Tensorflow federated attribute error

# Importing all the required libraries

import pandas as pd
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

import tensorflow as tf
import tensorflow_federated as tff

iris = load_iris()
df = pd.DataFrame(iris.data,columns=iris.feature_names)

# Splitting the dataframe into input features and target variables

x = df.drop('Species',axis=1)
y = df['Species']
# Function to create client datasets (assuming data is pre-partitioned)
def create_tf_dataset(client_data):
  """Creates a tf.data.Dataset from the provided client data (features, labels)."""
  features, labels = client_data
  return tf.data.Dataset.from_tensor_slices((features, labels))

# Split data into cliendatasets (simulating data partitioning)
client_datasets = []
num_clients = 5
for i in range(num_clients):
  start_index = int(i * (len(x) / num_clients))
  end_index = int((i + 1) * (len(x) / num_clients))
  client_features = x[start_index:end_index]
  client_labels = y[start_index:end_index]
  client_datasets.append(create_tf_dataset((client_features, client_labels)))
  # Define the model architecture (replace with your desired model complexity)
def model_fn(inputs):
   features, _ = inputs  # We only use features for classification
   dense1 = tf.keras.layers.Dense(10, activation='relu')(features)
   dense2 = tf.keras.layers.Dense(3, activation='softmax')(dense1)  # 3 units for 3 Iris classes
   return tf.keras.Model(inputs=features, outputs=dense2)

# Define the client optimizer
client_optimizer = tf.keras.optimizers.SGD(learning_rate=0.1)

# Define the server optimizer (for server-sided aggregation)
server_optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)
fed_learning_model = tff.learning.build_federated_averaging_process(

But I am getting this error consistently.

AttributeError Traceback (most recent call last) in <cell line: 1>() ----> 1 fed_learning_model = tff.learning.build_federated_averaging_process( 2 model_fn, 3 client_optimizer_fn=client_optimizer, 4 server_optimizer_fn=server_optimizer)

AttributeError: module ‘tensorflow_federated.python.learning’ has no attribute ‘build_federated_averaging_process’

I dont know whether my version suites. my tensorflow federated version is 0.76.0 my tensorflow version is 2.14.1 and python version is 3.10.12

When i search through the internet i saw that this code doesnot support for tensorflow version 0.21.0 onwards. but i dont know what to use in the latest version