91e2f847f6dd9d57af9276d8d4aeee1e1fdc9030,tensorflow_transform/mappers.py,,apply_vocab,#Any#Any#Any#Any#Any#Any#,373

Before Change

    table_size = table.size()
    return table.lookup(y), table_size

  with tf.name_scope(name, "apply_vocab"):
    lookup_fn = lookup_fn or _apply_vocab

    result, table_size = api.apply_function(
        lookup_fn, x, deferred_vocab_filename_tensor)

    // Specify schema overrides which will override the values in the schema
    // with the min and max values, which are deferred as they are only known
    // once the analyzer has run.
    //
    // `table_size` includes the num oov buckets.  The default value is only used
    // if num_oov_buckets > 0.
    min_value = tf.constant(0, tf.int64)
    max_value = table_size - 1
    if num_oov_buckets <= 0:
      min_value = tf.minimum(min_value, default_value)
      max_value = tf.maximum(max_value, default_value)
    api.set_tensor_schema_overrides(result, min_value, max_value)

    return result


def segment_indices(segment_ids, name=None):
  Returns a `Tensor` of indices within each segment.

  segment_ids should be a sequence of non-decreasing non-negative integers that

After Change

                lookup_fn=None,
                name=None):
  rSee `tft.apply_vocabulary`.
  return apply_vocabulary(
      x=x,
      deferred_vocab_filename_tensor=deferred_vocab_filename_tensor,
      default_value=default_value,
      num_oov_buckets=num_oov_buckets,
      lookup_fn=lookup_fn,
      name=name)


def segment_indices(segment_ids, name=None):
  Returns a `Tensor` of indices within each segment.

Instances