input: 
  def decode_libsvm(input, num_features, dtype=_dtypes.float32, label_dtype=_dtypes.int64, name=None):
      """Convert LibSVM input to tensors. The output consists of
  
      a label and a feature tensor. The shape of the label tensor
      is the same as input and the shape of the feature tensor is
      `[input_shape, num_features]`.
  
      Args:
          input: A `Tensor` of type `string`. Each string is a record in the LibSVM.
          num_features: An `int` that is `>= 1`. The number of features.
          dtype: An optional `tf.DType` from: `tf.float32, tf.float64, tf.int32, tf.int64`. Defaults to `tf.float32`.
          label_dtype: An optional `tf.DType` from: `tf.float32, tf.float64, tf.int32, tf.int64`. Defaults to `tf.int64`.
          name: A name for the operation (optional).
  
      Returns:
          A tuple of `Tensor` objects (label, feature_indices, feature_values, feature_shape).
  
          label: A `Tensor` of type `label_dtype`. A tensor of the same shape as input.
          feature_indices: A `Tensor` of type `int64`. A 2-D int64 tensor of dense_shape [N, ndims].
          feature_values: A `Tensor` of type `dtype`. A 1-D tensor of any type and dense_shape [N].
          feature_shape: A `Tensor` of type `int64`. A 1-D int64 tensor of dense_shape [ndims].
  
      Examples:
          >>> decode_libsvm(tf.constant(["1 1:1 2:2"]), 3)
          (<tf.Tensor: shape=(1,), dtype=int64, numpy=array([1], dtype=int64)>,
           <tf.Tensor: shape=(2,), dtype=int64, numpy=array([0, 1], dtype=int64)>,
           <tf.Tensor: shape=(2,), dtype=int64, numpy=array([1, 2], dtype=int64)>,
           <tf.Tensor: shape=(2,), dtype=int64, numpy=array([1, 2], dtype=int64)>)
      """
  
output:     
      with ops.name_scope(name, "decode_libsvm", [input]) as name:
          input = ops.convert_to_tensor(input, name="input")
          sparse_values, sparse_indices, sparse_shape, labels = (
              gen_parsing_ops.decode_libsvm(
                  input, num_features, dtype=dtype, label_dtype=label_dtype
              )
          )
          return labels, sparse_indices, sparse_values, sparse_shape
  