tensorflow_ranking/python/feature.py [140:182]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                             scope=None):
  """Returns dense tensors from features using feature columns.

  Args:
    features: (dict) mapping feature names (str) to feature values (`tf.Tensor`
      or `tf.SparseTensor`), possibly obtained from input_fn. For context
      features, the tensors are 2-D, while for example features the tensors are
      3-D.
    context_feature_columns: (dict) context feature names to columns.
    example_feature_columns: (dict) example feature names to columns.
    input_size: (int) [DEPRECATED: Use without this argument.] number of
      examples per query. If this is None, input_size is inferred as the size
      of second dimension of the Tensor corresponding to one of the example
      feature columns.
    mode: (`estimator.ModeKeys`) Specifies if this is training, evaluation or
      inference. See `ModeKeys`.
    scope: (str) variable scope for the per column input layers.

  Returns:
    context_features: (dict) A mapping from context feature names to dense
    2-D tensors of shape [batch_size, ...].
    example_features: (dict) A mapping from example feature names to dense
    3-D tensors of shape [batch_size, input_size, ...].

  Raises:
    ValueError: If `input size` is not equal to 2nd dimension of example
    tensors.
  """
  context_features = {}
  if context_feature_columns:
    context_cols_to_tensors = encode_features(
        features, context_feature_columns.values(), mode=mode, scope=scope)
    context_features = {
        name: context_cols_to_tensors[col]
        for name, col in six.iteritems(context_feature_columns)
    }

  # Compute example_features. Note that the keys in `example_feature_columns`
  # dict can be different from the keys in the `features` dict. We only need to
  # reshape the per-example tensors in `features`. To obtain the keys for
  # per-example features, we use the parsing feature specs.
  example_features = {}
  if example_feature_columns:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



tensorflow_ranking/python/feature.py [228:256]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                              scope=None):
  """Returns dense tensors from pointwise features using feature columns.

  Args:
    features: (dict) mapping feature names to 2-D tensors, possibly obtained
      from input_fn.
    context_feature_columns: (dict) context feature names to columns.
    example_feature_columns: (dict) example feature names to columns.
    mode: (`estimator.ModeKeys`) Specifies if this is training, evaluation or
      inference. See `ModeKeys`.
    scope: (str) variable scope for the per column input layers.

  Returns:
    context_features: (dict) A mapping from context feature names to dense
    2-D tensors of shape [batch_size, ...].
    example_features: (dict) A mapping from example feature names to dense
    3-D tensors of shape [batch_size, 1, ...].
  """
  context_features = {}
  if context_feature_columns:
    context_cols_to_tensors = encode_features(
        features, context_feature_columns.values(), mode=mode, scope=scope)
    context_features = {
        name: context_cols_to_tensors[col]
        for name, col in six.iteritems(context_feature_columns)
    }

  example_features = {}
  if example_feature_columns:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -