parameters: (outputs)
return_statement: return np.concatenate(outputs)
docstring: Concats prediction outputs along the batch dimension.
docstring_summary: Concats prediction outputs along the batch dimension.
docstring_tokens: Concats prediction outputs along the batch dimension .
function: def concat_along_batch_dimension(outputs):
    """Concats prediction outputs along the batch dimension."""
    if isinstance(outputs[0], sparse_tensor.SparseTensor):
      return sparse_ops.sparse_concat_v2(axis=0, sp_inputs=outputs)
    if isinstance(outputs[0], ragged_tensor.RaggedTensor):
      return array_ops.concat(outputs, axis=0)
    return np.concatenate(outputs)
function_tokens: def concat_along_batch_dimension ( outputs ) : if isinstance ( outputs [ 0 ] , sparse_tensor . SparseTensor ) : return sparse_ops . sparse_concat_v2 ( axis = 0 , sp_inputs = outputs ) if isinstance ( outputs [ 0 ] , ragged_tensor . RaggedTensor ) : return array_ops . concat ( outputs , axis = 0 ) return np . concatenate ( outputs )