parameters: (
        self, x=None, y=None, input_fn=None, steps=1, batch_size=None,
        monitors=None)
return_statement: return self.fit(x=x, y=y, input_fn=input_fn, steps=steps,
                      batch_size=batch_size, monitors=monitors)
docstring: Incremental fit on a batch of samples.
  
      This method is expected to be called several times consecutively
      on different or the same chunks of the dataset. This either can
      implement iterative training or out-of-core/online training.
  
      This is especially useful when the whole dataset is too big to
      fit in memory at the same time. Or when model is taking long time
      to converge, and you want to split up training into subparts.
  
      Args:
        x: Matrix of shape [n_samples, n_features...]. Can be iterator that
           returns arrays of features. The training input samples for fitting the
           model. If set, `input_fn` must be `None`.
        y: Vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
           iterator that returns array of targets. The training target values
           (class labels in classification, real numbers in regression). If set,
           `input_fn` must be `None`.
        input_fn: Input function. If set, `x`, `y`, and `batch_size` must be
          `None`.
        steps: Number of steps for which to train model. If `None`, train forever.
        batch_size: minibatch size to use on the input, defaults to first
          dimension of `x`. Must be `None` if `input_fn` is provided.
        monitors: List of `BaseMonitor` subclass instances. Used for callbacks
          inside the training loop.
  
      Returns:
        `self`, for chaining.
  
      Raises:
        ValueError: If at least one of `x` and `y` is provided, and `input_fn` is
            provided.
docstring_summary: Incremental fit on a batch of samples.
docstring_tokens: Incremental fit on a batch of samples .
function: def partial_fit(
        self, x=None, y=None, input_fn=None, steps=1, batch_size=None,
        monitors=None):
      """Incremental fit on a batch of samples.
  
      This method is expected to be called several times consecutively
      on different or the same chunks of the dataset. This either can
      implement iterative training or out-of-core/online training.
  
      This is especially useful when the whole dataset is too big to
      fit in memory at the same time. Or when model is taking long time
      to converge, and you want to split up training into subparts.
  
      Args:
        x: Matrix of shape [n_samples, n_features...]. Can be iterator that
           returns arrays of features. The training input samples for fitting the
           model. If set, `input_fn` must be `None`.
        y: Vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
           iterator that returns array of targets. The training target values
           (class labels in classification, real numbers in regression). If set,
           `input_fn` must be `None`.
        input_fn: Input function. If set, `x`, `y`, and `batch_size` must be
          `None`.
        steps: Number of steps for which to train model. If `None`, train forever.
        batch_size: minibatch size to use on the input, defaults to first
          dimension of `x`. Must be `None` if `input_fn` is provided.
        monitors: List of `BaseMonitor` subclass instances. Used for callbacks
          inside the training loop.
  
      Returns:
        `self`, for chaining.
  
      Raises:
        ValueError: If at least one of `x` and `y` is provided, and `input_fn` is
            provided.
      """
      logging.warning('The current implementation of partial_fit is not optimized'
                      'for use in a loop. Consider using fit() instead.')
      return self.fit(x=x, y=y, input_fn=input_fn, steps=steps,
                      batch_size=batch_size, monitors=monitors)
function_tokens: def partial_fit ( self , x = None , y = None , input_fn = None , steps = 1 , batch_size = None , monitors = None ) : logging . warning ( 'The current implementation of partial_fit is not optimized' 'for use in a loop. Consider using fit() instead.' ) return self . fit ( x = x , y = y , input_fn = input_fn , steps = steps , batch_size = batch_size , monitors = monitors )