An iterable containing all feature columns. All items should be instances of classes derived from _FeatureColumn.

A string identifying the label. It means tf$Example stores labels with this key.

A tf$dtype identifies the type of labels. By default it is tf$int64. If user defines a label_vocabulary, this should be set as tf$string. tf$float32 labels are only supported for binary classification.

used as label if label_key does not exist in given tf$Example. An example usage: let's say label_key is 'clicked' and tf$Example contains clicked data only for positive examples in following format key:clicked, value:1. This means that if there is no data with key 'clicked' it should count as negative example by setting label_deafault=0. Type of this value should be compatible with label_dtype.

A string or a numeric column created by column_numeric() defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. If it is a string, it is used as a key to fetch weight tensor from the features. If it is a numeric column, raw tensor is fetched by key weight_column$key, then weight_column$normalizer_fn is applied on it to get weight tensor.