parameters: (params: ragged_tensor.RaggedOrDense,
                   indices: ragged_tensor.RaggedOrDense,
                   name=None)
return_statement: return ragged_gather_ops.gather(params, indices, batch_dims=-1, name=name)
docstring: Gathers slices from `params` according to `indices` with batch dims.
  
    This operation is similar to `gather`, but it assumes that the leading `N`
    dimensions of `indices` and `params` are batch dimensions, and performs a
    gather within each batch.  In particular, when using this operation with `N`
    batch dimensions `B1...BN`:
  
    * `indices` has shape `[B1...BN, I]`
    * `params` has shape `[B1...BN, P1...PM]`.
    * `result` has shape `[B1...BN, I, P2...PM]`.
    * `result[b1...bN, i, p2...pM] =
      params[b1...bN, indices[b1...bN, i], p2...pM]`
  
    Args:
      params: A potentially ragged tensor with shape `[B1...BN, P1...PM]` (`N>=0`,
        `M>0`).
      indices: A potentially ragged tensor with shape `[B1...BN, I]` (`N>=0`).
      name: A name for the operation (optional).
  
    Returns:
      A potentially ragged tensor with shape `[B1...BN, I, P2...PM]`.
      `result.ragged_rank = max(indices.ragged_rank, params.ragged_rank)`.
  
    #### Example:
  
    >>> params = tf.ragged.constant([['a', 'b', 'c'], ['d'], [], ['e']])
    >>> indices = tf.ragged.constant([[1, 2, 0], [], [], [0, 0]])
    >>> tf.compat.v1.batch_gather(params, indices)
    <tf.RaggedTensor [[b'b', b'c', b'a'], [], [], [b'e', b'e']]>
docstring_summary: Gathers slices from `params` according to `indices` with batch dims.
docstring_tokens: Gathers slices from params according to indices with batch dims .
function: def batch_gather(params: ragged_tensor.RaggedOrDense,
                   indices: ragged_tensor.RaggedOrDense,
                   name=None):
    """Gathers slices from `params` according to `indices` with batch dims.
  
    This operation is similar to `gather`, but it assumes that the leading `N`
    dimensions of `indices` and `params` are batch dimensions, and performs a
    gather within each batch.  In particular, when using this operation with `N`
    batch dimensions `B1...BN`:
  
    * `indices` has shape `[B1...BN, I]`
    * `params` has shape `[B1...BN, P1...PM]`.
    * `result` has shape `[B1...BN, I, P2...PM]`.
    * `result[b1...bN, i, p2...pM] =
      params[b1...bN, indices[b1...bN, i], p2...pM]`
  
    Args:
      params: A potentially ragged tensor with shape `[B1...BN, P1...PM]` (`N>=0`,
        `M>0`).
      indices: A potentially ragged tensor with shape `[B1...BN, I]` (`N>=0`).
      name: A name for the operation (optional).
  
    Returns:
      A potentially ragged tensor with shape `[B1...BN, I, P2...PM]`.
      `result.ragged_rank = max(indices.ragged_rank, params.ragged_rank)`.
  
    #### Example:
  
    >>> params = tf.ragged.constant([['a', 'b', 'c'], ['d'], [], ['e']])
    >>> indices = tf.ragged.constant([[1, 2, 0], [], [], [0, 0]])
    >>> tf.compat.v1.batch_gather(params, indices)
    <tf.RaggedTensor [[b'b', b'c', b'a'], [], [], [b'e', b'e']]>
    """
    return ragged_gather_ops.gather(params, indices, batch_dims=-1, name=name)
function_tokens: def batch_gather ( params : ragged_tensor . RaggedOrDense , indices : ragged_tensor . RaggedOrDense , name = None ) : return ragged_gather_ops . gather ( params , indices , batch_dims = - 1 , name = name )