def _slice_with_actions(embeddings, actions):
  """Slice a Tensor.

  Take embeddings of the form [batch_size, num_actions, embed_dim]
  and actions of the form [batch_size, 1], and return the sliced embeddings
  like embeddings[:, actions, :].

  Args:
    embeddings: Tensor of embeddings to index.
    actions: int Tensor to use as index into embeddings

  Returns:
    Tensor of embeddings indexed by actions
  """
  batch_size, num_actions = embeddings.get_shape()[:2]

  # Values are the 'values' in a sparse tensor we will be setting
  act_indx = tf.cast(actions, tf.int64)[:, None]
  values = tf.reshape(tf.cast(tf.ones(tf.shape(actions)), tf.bool), [-1])

  # Create a range for each index into the batch
  act_range = tf.range(0, batch_size, dtype=tf.int64)[:, None]
  # Combine this into coordinates with the action indices
  indices = tf.concat([act_range, act_indx], 1)

  actions_mask = tf.SparseTensor(indices, values, [batch_size, num_actions])
  actions_mask = tf.stop_gradient(
      tf.sparse_tensor_to_dense(actions_mask, default_value=False))
  sliced_emb = tf.boolean_mask(embeddings, actions_mask)
  return sliced_emb
Slice a Tensor.

  Take embeddings of the form [batch_size, num_actions, embed_dim]
  and actions of the form [batch_size, 1], and return the sliced embeddings
  like embeddings[:, actions, :].

  Args:
    embeddings: Tensor of embeddings to index.
    actions: int Tensor to use as index into embeddings

  Returns:
    Tensor of embeddings indexed by actions
  
trfl/dist_value_ops.py