代码1：
import pennylane as qml
from pennylane import numpy as np

# 定义设备，使用11个量子比特
dev = qml.device("default.qubit", wires=11)


def encode_group(control_qubits, data_qubits, angles_q2, angles_q3):
    """
    模块化方法，用于对一组量子比特进行角度编码。

    参数：
    - control_qubits (list[int]): 控制比特的索引，例如 [0, 1]。
    - data_qubits (dict): 包含 'q2' 和 'q3' 的量子比特索引，例如 {'q2': 2, 'q3': 3}。
    - angles_q2 (list[float]): q2 在不同控制状态下的 Ry 旋转角度。
    - angles_q3 (list[float]): q3 在不同控制状态下的 Ry 旋转角度。
    """
    control_values = [[0, 0], [0, 1], [1, 0], [1, 1]]

    for cv, angle_q2, angle_q3 in zip(control_values, angles_q2, angles_q3):
        # 对 q2 应用 RY 旋转，受 control_qubits 控制，特定于 control_values
        qml.ctrl(qml.RY, control=control_qubits, control_values=cv)(angle_q2, wires=data_qubits['q2'])

        # 对 q3 应用 RY 旋转，受 control_qubits 控制，特定于 control_values
        qml.ctrl(qml.RY, control=control_qubits, control_values=cv)(angle_q3, wires=data_qubits['q3'])


def apply_controlled_swap(swap_qubit, q_swap1, q_swap2):
    """
    模块化方法，用于应用受控 SWAP 门。

    参数：
    - swap_qubit (int): 作为交换门控制比特的量子比特索引。
    - q_swap1 (int): 受交换操作影响的第一个量子比特索引。
    - q_swap2 (int): 受交换操作影响的第二个量子比特索引。
    """
    # 对 swap_qubit 应用 Hadamard 门
    qml.Hadamard(wires=swap_qubit)

    # 应用受控 SWAP 门
    qml.CSWAP(wires=[swap_qubit, q_swap1, q_swap2])

    # 再次对 swap_qubit 应用 Hadamard 门以恢复控制比特
    qml.Hadamard(wires=swap_qubit)


# 定义一个量子节点 (QNode)
@qml.qnode(dev)
def quantum_neural_network(encoding_data):
    """
    量子神经网络的 QNode，实现多组数据的编码和交换操作。

    参数：
    - encoding_data (list[dict]): 每个字典包含一组编码所需的信息，包括：
        - 'data_qubits': 包含 'q2' 和 'q3' 的量子比特索引。
        - 'swap_qubit': 控制交换门的量子比特索引。
        - 'angles_q2': q2 在不同控制状态下的 Ry 旋转角度列表。
        - 'angles_q3': q3 在不同控制状态下的 Ry 旋转角度列表。

    返回：
    - list[float]: 每组交换控制比特的 Pauli-Z 期望值。
    """
    # 控制比特为 q0 和 q1
    control_qubits = [0, 1]

    # 对每组数据进行编码
    for data_set in encoding_data:
        data_qubits = data_set['data_qubits']
        angles_q2 = data_set['angles_q2']
        angles_q3 = data_set['angles_q3']

        encode_group(control_qubits, data_qubits, angles_q2, angles_q3)

    # 对每组数据应用交换操作
    for data_set in encoding_data:
        swap_qubit = data_set['swap_qubit']
        data_qubits = data_set['data_qubits']

        apply_controlled_swap(swap_qubit, data_qubits['q3'], data_qubits['q2'])

    # 测量每个交换控制比特的 Pauli-Z 期望值
    results = [qml.expval(qml.PauliZ(data_set['swap_qubit'])) for data_set in encoding_data]

    return results


# 测试代码
if __name__ == "__main__":
    # 定义多组数据集，每组包含 q2 和 q3 的角度，以及对应的 swap 控制比特
    encoding_data = [
        {
            'data_qubits': {'q2': 2, 'q3': 3},
            'swap_qubit': 4,
            'angles_q2': [1.0, 3.0, 5.0, 7.0],
            'angles_q3': [2.0, 4.0, 6.0, 8.0]
        },
        {
            'data_qubits': {'q2': 5, 'q3': 6},
            'swap_qubit': 7,
            'angles_q2': [1.5, 3.5, 5.5, 7.5],
            'angles_q3': [2.5, 4.5, 6.5, 8.5]
        },
        {
            'data_qubits': {'q2': 8, 'q3': 9},
            'swap_qubit': 10,
            'angles_q2': [2.0, 4.0, 6.0, 8.0],
            'angles_q3': [3.0, 5.0, 7.0, 9.0]
        }
    ]

