code: def build_training_data_loader(self) -> keras.InputData:
      # Ignore header line and read the training CSV observations into a pandas DataFrame.
      train = pd.read_csv(
          self.context.get_data_config()["train_url"],
          names=DS_COLUMNS,
          header=0,
      )
      train_features, train_labels = train, train.pop(LABEL_HEADER)
  
      # Since we're building a classifier, convert the labels in the raw
      # dataset (0, 1, or 2) to one-hot vector encodings that we'll to
      # construct the Sequence data loaders that Determined expects.
      train_labels_categorical = to_categorical(
          train_labels, num_classes=3
      )
  
      return train_features.values, train_labels_categorical
  
file_path: determined/examples/computer_vision/iris_tf_keras/model_def.py
imports: from typing import List
  import pandas
  import tensorflow
  from tensorflow.keras.layers import Dense, Input
  from tensorflow.keras.losses import categorical_crossentropy
  from tensorflow.keras.metrics import categorical_accuracy
  from tensorflow.keras.models import Model
  from tensorflow.keras.optimizers.legacy import RMSprop
  from tensorflow.keras.utils import to_categorical
  from determined import keras
label_desc: Use image node_id 4 for calling the IrisTrial obj's underlying member method code with example usage: obj.build_training_data_loader() and returns: train_features,  train_labels_categorical