parameters: (
        self, x=None, y=None, input_fn=None, feed_fn=None, batch_size=None,
        steps=None, metrics=None, name=None)
docstring: Evaluates given model with provided evaluation data.
  
      Evaluates on the given input data. If `input_fn` is provided, that
      input function should raise an end-of-input exception (`OutOfRangeError` or
      `StopIteration`) after one epoch of the training data has been provided.
  
      By default, the whole evaluation dataset is used. If `steps` is provided,
      only `steps` batches of size `batch_size` are processed.
  
      The return value is a dict containing the metrics specified in `metrics`, as
      well as an entry `global_step` which contains the value of the global step
      for which this evaluation was performed.
  
      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`.
        feed_fn: Function creating a feed dict every time it is called. Called
          once per iteration. Must be `None` if `input_fn` is provided.
        batch_size: minibatch size to use on the input, defaults to first
          dimension of `x`, if specified. Must be `None` if `input_fn` is
          provided.
        steps: Number of steps for which to evaluate model. If `None`, evaluate
          until running tensors generated by `metrics` raises an exception.
        metrics: Dict of metric ops to run. If `None`, the default metric
          functions are used; if `{}`, no metrics are used. If model has one
          output (i.e., returning single predction), keys are `str`, e.g.
          `'accuracy'` - just a name of the metric that will show up in
          the logs / summaries. Otherwise, keys are tuple of two `str`, e.g.
          `('accuracy', 'classes')`- name of the metric and name of `Tensor` in
          the predictions to run this metric on.
  
          Metric ops should support streaming, e.g., returning
          update_op and value tensors. See more details in
          ../../../metrics/python/metrics/ops/streaming_metrics.py.
        name: Name of the evaluation if user needs to run multiple evaluations on
          different data sets, such as on training data vs test data.
  
      Returns:
        Returns `dict` with evaluation results.
docstring_summary: Evaluates given model with provided evaluation data.
docstring_tokens: Evaluates given model with provided evaluation data .
function: def evaluate(
        self, x=None, y=None, input_fn=None, feed_fn=None, batch_size=None,
        steps=None, metrics=None, name=None):
      """Evaluates given model with provided evaluation data.
  
      Evaluates on the given input data. If `input_fn` is provided, that
      input function should raise an end-of-input exception (`OutOfRangeError` or
      `StopIteration`) after one epoch of the training data has been provided.
  
      By default, the whole evaluation dataset is used. If `steps` is provided,
      only `steps` batches of size `batch_size` are processed.
  
      The return value is a dict containing the metrics specified in `metrics`, as
      well as an entry `global_step` which contains the value of the global step
      for which this evaluation was performed.
  
      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`.
        feed_fn: Function creating a feed dict every time it is called. Called
          once per iteration. Must be `None` if `input_fn` is provided.
        batch_size: minibatch size to use on the input, defaults to first
          dimension of `x`, if specified. Must be `None` if `input_fn` is
          provided.
        steps: Number of steps for which to evaluate model. If `None`, evaluate
          until running tensors generated by `metrics` raises an exception.
        metrics: Dict of metric ops to run. If `None`, the default metric
          functions are used; if `{}`, no metrics are used. If model has one
          output (i.e., returning single predction), keys are `str`, e.g.
          `'accuracy'` - just a name of the metric that will show up in
          the logs / summaries. Otherwise, keys are tuple of two `str`, e.g.
          `('accuracy', 'classes')`- name of the metric and name of `Tensor` in
          the predictions to run this metric on.
  
          Metric ops should support streaming, e.g., returning
          update_op and value tensors. See more details in
          ../../../metrics/python/metrics/ops/streaming_metrics.py.
        name: Name of the evaluation if user needs to run multiple evaluations on
          different data sets, such as on training data vs test data.
  
      Returns:
        Returns `dict` with evaluation results.
      """
      raise NotImplementedError
function_tokens: def evaluate ( self , x = None , y = None , input_fn = None , feed_fn = None , batch_size = None , steps = None , metrics = None , name = None ) : raise NotImplementedError