tff.simulation.datasets.ClientData to build federated learning model from CSV files

Question

I am building a federated learning model using my own dataset. I aim to build a multi classification model. The data are presented in separate 8 CSV files.

I followed the instructions in this post As shown in the code below.

dataset_paths = {
  'client_0': '/content/ds1.csv',
  'client_1': '/content/ds2.csv',
  'client_2': '/content/ds3.csv',
  'client_3': '/content/ds4.csv',
  'client_4': '/content/ds5.csv',
}

def create_tf_dataset_for_client_fn(id):
   path = dataset_paths.get(id)
   if path is None:
     raise ValueError(f'No dataset for client {id}')
   return tf.data.Dataset.TextLineDataset(path)

source = tff.simulation.datasets.ClientData.from_clients_and_fn(
  dataset_paths.keys(), create_tf_dataset_for_client_fn)

but it gave me this error

AttributeError: type object 'ClientData' has no attribute 'from_clients_and_fn'

I was reading this documentation and found that .datasets methods would work, so I replaced with .from_clients_and_fn and the error disappeared but I dont know if it is right and what is next?

My questions are:

1. it this is a right method to upload the data to the clients?
2. if it is not possible to upload the CSV files separately, can I combine all of the data into one CSV file and then consider them as a non-IID data and train them accordingly? I need some guidance here

and thanks in advance