    # 执行量子神经网络
    result = quantum_neural_network(encoding_data)

    # 打印测量结果
    for i, res in enumerate(result, start=1):
        print(f"测量 swap_qubit 的 Pauli-Z 期望值 (集 {i}): {res}")

    # 绘制并打印量子电路
    drawer = qml.draw(quantum_neural_network)
    print(drawer(encoding_data))
代码2：import sys
import os
import pennylane as qml
from pennylane import numpy as np
import tensorflow as tf  # 确保 TensorFlow 在设置 floatx 后导入
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from tensorflow.python.keras.layers import Dense
from example_utils import get_parser
from importance_sampling.training import ConstantTimeImportanceTraining
from pennylane.qnn.keras import KerasLayer

# 设置 TensorFlow 的默认浮点类型为 float64
tf.keras.backend.set_floatx('float64')

# 重定向stdout以捕获打印输出
class Tee(object):
    def __init__(self, *files):
        self.files = files
    def write(self, obj):
        for f in self.files:
            f.write(obj)
            f.flush()
    def flush(self):
        for f in self.files:
            f.flush()

output_file = open('output.log', 'w', encoding='utf-8')
original_stdout = sys.stdout
sys.stdout = Tee(sys.stdout, output_file)

def save_plot(fig, filename):
    fig.savefig(filename)
    plt.close(fig)

def plot_history(history):
    # 绘制训练和验证准确率
    fig, ax = plt.subplots(figsize=(10, 5))
    ax.plot(history.history['accuracy'])
    ax.plot(history.history['val_accuracy'])
    ax.set_title('Model Accuracy')
    ax.set_ylabel('Accuracy')
    ax.set_xlabel('Epoch')
    ax.legend(['Train', 'Validation'], loc='upper left')
    save_plot(fig, 'accuracy_plot.png')

    # 绘制训练和验证损失
    fig, ax = plt.subplots(figsize=(10, 5))
    ax.plot(history.history['loss'])
    ax.plot(history.history['val_loss'])
    ax.set_title('Model Loss')
    ax.set_ylabel('Loss')
    ax.set_xlabel('Epoch')
    ax.legend(['Train', 'Validation'], loc='upper left')
    save_plot(fig, 'loss_plot.png')

def calculate_accuracy_fluctuation(history):
    val_accuracies = history.history['val_accuracy']
    mean_accuracy = np.mean(val_accuracies)
    fluctuation = np.sum(np.abs(val_accuracies - mean_accuracy))
    return mean_accuracy, fluctuation

# 新的量子神经网络实现
# 定义设备，使用11个量子比特
dev = qml.device("default.qubit", wires=11, shots=None)

def encode_group(control_qubits, data_qubits, angles_q2, angles_q3):
    """
    模块化方法，用于对一组量子比特进行角度编码。

    参数：
    - control_qubits (list[int]): 控制比特的索引，例如 [0, 1]。
    - data_qubits (dict): 包含 'q2' 和 'q3' 的量子比特索引，例如 {'q2': 2, 'q3': 3}。
    - angles_q2 (list[float]): q2 在不同控制状态下的 Ry 旋转角度。
    - angles_q3 (list[float]): q3 在不同控制状态下的 Ry 旋转角度。
    """
    control_values = [[0, 0], [0, 1], [1, 0], [1, 1]]

    for cv, angle_q2, angle_q3 in zip(control_values, angles_q2, angles_q3):
        # 翻转控制比特为0的量子比特，以便使用受控RY门
        for qubit, val in zip(control_qubits, cv):
            if val == 0:
                qml.PauliX(wires=qubit)

        # 对 q2 应用受控 RY 旋转
        qml.ctrl(qml.RY, control=control_qubits)(angle_q2, wires=data_qubits['q2'])

        # 对 q3 应用受控 RY 旋转
        qml.ctrl(qml.RY, control=control_qubits)(angle_q3, wires=data_qubits['q3'])

        # 恢复控制比特的原始状态
        for qubit, val in zip(control_qubits, cv):
            if val == 0:
                qml.PauliX(wires=qubit)

def apply_controlled_swap(swap_qubit, q_swap1, q_swap2):
    """
    模块化方法，用于应用受控 SWAP 门。

