parameters: (self,
                 export_dir,
                 signature_def_key=None,
                 signature_def=None,
                 input_names=None,
                 output_names=None,
                 tags=None,
                 graph=None)
docstring: Initialize a `CoreEstimatorPredictor`.
  
      Args:
        export_dir: a path to a directory containing a `SavedModel`.
        signature_def_key: Optional string specifying the signature to use. If
          `None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used. Only one of
          `signature_def_key` and `signature_def` should be specified.
        signature_def: A `SignatureDef` proto specifying the inputs and outputs
          for prediction. Only one of `signature_def_key` and `signature_def`
          should be specified.
        input_names: A dictionary mapping strings to `Tensor`s in the `SavedModel`
          that represent the input. The keys can be any string of the user's
          choosing.
        output_names: A dictionary mapping strings to `Tensor`s in the
          `SavedModel` that represent the output. The keys can be any string of
          the user's choosing.
        tags: Optional. Comma separated list of tags that will be used to retrieve
          the correct `SignatureDef`. Defaults to `DEFAULT_TAGS`.
        graph: Optional. The Tensorflow `graph` in which prediction should be
          done.
      Raises:
        ValueError: If more than one of signature_def_key OR signature_def OR
          (input_names AND output_names) is specified.
docstring_summary: Initialize a `CoreEstimatorPredictor`.
docstring_tokens: Initialize a CoreEstimatorPredictor .
function: def __init__(self,
                 export_dir,
                 signature_def_key=None,
                 signature_def=None,
                 input_names=None,
                 output_names=None,
                 tags=None,
                 graph=None):
      """Initialize a `CoreEstimatorPredictor`.
  
      Args:
        export_dir: a path to a directory containing a `SavedModel`.
        signature_def_key: Optional string specifying the signature to use. If
          `None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used. Only one of
          `signature_def_key` and `signature_def` should be specified.
        signature_def: A `SignatureDef` proto specifying the inputs and outputs
          for prediction. Only one of `signature_def_key` and `signature_def`
          should be specified.
        input_names: A dictionary mapping strings to `Tensor`s in the `SavedModel`
          that represent the input. The keys can be any string of the user's
          choosing.
        output_names: A dictionary mapping strings to `Tensor`s in the
          `SavedModel` that represent the output. The keys can be any string of
          the user's choosing.
        tags: Optional. Comma separated list of tags that will be used to retrieve
          the correct `SignatureDef`. Defaults to `DEFAULT_TAGS`.
        graph: Optional. The Tensorflow `graph` in which prediction should be
          done.
      Raises:
        ValueError: If more than one of signature_def_key OR signature_def OR
          (input_names AND output_names) is specified.
      """
      _check_signature_arguments(
          signature_def_key, signature_def, input_names, output_names)
      tags = tags or DEFAULT_TAGS
      self._graph = graph or ops.Graph()
  
      with self._graph.as_default():
        self._session = session.Session()
        loader.load(self._session, tags.split(','), export_dir)
  
      if input_names is None:
        if signature_def is None:
          signature_def = _get_signature_def(signature_def_key, export_dir, tags)
        input_names = {k: v.name for k, v in signature_def.inputs.items()}
        output_names = {k: v.name for k, v in signature_def.outputs.items()}
  
      self._feed_tensors = {k: self._graph.get_tensor_by_name(v)
                            for k, v in input_names.items()}
      self._fetch_tensors = {k: self._graph.get_tensor_by_name(v)
                             for k, v in output_names.items()}
function_tokens: def __init__ ( self , export_dir , signature_def_key = None , signature_def = None , input_names = None , output_names = None , tags = None , graph = None ) : _check_signature_arguments ( signature_def_key , signature_def , input_names , output_names ) tags = tags or DEFAULT_TAGS self . _graph = graph or ops . Graph ( ) with self . _graph . as_default ( ) : self . _session = session . Session ( ) loader . load ( self . _session , tags . split ( ',' ) , export_dir ) if input_names is None : if signature_def is None : signature_def = _get_signature_def ( signature_def_key , export_dir , tags ) input_names = { k : v . name for k , v in signature_def . inputs . items ( ) } output_names = { k : v . name for k , v in signature_def . outputs . items ( ) } self . _feed_tensors = { k : self . _graph . get_tensor_by_name ( v ) for k , v in input_names . items ( ) } self . _fetch_tensors = { k : self . _graph . get_tensor_by_name ( v ) for k , v in output_names . items ( ) }