code: def train_test_split(pd_dataframe):
      """
      Training data - 45 initial at-bats and hits for each player.
      Validation data - Full season at-bats and hits for each player.
      """
      device = torch.Tensor().device
      train_data = torch.tensor(
          pd_dataframe[["At-Bats", "Hits"]].values,
          dtype=torch.float,
          device=device,
      )
      test_data = torch.tensor(
          pd_dataframe[["SeasonAt-Bats", "SeasonHits"]].values,
          dtype=torch.float,
          device=device,
      )
      first_name = pd_dataframe["FirstName"].values
      last_name = pd_dataframe["LastName"].values
      player_names = [
          " ".join([first, last])
          for first, last in zip(first_name, last_name)
      ]
      return train_data, test_data, player_names
  
file_path: pyro/examples/baseball.py
imports: import argparse
  import logging
  import math
  import pandas
  import torch
  import pyro
  from pyro.distributions import Beta, Binomial, HalfCauchy, Normal, Pareto, Uniform
  from pyro.distributions.util import scalar_like
  from pyro.infer import MCMC, NUTS, Predictive
  from pyro.infer.mcmc.util import initialize_model, summary
  from pyro.util import ignore_experimental_warning
label_desc: Use image node_id 6 for calling a global function with example usage: train_test_split(pd_dataframe) and returns: train_data,  test_data,  player_names