input: 
  @tf_export('decode_libsvm')
  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].
  
      >>> decode_libsvm(["1 1:1 2:2", "2 1:1 2:2"], 2)
      (<tf.Tensor: shape=(2,), dtype=int64, numpy=array([1, 2], dtype=int64)>,
       <tf.Tensor: shape=(4, 2), dtype=int64, numpy=
       array([[0, 1],
              [0, 2],
              [1, 1],
              [1, 2]], dtype=int64)>,
       <tf.Tensor: shape=(4,), dtype=float32, numpy=array([1., 2., 1., 2.], dtype=float32)>,
       <tf.Tensor: shape=(2,), dtype=int64, numpy=array([2, 2], dtype=int64)>)
      """
  
output:     
      with ops.name_scope(name, 'decode_libsvm', [input]) as name:
          input = ops.convert_to_tensor(input, name='input')
          lines = tf.strings.split(input, sep='\n')
          labels = tf.strings.to_number(tf.strings.split(lines, sep=' ').values[0], out_type=label_dtype)
          features = tf.strings.split(lines, sep=' ').values[1:]
          feature_indices = tf.strings.to_number(tf.strings.split(features, sep=':').values[:, 0], out_type=_dtypes.int64)
          feature_values = tf.strings.to_number(tf.strings.split(features, sep=':').values[:, 1], out_type=dtype)
          feature_indices = tf.stack([tf.range(tf.shape(feature_indices)[0], dtype=_dtypes.int64), feature_indices - 1], axis=1)
          feature_shape = tf.stack([tf.shape(input, out_type=_dtypes.int64)[0], num_features])
          return labels, feature_indices, feature_values, feature_shape
  