parameters: (self, x=None, input_fn=None, batch_size=None, outputs=None,
                as_iterable=True)
return_statement: return super(LinearRegressor, self).predict(
          x=x,
          input_fn=input_fn,
          batch_size=batch_size,
          outputs=outputs,
          as_iterable=as_iterable)
docstring: Returns predictions for given features.
  
      By default, returns predicted scores. But this default will be dropped
      soon. Users should either pass `outputs`, or call `predict_scores` method.
  
      Args:
        x: features.
        input_fn: Input function. If set, x must be None.
        batch_size: Override default batch size.
        outputs: list of `str`, name of the output to predict.
          If `None`, returns scores.
        as_iterable: If True, return an iterable which keeps yielding predictions
          for each example until inputs are exhausted. Note: The inputs must
          terminate if you want the iterable to terminate (e.g. be sure to pass
          num_epochs=1 if you are using something like read_batch_features).
  
      Returns:
        Numpy array of predicted scores (or an iterable of predicted scores if
        as_iterable is True). If `label_dimension == 1`, the shape of the output
        is `[batch_size]`, otherwise the shape is `[batch_size, label_dimension]`.
        If `outputs` is set, returns a dict of predictions.
docstring_summary: Returns predictions for given features.
docstring_tokens: Returns predictions for given features .
function: def predict(self, x=None, input_fn=None, batch_size=None, outputs=None,
                as_iterable=True):
      """Returns predictions for given features.
  
      By default, returns predicted scores. But this default will be dropped
      soon. Users should either pass `outputs`, or call `predict_scores` method.
  
      Args:
        x: features.
        input_fn: Input function. If set, x must be None.
        batch_size: Override default batch size.
        outputs: list of `str`, name of the output to predict.
          If `None`, returns scores.
        as_iterable: If True, return an iterable which keeps yielding predictions
          for each example until inputs are exhausted. Note: The inputs must
          terminate if you want the iterable to terminate (e.g. be sure to pass
          num_epochs=1 if you are using something like read_batch_features).
  
      Returns:
        Numpy array of predicted scores (or an iterable of predicted scores if
        as_iterable is True). If `label_dimension == 1`, the shape of the output
        is `[batch_size]`, otherwise the shape is `[batch_size, label_dimension]`.
        If `outputs` is set, returns a dict of predictions.
      """
      if not outputs:
        return self.predict_scores(
            x=x,
            input_fn=input_fn,
            batch_size=batch_size,
            as_iterable=as_iterable)
      return super(LinearRegressor, self).predict(
          x=x,
          input_fn=input_fn,
          batch_size=batch_size,
          outputs=outputs,
          as_iterable=as_iterable)
function_tokens: def predict ( self , x = None , input_fn = None , batch_size = None , outputs = None , as_iterable = True ) : if not outputs : return self . predict_scores ( x = x , input_fn = input_fn , batch_size = batch_size , as_iterable = as_iterable ) return super ( LinearRegressor , self ) . predict ( x = x , input_fn = input_fn , batch_size = batch_size , outputs = outputs , as_iterable = as_iterable )