input: 
  def decode_libsvm(input: Tensor, num_features: int, dtype: tf.DType = tf.float32, label_dtype: tf.DType= tf.int64, name: str = None) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
      """ 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].
      
      Example:
      >>> decode_libsvm(tf.constant(["1 1:0.3", "2 1:-0.2"]), 2)
      (<tf.Tensor: shape=(2,), dtype=int64, numpy=array([1, 2], dtype=int64)>, <tf.Tensor: shape=(2, 2), dtype=int64, numpy= array([[0, 0], [1, 0]])>, <tf.Tensor: shape=(2,), dtype=float32, numpy=array([ 0.3, -0,2], dtype=float32)>, <tf.Tensor: shape=(2,), dtype=int64, numpy=array([2, 2], dtype=int64)>)
      """
  
output:     
      with tf.name_scope(name or "decode_libsvm"):
          def parse_line(line):
              parts = tf.strings.split(line, ' ')
              label = tf.strings.to_number(parts[0], out_type=label_dtype)
              indices = []
              values = []
              for part in parts[1:]:
                  index, value = tf.strings.split(part, ':')
                  indices.append([tf.strings.to_number(index, out_type=tf.int64)])
                  values.append(tf.strings.to_number(value, out_type=dtype))
              return label, indices, values
  
          labels, indices, values = tf.map_fn(
              parse_line,
              input,
              dtype=(label_dtype, tf.int64, dtype),
              back_prop=False
          )
  
          feature_indices = tf.ragged.stack(indices).to_tensor(default_value=0, shape=[None, num_features])
          feature_values = tf.ragged.stack(values).to_tensor(default_value=0)
          feature_shape = tf.shape(feature_indices, out_type=tf.int64)
  
          return labels, feature_indices, feature_values, feature_shape
  