    参数：
    - swap_qubit (int): 作为交换门控制比特的量子比特索引。
    - q_swap1 (int): 受交换操作影响的第一个量子比特索引。
    - q_swap2 (int): 受交换操作影响的第二个量子比特索引。
    """
    # 对 swap_qubit 应用 Hadamard 门
    qml.Hadamard(wires=swap_qubit)

    # 应用受控 SWAP 门
    qml.CSWAP(wires=[swap_qubit, q_swap1, q_swap2])

    # 再次对 swap_qubit 应用 Hadamard 门以恢复控制比特
    qml.Hadamard(wires=swap_qubit)

# 定义一个量子节点 (QNode) 用于训练
@qml.qnode(dev, interface='tf', diff_method='backprop')
def quantum_neural_network(inputs, weights):
    """
    量子神经网络的 QNode，实现多组数据的编码和交换操作。

    参数：
    - inputs (array[float]): 输入特征向量。
    - weights (array[float]): 量子电路的参数权重。

    返回：
    - tensor: 每组交换控制比特的 Pauli-Z 期望值。
    """
    # 使用输入数据对量子态进行编码
    # 假设前4个量子比特对应于4个输入特征
    for i in range(len(inputs)):
        qml.RY(inputs[i], wires=i)

    # 控制比特为 q0 和 q1
    control_qubits = [0, 1]

    # 定义各组的量子比特索引和旋转角度
    encoding_data = [
        {
            'data_qubits': {'q2': 2, 'q3': 3},
            'swap_qubit': 4,
            'angles_q2': weights[0, 2, :],  # 使用权重调整角度
            'angles_q3': weights[0, 3, :]
        },
        {
            'data_qubits': {'q2': 5, 'q3': 6},
            'swap_qubit': 7,
            'angles_q2': weights[0, 5, :],
            'angles_q3': weights[0, 6, :]
        },
        {
            'data_qubits': {'q2': 8, 'q3': 9},
            'swap_qubit': 10,
            'angles_q2': weights[0, 8, :],
            'angles_q3': weights[0, 9, :]
        }
    ]

    # 对每组数据进行编码
    for data_set in encoding_data:
        data_qubits = data_set['data_qubits']
        angles_q2 = data_set['angles_q2']
        angles_q3 = data_set['angles_q3']
        encode_group(control_qubits, data_qubits, angles_q2, angles_q3)

    # 对每组数据应用交换操作
    for data_set in encoding_data:
        swap_qubit = data_set['swap_qubit']
        data_qubits = data_set['data_qubits']
        apply_controlled_swap(swap_qubit, data_qubits['q3'], data_qubits['q2'])

    # 应用 Rot 层
    # 这里假设 weights 的形状为 (n_layers, n_qubits, 3)
    for layer in range(weights.shape[0]):
        for qubit in range(weights.shape[1]):
            qml.Rot(weights[layer, qubit, 0],
                    weights[layer, qubit, 1],
                    weights[layer, qubit, 2],
                    wires=qubit)

    # 测量每个交换控制比特的 Pauli-Z 期望值
    results = [qml.expval(qml.PauliZ(data_set['swap_qubit'])) for data_set in encoding_data]

    # 返回测量结果的元组
    return tuple(results)

# 定义一个辅助的 QNode 用于绘制量子电路
@qml.qnode(dev, interface='autograd', diff_method='backprop')
def quantum_neural_network_draw(inputs, weights):
    """
    辅助 QNode，仅用于绘制量子电路图。

    参数：
    - inputs (array[float]): 输入特征向量。
    - weights (array[float]): 量子电路的参数权重。

    返回：
    - tuple: 每组交换控制比特的 Pauli-Z 期望值。
    """
    # 使用输入数据对量子态进行编码
    # 假设前4个量子比特对应于4个输入特征
    for i in range(len(inputs)):
        qml.RY(inputs[i], wires=i)

