tf.contrib.learn.LinearRegressor.export()

Args:
  export_dir: A string containing a directory to write the exported graph
    and checkpoints.
  input_fn: If `use_deprecated_input_fn` is true, then a function that given
    `Tensor` of `Example` strings, parses it into features that are then
    passed to the model. Otherwise, a function that takes no argument and
    returns a tuple of (features, targets), where features is a dict of
    string key to `Tensor` and targets is a `Tensor` that's currently not
    used (and so can be `None`).
  input_feature_key: Only used if `use_deprecated_input_fn` is false. String
    key into the features dict returned by `input_fn` that corresponds to
    the raw `Example` strings `Tensor` that the exported model will take as
    input. Can only be `None` if you're using a custom `signature_fn` that
    does not use the first arg (examples).
  use_deprecated_input_fn: Determines the signature format of `input_fn`.
  signature_fn: Function that returns a default signature and a named
    signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
    for features and `Tensor` or `dict` of `Tensor`s for predictions.
  prediction_key: The key for a tensor in the `predictions` dict (output
    from the `model_fn`) to use as the `predictions` input to the
    `signature_fn`. Optional. If `None`, predictions will pass to
    `signature_fn` without filtering.
  default_batch_size: Default batch size of the `Example` placeholder.
  exports_to_keep: Number of exports to keep.

Returns:
  The string path to the exported directory. NB: this functionality was
  added ca. 2016/09/25; clients that depend on the return value may need
  to handle the case where this function returns None because subclasses
  are not returning a value.