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]`.
  
      >>> decode_libsvm(tf.constant(["1 2:3", "2 1:4"]), 2)
      (
          <tf.Tensor: shape=(2,), dtype=int64, numpy=array([1, 2])>,
          <tf.Tensor: shape=(2, 2), dtype=int64, numpy=array([[0, 1], [1, 0]])>,
          <tf.Tensor: shape=(2,), dtype=float32, numpy=array([3., 4.], dtype=float32)>,
          <tf.Tensor: shape=(2,), dtype=int64, numpy=array([2, 2])>
      )
      """
      pass
output:     
      with tf.name_scope(name or "decode_libsvm"):
          input_strings = tf.strings.split(input, " ")
          labels = tf.strings.to_number(input_strings.values[0::2], out_type=label_dtype)
          features = tf.strings.to_number(input_strings.values[1::2], out_type=tf.int64)
          
          indices = tf.strings.split(features, ":")
          feature_indices = tf.strings.to_number(indices.values[0::2], out_type=tf.int64)
          feature_values = tf.strings.to_number(indices.values[1::2], out_type=dtype)
          
          sparse_indices = tf.stack([tf.range(tf.shape(input)[0], dtype=tf.int64), feature_indices], axis=1)
          sparse_tensor = tf.SparseTensor(indices=sparse_indices, values=feature_values, dense_shape=[tf.shape(input)[0], num_features])
          
          dense_features = tf.sparse.to_dense(sparse_tensor, default_value=0)
          
          return labels, dense_features, feature_values, feature_indices
  