parameters: (input_sequence, shared_name=None)
return_statement: return transformed_input_seq, sparse_tensor_keys, tensor_op_list
docstring: Converts `SparseTensor` values into `Tensors` of IDs and meta data.
  
    Given a dict of keys -> `Tensor` or `SparseTensor` transforms the
    `SparseTensor` values into `Tensor` values of IDs by calling `_store_sparse`.
    The IDs are pointers into and underlying `SparseTensorsMap` that is being
    constructed. Additional meta data is returned in order to be able to
    reconstruct `SparseTensor` values after batching and segmenting the IDs
    `Tensor`.
  
    Args:
      input_sequence: dictionary with `Tensor` or `SparseTensor` values.
      shared_name: The shared name for the underlying `SparseTensorsMap`
        (optional, defaults to the name of the newly created op).
    Returns:
      A tuple `(sequence, sparse_tensor_keys, tensor_list)` where `sequence` is
      dictionary with the same keys as `input_sequence` but only `Tensor` values,
      `sparse_tensor_keys` is a list of the keys of the `SparseTensor` values that
      were converted, and `tensor_list` is a list of the same length with
      `Tensor` objects.
docstring_summary: Converts `SparseTensor` values into `Tensors` of IDs and meta data.
docstring_tokens: Converts SparseTensor values into Tensors of IDs and meta data .
function: def _deconstruct_sparse_tensor_seq(input_sequence, shared_name=None):
    """Converts `SparseTensor` values into `Tensors` of IDs and meta data.
  
    Given a dict of keys -> `Tensor` or `SparseTensor` transforms the
    `SparseTensor` values into `Tensor` values of IDs by calling `_store_sparse`.
    The IDs are pointers into and underlying `SparseTensorsMap` that is being
    constructed. Additional meta data is returned in order to be able to
    reconstruct `SparseTensor` values after batching and segmenting the IDs
    `Tensor`.
  
    Args:
      input_sequence: dictionary with `Tensor` or `SparseTensor` values.
      shared_name: The shared name for the underlying `SparseTensorsMap`
        (optional, defaults to the name of the newly created op).
    Returns:
      A tuple `(sequence, sparse_tensor_keys, tensor_list)` where `sequence` is
      dictionary with the same keys as `input_sequence` but only `Tensor` values,
      `sparse_tensor_keys` is a list of the keys of the `SparseTensor` values that
      were converted, and `tensor_list` is a list of the same length with
      `Tensor` objects.
    """
    sparse_tensor_keys = [
        k for k in sorted(input_sequence.keys())
        if (isinstance(input_sequence[k], sparse_tensor.SparseTensor) or
            isinstance(input_sequence[k], sparse_tensor.SparseTensorValue))]
    if not sparse_tensor_keys:
      return input_sequence, None, sparse_tensor_keys
    sparse_tensor_list = [input_sequence[k] for k in sparse_tensor_keys]
    tensor_list = [_store_sparse(sp_tensor, shared_name=shared_name)
                   for sp_tensor in sparse_tensor_list]
    transformed_input_seq = dict(input_sequence)
    tensor_op_list = []
    for i, k in enumerate(sparse_tensor_keys):
      transformed_input_seq[k] = tensor_list[i]
      tensor_op_list += [tensor_list[i].op]
    return transformed_input_seq, sparse_tensor_keys, tensor_op_list
function_tokens: def _deconstruct_sparse_tensor_seq ( input_sequence , shared_name = None ) : sparse_tensor_keys = [ k for k in sorted ( input_sequence . keys ( ) ) if ( isinstance ( input_sequence [ k ] , sparse_tensor . SparseTensor ) or isinstance ( input_sequence [ k ] , sparse_tensor . SparseTensorValue ) ) ] if not sparse_tensor_keys : return input_sequence , None , sparse_tensor_keys sparse_tensor_list = [ input_sequence [ k ] for k in sparse_tensor_keys ] tensor_list = [ _store_sparse ( sp_tensor , shared_name = shared_name ) for sp_tensor in sparse_tensor_list ] transformed_input_seq = dict ( input_sequence ) tensor_op_list = [ ] for i , k in enumerate ( sparse_tensor_keys ) : transformed_input_seq [ k ] = tensor_list [ i ] tensor_op_list += [ tensor_list [ i ] . op ] return transformed_input_seq , sparse_tensor_keys , tensor_op_list