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 format.
          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(['1 1:1 2:2', '2 1:3 2:4'], 2).label.numpy()
      array([1, 2])
      >>> decode_libsvm(['1 1:1 2:2', '2 1:3 2:4'], 2).feature_values.numpy()
      array([1., 2., 3., 4.])
      >>> decode_libsvm(['1 1:1 2:2', '2 1:3 2:4'], 2, dtype=tf.int32).feature_values.numpy()
      array([1, 2, 3, 4])
      """
  
output:     with ops.name_scope(name, "decode_libsvm", [input]) as name:
          input = ops.convert_to_tensor(input, name="input")
          num_features = ops.convert_to_tensor(num_features, dtype=_dtypes.int32, name="num_features")
  
          def _parse_libsvm(line):
              tokens = tf.strings.split([line], ' ')
              label = tf.strings.to_number(tokens[0], out_type=label_dtype)
              indices = tf.strings.to_number(tf.strings.split(tokens[1:], ':')[::2], out_type=_dtypes.int64)
              values = tf.strings.to_number(tf.strings.split(tokens[1:], ':')[1::2], out_type=dtype)
              return label, indices, values
  
          labels, indices, values = tf.map_fn(
              _parse_libsvm,
              input,
              dtype=(label_dtype, _dtypes.int64, dtype),
              back_prop=False
          )
  
          labels = tf.reshape(labels, [-1])
          indices = tf.reshape(indices, [-1, 2])
          values = tf.reshape(values, [-1])
          feature_shape = tf.stack([tf.shape(input)[0], num_features])
  
          return labels, indices, values, feature_shape