code: def ignore_errors(log_warning=False):
      """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.debugging.check_numerics(1. / 0.)` will raise an
      InvalidArgumentError.
      dataset = dataset.map(lambda x: tf.debugging.check_numerics(1. / x, "error"))
  
      # Using `ignore_errors()` will drop the element that causes an error.
      dataset =
          dataset.apply(tf.data.experimental.ignore_errors())  # ==> {1., 0.5, 0.2}
      ```
      Args:
         log_warning: (Optional.) A 'tf.bool' scalar indicating whether ignored
          errors should be logged to stderr. Defaults to 'False'.
  
      Returns:
        A `Dataset` transformation function, which can be passed to
        `tf.data.Dataset.apply`.
      """
  
      def _apply_fn(dataset):
          return dataset.ignore_errors(log_warning)
  
      return _apply_fn
  
label_desc: Use image node_id 1 for calling a global function with example usage: ignore_errors(log_warning) and returns: _apply_fn, dataset