    # 控制比特为 q0 和 q1
    control_qubits = [0, 1]

    # 定义各组的量子比特索引和旋转角度
    encoding_data = [
        {
            'data_qubits': {'q2': 2, 'q3': 3},
            'swap_qubit': 4,
            'angles_q2': weights[0, 2, :],  # 使用权重调整角度
            'angles_q3': weights[0, 3, :]
        },
        {
            'data_qubits': {'q2': 5, 'q3': 6},
            'swap_qubit': 7,
            'angles_q2': weights[0, 5, :],
            'angles_q3': weights[0, 6, :]
        },
        {
            'data_qubits': {'q2': 8, 'q3': 9},
            'swap_qubit': 10,
            'angles_q2': weights[0, 8, :],
            'angles_q3': weights[0, 9, :]
        }
    ]

    # 对每组数据进行编码
    for data_set in encoding_data:
        data_qubits = data_set['data_qubits']
        angles_q2 = data_set['angles_q2']
        angles_q3 = data_set['angles_q3']
        encode_group(control_qubits, data_qubits, angles_q2, angles_q3)

    # 对每组数据应用交换操作
    for data_set in encoding_data:
        swap_qubit = data_set['swap_qubit']
        data_qubits = data_set['data_qubits']
        apply_controlled_swap(swap_qubit, data_qubits['q3'], data_qubits['q2'])

    # 应用 Rot 层
    # 这里假设 weights 的形状为 (n_layers, n_qubits, 3)
    for layer in range(weights.shape[0]):
        for qubit in range(weights.shape[1]):
            qml.Rot(weights[layer, qubit, 0],
                    weights[layer, qubit, 1],
                    weights[layer, qubit, 2],
                    wires=qubit)

    # 测量每个交换控制比特的 Pauli-Z 期望值
    results = [qml.expval(qml.PauliZ(data_set['swap_qubit'])) for data_set in encoding_data]

    # 返回测量结果的元组
    return tuple(results)

# 定义一个量子神经网络层
class QuantumNeuralNetworkLayer(tf.keras.layers.Layer):
    def __init__(self, n_qubits, n_layers, num_classes, **kwargs):
        super(QuantumNeuralNetworkLayer, self).__init__(**kwargs)
        self.n_qubits = n_qubits
        self.n_layers = n_layers
        self.num_classes = num_classes

        # Initialize weights
        weights_shape = (n_layers, n_qubits, 3)
        self.weights_qnn = self.add_weight(
            shape=weights_shape,
            initializer=tf.keras.initializers.GlorotUniform(),
            trainable=True,
            name='weights_qnn'
        )

    def call(self, inputs):
        # 使用 tf.map_fn 将 QNode 应用于每个样本
        q_output = tf.map_fn(
            lambda x: quantum_neural_network(x, self.weights_qnn),
            inputs,
            dtype=tf.float64  # 使用 float64 以匹配 Pennylane 的输出
        )
        return q_output

# 定义一个量子神经网络模型
class QuantumNeuralNetwork(tf.keras.Model):
    def __init__(self, n_qubits, n_layers, num_classes, **kwargs):
        super(QuantumNeuralNetwork, self).__init__(**kwargs)
        self.n_qubits = n_qubits
        self.n_layers = n_layers
        self.num_classes = num_classes

        # Quantum layer
        self.quantum_layer = QuantumNeuralNetworkLayer(n_qubits, n_layers, num_classes)

        # Classical layers
        self.dense = Dense(num_classes, activation='softmax')

    def call(self, inputs):
        q_output = self.quantum_layer(inputs)
        return self.dense(q_output)

if __name__ == "__main__":
    parser = get_parser("Train model on Iris dataset")
    args = parser.parse_args()

    iris = load_iris()
    X = iris['data'].astype(np.float64)  # 转换为 float64
    y = iris['target'].reshape(-1, 1)

    encoder = OneHotEncoder(sparse_output=False)
    y = encoder.fit_transform(y).astype(np.float64)  # 转换为 float64

    x_train, x_test, y_train, y_test = train_test_split(
        X, y, test_size=0.3, random_state=116
    )

    learning_rate = 0.05
    epochs = 100
    batch_size = 8
    num_classes = 3
    n_qubits = 11  # 调整为11个量子比特（0到10）
    n_layers = 2

    model = QuantumNeuralNetwork(
        n_qubits=n_qubits,
        n_layers=n_layers,
        num_classes=num_classes
    )

    model.compile(
        optimizer=tf.keras.optimizers.SGD(learning_rate=learning_rate),
        loss='categorical_crossentropy',
        metrics=['accuracy']
    )

    def is_quantum_model(model):
        for layer in model.layers:
            if isinstance(layer, QuantumNeuralNetworkLayer):
                return True
        return False

    if is_quantum_model(model):
        print("The model contains a Quantum Neural Network (QNN) layer.")
    else:
        print("The model does not contain a Quantum Neural Network (QNN) layer.")

