parameters: ()
return_statement: return _apply_fn
docstring: Creates a `Dataset` from another `Dataset` and silently ignores any errors.
  
    Use this transformation to produce a dataset that contains the same elements
    as the input, but silently drops any elements that caused an error. For
    example:
  
    ```python
    dataset = tf.data.Dataset.from_tensor_slices([1., 2., 0., 4.])
  
    # Computing `tf.check_numerics(1. / 0.)` will raise an InvalidArgumentError.
    dataset = dataset.map(lambda x: tf.check_numerics(1. / x, "error"))
  
    # Using `ignore_errors()` will drop the element that causes an error.
    dataset =
        dataset.apply(tf.contrib.data.ignore_errors())  # ==> { 1., 0.5, 0.2 }
    ```
  
    Returns:
      A `Dataset` transformation function, which can be passed to
      @{tf.data.Dataset.apply}.
docstring_summary: Creates a `Dataset` from another `Dataset` and silently ignores any errors.
docstring_tokens: Creates a Dataset from another Dataset and silently ignores any errors .
function: def ignore_errors():
    """Creates a `Dataset` from another `Dataset` and silently ignores any errors.
  
    Use this transformation to produce a dataset that contains the same elements
    as the input, but silently drops any elements that caused an error. For
    example:
  
    ```python
    dataset = tf.data.Dataset.from_tensor_slices([1., 2., 0., 4.])
  
    # Computing `tf.check_numerics(1. / 0.)` will raise an InvalidArgumentError.
    dataset = dataset.map(lambda x: tf.check_numerics(1. / x, "error"))
  
    # Using `ignore_errors()` will drop the element that causes an error.
    dataset =
        dataset.apply(tf.contrib.data.ignore_errors())  # ==> { 1., 0.5, 0.2 }
    ```
  
    Returns:
      A `Dataset` transformation function, which can be passed to
      @{tf.data.Dataset.apply}.
    """
  
    def _apply_fn(dataset):
      return IgnoreErrorsDataset(dataset)
  
    return _apply_fn
function_tokens: def ignore_errors ( ) : def _apply_fn ( dataset ) : return IgnoreErrorsDataset ( dataset ) return _apply_fn