predict_savedmodel.graph_prediction: Predict using a Loaded SavedModel
In tfdeploy: Deploy 'TensorFlow' Models

Performs a prediction using a SavedModel model already loaded using load_savedmodel().

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## S3 method for class 'graph_prediction'
predict_savedmodel(instances, model, sess,
  signature_name = "serving_default", ...)

`instances`	A list of prediction instances to be passed as input tensors to the service. Even for single predictions, a list with one entry is expected.
`model`	The model as a local path, a REST url or graph object. A local path can be exported using `export_savedmodel()`, a REST URL can be created using `serve_savedmodel()` and a graph object loaded using `load_savedmodel()`. A `type` parameter can be specified to explicitly choose the type model performing the prediction. Valid values are `export`, `webapi` and `graph`.
`sess`	The active TensorFlow session.
`signature_name`	The named entry point to use in the model for prediction.
`...`	See `predict_savedmodel.export_prediction()`, `predict_savedmodel.graph_prediction()`, `predict_savedmodel.webapi_prediction()` for additional options. #' @section Implementations: `predict_savedmodel.export_prediction()` `predict_savedmodel.graph_prediction()` `predict_savedmodel.webapi_prediction()`]