    print("\nQuantum Circuit Diagram:")
    sample_inputs = np.ones(4, dtype=np.float64) * 0.0  # 示例输入，对应4个特征
    sample_weights = np.zeros((n_layers, n_qubits, 3), dtype=np.float64)  # 示例权重
    print(qml.draw(quantum_neural_network_draw)(sample_inputs, sample_weights))  # 使用辅助 QNode

    if args.importance_training:
        wrapped_model = ConstantTimeImportanceTraining(model, score="loss")
        history = wrapped_model.fit(
            x_train, y_train,
            batch_size=batch_size,
            epochs=epochs,
            verbose=1,
            validation_data=(x_test, y_test)
        )
    else:
        history = model.fit(
            x_train, y_train,
            batch_size=batch_size,
            epochs=epochs,
            verbose=1,
            validation_data=(x_test, y_test)
        )

    score = model.evaluate(x_test, y_test, verbose=0)
    print('Test loss:', score[0])
    print('Test accuracy:', score[1])

    mean_accuracy, fluctuation = calculate_accuracy_fluctuation(history)
    print('Mean validation accuracy:', mean_accuracy)
    print('Accuracy fluctuation:', fluctuation)

    plot_history(history)

    # 关闭输出文件并恢复stdout
    sys.stdout = original_stdout
    output_file.close()

print("脚本执行完成。查看 'output.log' 获取打印输出，查看当前目录获取保存的图表。")
仔细核对代码1与代码2，我需要你明确告诉我，代码1的量子神经网络和代码2中的量子神经网络在除了版本不同之外还有什么区别，如果代码2中的量子神经网络不同于代码1中的内容（只要有任何内容不同），都需要修改代码2中的代码，使其与代码1中一直，同时你需要注意用代码2中的环境来进行修改，下面我将给你对应的环境：absl-py                      2.1.0
annotated-types              0.7.0
anyio                        4.3.0
appdirs                      1.4.4
argon2-cffi                  23.1.0
argon2-cffi-bindings         21.2.0
array-record                 0.4.1
arrow                        1.3.0
asttokens                    2.4.1
astunparse                   1.6.3
async-lru                    2.0.4
attrs                        23.2.0
autograd                     1.7.0
autoray                      0.6.12
Babel                        2.14.0
beautifulsoup4               4.12.3
bleach                       6.1.0
blinker                      1.8.2
cachetools                   5.5.0
certifi                      2024.8.30
cffi                         1.16.0
charset-normalizer           3.3.2
click                        8.1.7
colorama                     0.4.6
comm                         0.2.1
contourpy                    1.3.0
cryptography                 42.0.5
cycler                       0.12.1
debugpy                      1.8.1
decorator                    5.1.1
defusedxml                   0.7.1
dill                         0.3.9
dm-tree                      0.1.8
docplex                      2.28.240
etils                        1.5.2
exceptiongroup               1.2.0
executing                    2.0.1
fastjsonschema               2.19.1
flatbuffers                  24.3.25
fonttools                    4.54.1
fqdn                         1.5.1
fsspec                       2024.9.0
gast                         0.4.0
google-auth                  2.35.0
google-auth-oauthlib         0.4.6
google-pasta                 0.2.0
graphviz                     0.20.1
grpcio                       1.66.2
h11                          0.14.0
h5py                         3.12.1
httpcore                     1.0.4
httpx                        0.27.0
ibm-cloud-sdk-core           3.21.0
ibm-platform-services        0.57.1
idna                         3.10
imageio                      2.35.1
importlib_metadata           8.5.0
importlib_resources          6.4.5
ipykernel                    6.29.3
ipython                      8.18.1
ipython-genutils             0.2.0
ipyvue                       1.10.2
ipyvuetify                   1.9.1
ipywidgets                   7.8.1
isoduration                  20.11.0
jedi                         0.19.1
Jinja2                       3.1.3
joblib                       1.4.2
json5                        0.9.20
jsonpointer                  2.4
jsonschema                   4.21.1
jsonschema-specifications    2023.12.1
jupyter_client               8.6.0
jupyter_core                 5.7.1
jupyter-events               0.9.0
jupyter-lsp                  2.2.3
jupyter_server               2.13.0
jupyter_server_terminals     0.5.2
jupyterlab                   4.1.2
jupyterlab_pygments          0.3.0
jupyterlab_server            2.25.3
jupyterlab-widgets           1.1.7
keras                        2.10.0
Keras-Preprocessing          1.1.2
kiwisolver                   1.4.7
lazy_loader                  0.4
