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].
      
      >>> decode_libsvm(tf.constant(["1 1:1 2:2"]), 2)
      (
          <tf.Tensor: shape=(1,), dtype=int64, numpy=array([1])>,
          <tf.Tensor: shape=(2, 2), dtype=int64, numpy=
          array([[0, 1],
                 [0, 2]])>,
          <tf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 2.], dtype=float32)>,
          <tf.Tensor: shape=(2,), dtype=int64, numpy=array([1, 2])>
      )
      """
  
output:     with tf.name_scope(name or "decode_libsvm"):
          def parse_libsvm(line):
              parts = tf.strings.split(line, ' ')
              label = tf.strings.to_number(parts[0], out_type=label_dtype)
              features = parts[1:]
              indices = tf.strings.to_number(tf.strings.split(features, ':')[0::2], out_type=tf.int64)
              values = tf.strings.to_number(tf.strings.split(features, ':')[1::2], out_type=dtype)
              return label, indices, values
  
          labels, indices, values = tf.map_fn(
              parse_libsvm,
              input,
              dtype=(label_dtype, tf.int64, dtype),
              name="map_parse_libsvm"
          )
  
          feature_indices = tf.stack([tf.range(tf.shape(indices)[0], dtype=tf.int64), indices], axis=2)
          feature_values = tf.reshape(values, [-1])
          feature_shape = tf.stack([tf.shape(input, out_type=tf.int64)[0], num_features])
  
          return labels, feature_indices, feature_values, feature_shape