parameters: (input_tensor_alias,
          input_tensors,
          output_tensor_alias,
          target_address,
          model_name,
          model_version=-1,
          max_rpc_deadline_millis=3000,
          output_types=None,
          name=None,
          signature_name='serving_default')
return_statement: return (gen_remote_predict_op.tf_serving_remote_predict(
        input_tensor_alias,
        input_tensors,
        output_tensor_alias,
        target_address=target_address,
        model_name=model_name,
        model_version=model_version,
        fail_op_on_rpc_error=True,
        max_rpc_deadline_millis=max_rpc_deadline_millis,
        signature_name=signature_name,
        output_types=output_types,
        name=name))[2]
docstring: Runs a predict in remote process through rpc.
  
    Args:
      input_tensor_alias: input tensor alias for Predict
      input_tensors: input tensors for Predict
      output_tensor_alias: output tensor alias for Predict
      target_address: target_address where the rpc is sent to
      model_name: model_name that the Predict is running on
      model_version: the model version for the Predict call. If unset, the highest
        version available for serving will be targeted.
      max_rpc_deadline_millis: rpc deadline in millis
      output_types: output types for Predict
      name: name for the op in the graph
      signature_name: the signature def for remote graph inference
  
    Returns:
      output_tensors as a result of the Predict.
  
    Raises ValueError if model_name value is missing.
docstring_summary: Runs a predict in remote process through rpc.
docstring_tokens: Runs a predict in remote process through rpc .
function: def run(input_tensor_alias,
          input_tensors,
          output_tensor_alias,
          target_address,
          model_name,
          model_version=-1,
          max_rpc_deadline_millis=3000,
          output_types=None,
          name=None,
          signature_name='serving_default'):
    """Runs a predict in remote process through rpc.
  
    Args:
      input_tensor_alias: input tensor alias for Predict
      input_tensors: input tensors for Predict
      output_tensor_alias: output tensor alias for Predict
      target_address: target_address where the rpc is sent to
      model_name: model_name that the Predict is running on
      model_version: the model version for the Predict call. If unset, the highest
        version available for serving will be targeted.
      max_rpc_deadline_millis: rpc deadline in millis
      output_types: output types for Predict
      name: name for the op in the graph
      signature_name: the signature def for remote graph inference
  
    Returns:
      output_tensors as a result of the Predict.
  
    Raises ValueError if model_name value is missing.
    """
    if model_name is None:
      raise ValueError('model_name must be specified.')
    return (gen_remote_predict_op.tf_serving_remote_predict(
        input_tensor_alias,
        input_tensors,
        output_tensor_alias,
        target_address=target_address,
        model_name=model_name,
        model_version=model_version,
        fail_op_on_rpc_error=True,
        max_rpc_deadline_millis=max_rpc_deadline_millis,
        signature_name=signature_name,
        output_types=output_types,
        name=name))[2]
function_tokens: def run ( input_tensor_alias , input_tensors , output_tensor_alias , target_address , model_name , model_version = - 1 , max_rpc_deadline_millis = 3000 , output_types = None , name = None , signature_name = 'serving_default' ) : if model_name is None : raise ValueError ( 'model_name must be specified.' ) return ( gen_remote_predict_op . tf_serving_remote_predict ( input_tensor_alias , input_tensors , output_tensor_alias , target_address = target_address , model_name = model_name , model_version = model_version , fail_op_on_rpc_error = True , max_rpc_deadline_millis = max_rpc_deadline_millis , signature_name = signature_name , output_types = output_types , name = name ) ) [ 2 ]