libclang                     18.1.1
Markdown                     3.7
markdown-it-py               3.0.0
MarkupSafe                   2.1.5
matplotlib                   3.9.2
matplotlib-inline            0.1.6
mdurl                        0.1.2
mistune                      3.0.2
ml-dtypes                    0.3.2
mpmath                       1.3.0
namex                        0.0.7
nbclient                     0.7.4
nbconvert                    7.16.2
nbformat                     5.9.2
nest-asyncio                 1.6.0
networkx                     3.2.1
notebook                     7.1.1
notebook_shim                0.2.4
numpy                        1.26.4
oauthlib                     3.2.2
opt_einsum                   3.4.0
overrides                    7.7.0
packaging                    24.1
pandas                       2.2.1
pandocfilters                1.5.1
parso                        0.8.3
pbr                          6.1.0
PennyLane                    0.15.0
pexpect                      4.9.0
pillow                       10.4.0
pip                          24.2
platformdirs                 4.2.0
plotly                       5.19.0
prometheus_client            0.20.0
promise                      2.3
prompt-toolkit               3.0.43
protobuf                     3.19.6
psutil                       6.0.0
ptyprocess                   0.7.0
pure-eval                    0.2.2
pyasn1                       0.6.1
pyasn1_modules               0.4.1
pycparser                    2.21
pydantic                     2.9.2
pydantic_core                2.23.4
pydot                        3.0.2
pydotplus                    2.0.2
Pygments                     2.17.2
PyJWT                        2.9.0
pylatexenc                   2.10
pyparsing                    3.1.4
pyperclip                    1.8.2
pyspnego                     0.10.2
python-dateutil              2.9.0.post0
python-json-logger           2.0.7
pytz                         2024.1
pywin32                      308
pywinpty                     2.0.13
PyYAML                       6.0.1
pyzmq                        25.1.2
qiskit                       1.0.2
qiskit-aer                   0.14.0.1
qiskit-algorithms            0.3.0
qiskit-ibm-provider          0.10.0
qiskit-optimization          0.6.1
referencing                  0.33.0
requests                     2.32.3
requests-ntlm                1.2.0
requests-oauthlib            2.0.0
rfc3339-validator            0.1.4
rfc3986-validator            0.1.1
rich                         13.7.1
rpds-py                      0.18.0
rsa                          4.9
rustworkx                    0.15.1
scikit-image                 0.24.0
scikit-learn                 1.5.2
scipy                        1.13.1
seaborn                      0.13.2
semantic-version             2.6.0
Send2Trash                   1.8.2
setuptools                   69.0.3
six                          1.16.0
sniffio                      1.3.1
soupsieve                    2.5
sspilib                      0.2.0
stack-data                   0.6.3
stevedore                    5.3.0
symengine                    0.11.0
sympy                        1.13.3
tenacity                     8.2.3
tensorboard                  2.10.1
tensorboard-data-server      0.6.1
tensorboard-plugin-wit       1.8.1
tensorflow                   2.10.0
tensorflow-datasets          4.9.3
tensorflow-estimator         2.10.0
tensorflow-intel             2.16.1
tensorflow-io-gcs-filesystem 0.31.0
tensorflow-metadata          1.16.1
termcolor                    2.4.0
terminado                    0.18.0
threadpoolctl                3.5.0
tifffile                     2024.8.30
tinycss2                     1.2.1
toml                         0.10.2
tomli                        2.0.1
tornado                      6.4
tqdm                         4.66.5
traitlets                    5.14.1
types-python-dateutil        2.8.19.20240106
typing_extensions            4.12.2
tzdata                       2024.1
uri-template                 1.3.0
urllib3                      2.2.3
voila                        0.5.5
wcwidth                      0.2.13
webcolors                    1.13
webencodings                 0.5.1
websocket-client             1.7.0
websockets                   12.0
Werkzeug                     3.0.4
wheel                        0.42.0
widgetsnbextension           3.6.6
wrapt                        1.16.0
zipp                         3.20.2