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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _create_tensor_summary( name, true_positive_counts, false_positive_counts, true_negative_counts, false_negative_counts, precision, recall, num_thresholds=None, display_name=None, description=None, collections=None):
 """A private helper method for generating a tensor summary. We use a helper method instead of having `op` directly call `raw_data_op` to prevent the scope of `raw_data_op` from being embedded within `op`. Arguments are the same as for raw_data_op. Returns: A tensor summary that collects data for PR curves. """

  # TODO(nickfelt): remove on-demand imports once dep situation is fixed.
  import tensorflow.compat.v1 as tf

  # Store the number of thresholds within the summary metadata because
  # that value is constant for all pr curve summaries with the same tag.
  summary_metadata = metadata.create_summary_metadata(
      display_name=display_name if display_name is not None else name,
      description=description or '',
      num_thresholds=num_thresholds)

  # Store values within a tensor. We store them in the order:
  # true positives, false positives, true negatives, false
  # negatives, precision, and recall.
  combined_data = tf.stack([
      tf.cast(true_positive_counts, tf.float32),
      tf.cast(false_positive_counts, tf.float32),
      tf.cast(true_negative_counts, tf.float32),
      tf.cast(false_negative_counts, tf.float32),
      tf.cast(precision, tf.float32),
      tf.cast(recall, tf.float32)])

  return tf.summary.tensor_summary(
      name='pr_curves',
      tensor=combined_data,
      collections=collections,
      summary_metadata=summary_metadata)