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An Estimator input_fn for running predict() after evaluate().

If the call to evaluate() we are making predictions based on had a batch_size greater than one, predictions will start after each of these windows (i.e. will have the same batch dimension).

evaluation The dictionary returned by Estimator.evaluate, with keys FilteringResults.STATE_TUPLE and FilteringResults.TIMES.
steps The number of steps to predict (scalar), starting after the evaluation. If times is specified, steps must not be; one is required.
times A [batch_size x window_size] array of integers (not a Tensor) indicating times to make predictions for. These times must be after the corresponding evaluation. If steps is specified, times must not be; one is required. If the batch dimension is omitted, it is assumed to be 1.
exogenous_features Optional dictionary. If specified, indicates exogenous features for the model to use while making the predictions. Values must have shape [batch_size x window_size x ...], where batch_size matches the batch dimension used when creating evaluation, and window_size is either the steps argument or the window_size of the times argument (depending on which was specified).

An input_fn suitable for passing to the predict function of a time series Estimator.

ValueError If times or steps are misspecified.