code: def decode_libsvm(
      content, num_features, dtype=None, label_dtype=None
  ):
      """Convert Libsvm records to a tensor of label and a tensor of feature.
  
      Args:
        content: A `Tensor` of type `string`. Each string is a record/row in
          the Libsvm format.
        num_features: The number of features.
        dtype: The type of the output feature tensor. Default to tf.float32.
        label_dtype: The type of the output label tensor. Default to tf.int64.
  
      Returns:
        features: A `SparseTensor` of the shape `[input_shape, num_features]`.
        labels: A `Tensor` of the same shape as content.
      """
      labels, indices, values, shape = core_ops.io_decode_libsvm(
          content, num_features, dtype=dtype, label_dtype=label_dtype
      )
      return sparse.SparseTensor(indices, values, shape), labels
  
file_path: io/tensorflow_io/python/experimental/text_ops.py
imports: from tensorflow import sparse
  from tensorflow_io.python.ops import core_ops
label_desc: Use image node_id 1 for calling a global function with example usage: decode_libsvm(content, num_features, dtype, label_dtype) and returns: sparse,  labels