parameters: (serialized_sparse, dtype, rank=None, name=None)
return_statement: return sparse_tensor.SparseTensor(output_indices, output_values, output_shape)
docstring: Deserialize `SparseTensor` objects.
  
    The input `serialized_sparse` must have the shape `[?, ?, ..., ?, 3]` where
    the last dimension stores serialized `SparseTensor` objects and the other N
    dimensions (N >= 0) correspond to a batch. The ranks of the original
    `SparseTensor` objects must all match. When the final `SparseTensor` is
    created, its rank is the rank of the incoming `SparseTensor` objects plus N;
    the sparse tensors have been concatenated along new dimensions, one for each
    batch.
  
    The output `SparseTensor` object's shape values for the original dimensions
    are the max across the input `SparseTensor` objects' shape values for the
    corresponding dimensions. The new dimensions match the size of the batch.
  
    The input `SparseTensor` objects' indices are assumed ordered in
    standard lexicographic order.  If this is not the case, after this
    step run `SparseReorder` to restore index ordering.
  
    For example, if the serialized input is a `[2 x 3]` matrix representing two
    original `SparseTensor` objects:
  
        index = [ 0]
                [10]
                [20]
        values = [1, 2, 3]
        shape = [50]
  
    and
  
        index = [ 2]
                [10]
        values = [4, 5]
        shape = [30]
  
    then the final deserialized `SparseTensor` will be:
  
        index = [0  0]
                [0 10]
                [0 20]
                [1  2]
                [1 10]
        values = [1, 2, 3, 4, 5]
        shape = [2 50]
  
    Args:
      serialized_sparse: The serialized `SparseTensor` objects.
        The last dimension must have 3 columns.
      dtype: The `dtype` of the serialized `SparseTensor` objects.
      rank: (optional) Python int, the rank of the `SparseTensor` objects.
      name: A name prefix for the returned tensors (optional).
  
    Returns:
      A `SparseTensor` representing the deserialized `SparseTensor` objects.
docstring_summary: Deserialize `SparseTensor` objects.
docstring_tokens: Deserialize SparseTensor objects .
function: def deserialize_sparse(serialized_sparse, dtype, rank=None, name=None):
    """Deserialize `SparseTensor` objects.
  
    The input `serialized_sparse` must have the shape `[?, ?, ..., ?, 3]` where
    the last dimension stores serialized `SparseTensor` objects and the other N
    dimensions (N >= 0) correspond to a batch. The ranks of the original
    `SparseTensor` objects must all match. When the final `SparseTensor` is
    created, its rank is the rank of the incoming `SparseTensor` objects plus N;
    the sparse tensors have been concatenated along new dimensions, one for each
    batch.
  
    The output `SparseTensor` object's shape values for the original dimensions
    are the max across the input `SparseTensor` objects' shape values for the
    corresponding dimensions. The new dimensions match the size of the batch.
  
    The input `SparseTensor` objects' indices are assumed ordered in
    standard lexicographic order.  If this is not the case, after this
    step run `SparseReorder` to restore index ordering.
  
    For example, if the serialized input is a `[2 x 3]` matrix representing two
    original `SparseTensor` objects:
  
        index = [ 0]
                [10]
                [20]
        values = [1, 2, 3]
        shape = [50]
  
    and
  
        index = [ 2]
                [10]
        values = [4, 5]
        shape = [30]
  
    then the final deserialized `SparseTensor` will be:
  
        index = [0  0]
                [0 10]
                [0 20]
                [1  2]
                [1 10]
        values = [1, 2, 3, 4, 5]
        shape = [2 50]
  
    Args:
      serialized_sparse: The serialized `SparseTensor` objects.
        The last dimension must have 3 columns.
      dtype: The `dtype` of the serialized `SparseTensor` objects.
      rank: (optional) Python int, the rank of the `SparseTensor` objects.
      name: A name prefix for the returned tensors (optional).
  
    Returns:
      A `SparseTensor` representing the deserialized `SparseTensor` objects.
  
    """
    output_indices, output_values, output_shape = (
        gen_sparse_ops.deserialize_sparse(serialized_sparse, dtype, name=name))
  
    # Feed rank data back in, if available
    output_indices.set_shape([None, rank])
    output_shape.set_shape([rank])
  
    return sparse_tensor.SparseTensor(output_indices, output_values, output_shape)
function_tokens: def deserialize_sparse ( serialized_sparse , dtype , rank = None , name = None ) : output_indices , output_values , output_shape = ( gen_sparse_ops . deserialize_sparse ( serialized_sparse , dtype , name = name ) ) # Feed rank data back in, if available output_indices . set_shape ( [ None , rank ] ) output_shape . set_shape ( [ rank ] ) return sparse_tensor . SparseTensor ( output_indices , output_values , output_shape )