parameters: (serialized_sparse, dtype, rank=None, name=None)
return_statement: return sparse_tensor.SparseTensor(output_indices, output_values, output_shape)
docstring: Deserialize and concatenate `SparseTensors` from a serialized minibatch.
  
    The input `serialized_sparse` must be a string matrix of shape `[N x 3]` where
    `N` is the minibatch size and the rows correspond to packed outputs of
    `serialize_sparse`.  The ranks of the original `SparseTensor` objects
    must all match.  When the final `SparseTensor` is created, it has rank one
    higher than the ranks of the incoming `SparseTensor` objects (they have been
    concatenated along a new row dimension).
  
    The output `SparseTensor` object's shape values for all dimensions but the
    first are the max across the input `SparseTensor` objects' shape values
    for the corresponding dimensions.  Its first shape value is `N`, the minibatch
    size.
  
    The input `SparseTensor` objects' indices are assumed ordered in
    standard lexicographic order.  If this is not the case, after this
    step run `sparse.reorder` to restore index ordering.
  
    For example, if the serialized input is a `[2, 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: 2-D `Tensor` of type `string` of shape `[N, 3]`.
        The serialized and packed `SparseTensor` objects.
      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`s,
      concatenated along the `SparseTensor`s' first dimension.
  
      All of the serialized `SparseTensor`s must have had the same rank and type.
docstring_summary: Deserialize and concatenate `SparseTensors` from a serialized minibatch.
docstring_tokens: Deserialize and concatenate SparseTensors from a serialized minibatch .
function: def deserialize_many_sparse(serialized_sparse, dtype, rank=None, name=None):
    """Deserialize and concatenate `SparseTensors` from a serialized minibatch.
  
    The input `serialized_sparse` must be a string matrix of shape `[N x 3]` where
    `N` is the minibatch size and the rows correspond to packed outputs of
    `serialize_sparse`.  The ranks of the original `SparseTensor` objects
    must all match.  When the final `SparseTensor` is created, it has rank one
    higher than the ranks of the incoming `SparseTensor` objects (they have been
    concatenated along a new row dimension).
  
    The output `SparseTensor` object's shape values for all dimensions but the
    first are the max across the input `SparseTensor` objects' shape values
    for the corresponding dimensions.  Its first shape value is `N`, the minibatch
    size.
  
    The input `SparseTensor` objects' indices are assumed ordered in
    standard lexicographic order.  If this is not the case, after this
    step run `sparse.reorder` to restore index ordering.
  
    For example, if the serialized input is a `[2, 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: 2-D `Tensor` of type `string` of shape `[N, 3]`.
        The serialized and packed `SparseTensor` objects.
      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`s,
      concatenated along the `SparseTensor`s' first dimension.
  
      All of the serialized `SparseTensor`s must have had the same rank and type.
    """
    output_indices, output_values, output_shape = (
        gen_sparse_ops.deserialize_many_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_many_sparse ( serialized_sparse , dtype , rank = None , name = None ) : output_indices , output_values , output_shape = ( gen_sparse_ops . deserialize_many_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 )