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([b'1 1:1 2:2', b'2 1:1 3:3'], 3, dtype=_dtypes.int32, label_dtype=_dtypes.int32)
      (<tf.Tensor: shape=(2,), dtype=int32, numpy=array([1, 2], dtype=int32)>, <tf.Tensor: shape=(4, 2), dtype=int64, numpy=
      array([[0, 1],
             [0, 2],
             [1, 1],
             [1, 3]])>, <tf.Tensor: shape=(4,), dtype=int32, numpy=array([1, 2, 1, 3], dtype=int32)>, <tf.Tensor: shape=(2,), dtype=int64, numpy=array([2, 3]))>)
      >>> decode_libsvm([b'1 1:0.5', b'2 2:0.3'], 2, dtype=_dtypes.float32, label_dtype=_dtypes.float32)
      (<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 2.], dtype=float32)>, <tf.Tensor: shape=(2, 2), dtype=int64, numpy=
      array([[0, 1],
             [1, 2]])>, <tf.Tensor: shape=(2,), dtype=float32, numpy=array([0.5, 0.3], dtype=float32)>, <tf.Tensor: shape=(2,), dtype=int64, numpy=array([1, 1]))>)
      """
  
output:     
      with ops.name_scope(name, "decode_libsvm", [input]) as name:
          def _parse_line(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_line,
              input,
              dtype=(label_dtype, tf.int64, dtype),
              back_prop=False
          )
  
          labels = tf.reshape(labels, [-1])
          indices = tf.reshape(indices, [-1, 2])
          values = tf.reshape(values, [-1])
          feature_shape = tf.constant([num_features], dtype=tf.int64)
  
          return labels, indices, values, feature_shape
  