------------------------------
#
# First, we import PennyLane, NumPy, and Matplotlib

import pennylane as qml
from pennylane import numpy as np
import matplotlib.pyplot as plt


##################################################
# Next, we create a randomized variational circuit

# Set a seed for reproducibility
np.random.seed(20)

num_qubits = 4
dev = qml.device("default.qubit", wires=num_qubits)
gate_set = [qml.RX, qml.RY, qml.RZ]
gate_sequence = {i: np.random.choice(gate_set) for i in range(num_qubits)}


def rand_circuit(params, random_gate_sequence=None, num_qubits=None):
    """A random variational quantum circuit.

    Args:
        params (array[float]): array of parameters
        random_gate_sequence (dict): a dictionary of random gates
        num_qubits (int): the number of qubits in the circuit

    Returns:

def prepare_and_sample(weights):

    # Variational circuit generating a guess for the solution vector |x>
    variational_block(weights)

    # We assume that the system is measured in the computational basis.
    # If we label each basis state with a decimal integer j = 0, 1, ... 2 ** n_qubits - 1,
    # this is equivalent to a measurement of the following diagonal observable.
    basis_obs = qml.Hermitian(np.diag(range(2 ** n_qubits)), wires=range(n_qubits))

    return qml.sample(basis_obs)

def param_shift(theta1):
    return 0.5 * (noisy_cost([theta1 + np.pi / 2, theta2]) - \
             noisy_cost([theta1 - np.pi / 2, theta2]))

# For learning tasks, the cost depends on the data - here the features and
# labels considered in the iteration of the optimization routine.


def cost(var, X, Y):
    predictions = [variational_classifier(var, x=x) for x in X]
    return square_loss(Y, predictions)


##############################################################################
# Optimization
# ~~~~~~~~~~~~
#
# Let’s now load and preprocess some data.

data = np.loadtxt("data/parity.txt")
X = data[:, :-1]
Y = data[:, -1]
Y = Y * 2 - np.ones(len(Y))  # shift label from {0, 1} to {-1, 1}

for i in range(5):
    print("X = {}, Y = {: d}".format(X[i], int(Y[i])))

print("...")

##############################################################################
# We initialize the variables randomly (but fix a seed for
# reproducability). The first variable in the list is used as a bias,
# while the rest is fed into the gates of the variational circuit.

np.random.seed(0)
num_qubits = 4

# This is a sample of four images:
#
# .. figure:: ../demonstrations/embedding_metric_learning/data_example.png
#    :align: center
#    :width: 50%
#
# For convenience, instead of coding up the classical neural network, we
# load `pre-extracted feature vectors of the images
# `_.
# These were created by
# resizing, cropping and normalizing the images, and passing them through
# PyTorch's pretrained ResNet 512 (that is, without the final linear
# layer).
#

X = np.loadtxt("embedding_metric_learning/X_antbees.txt", ndmin=2)  #1  pre-extracted inputs
Y = np.loadtxt("embedding_metric_learning/Y_antbees.txt")  # labels
X_val = np.loadtxt(
    "embedding_metric_learning/X_antbees_test.txt", ndmin=2
)  # pre-extracted validation inputs
Y_val = np.loadtxt("embedding_metric_learning/Y_antbees_test.txt")  # validation labels

# split data into two classes
A = X[Y == -1]
B = X[Y == 1]
A_val = X_val[Y_val == -1]
B_val = X_val[Y_val == 1]

print(A.shape)
print(B.shape)