7bf2f1298fddc5338c1e88f677dfb05ab997887b,graph_nets/utils_tf.py,,repeat,#Any#Any#Any#Any#,510

Before Change

  Returns:
    The `tf.Tensor` with repeated values.
  
  with tf.name_scope(name):
    cumsum = tf.cumsum(repeats)
    range_ = tf.range(cumsum[-1])

    indicator_matrix = tf.cast(tf.expand_dims(range_, 1) >= cumsum, tf.int32)
    indices = tf.reduce_sum(indicator_matrix, reduction_indices=1)

    shifted_tensor = _axis_to_inside(tensor, axis)
    repeated_shifted_tensor = tf.gather(shifted_tensor, indices)
    repeated_tensor = _inside_to_axis(repeated_shifted_tensor, axis)

    shape = tensor.shape.as_list()
    shape[axis] = None
    repeated_tensor.set_shape(shape)

    return repeated_tensor


def _populate_number_fields(data_dict):
  Returns a dict with the number fields N_NODE, N_EDGE filled in.

  The N_NODE field is filled if the graph contains a non-`None` NODES field;

After Change

  Returns:
    The `tf.Tensor` with repeated values.
  
  return tf.repeat(tensor, repeats, axis=axis, name=name)


def _populate_number_fields(data_dict):
  Returns a dict with the number fields N_NODE, N_EDGE filled in.

Instances