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 3:1', b'2 2:1 4:1'], 5)
      (<tf.Tensor: shape=(2,), dtype=int64, numpy=array([1, 2])>,
       <tf.Tensor: shape=(4, 2), dtype=int64, numpy=
       array([[0, 1],
              [0, 3],
              [1, 2],
              [1, 4]])>,
       <tf.Tensor: shape=(4,), dtype=float32, numpy=array([1., 1., 1., 1.], dtype=float32)>,
       <tf.Tensor: shape=(2,), dtype=int64, numpy=array([2, 5])>)
      """
output:     with ops.name_scope(name, "decode_libsvm", [input]) as name:
          input = ops.convert_to_tensor(input, dtype=dtypes.string)
          num_features = ops.convert_to_tensor(num_features, dtype=dtypes.int32)
  
          def _parse_single_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=dtypes.int64) - 1)
                  values.append(tf.strings.to_number(value, out_type=dtype))
              return label, indices, values
  
          labels, indices, values = tf.map_fn(
              _parse_single_line, input,
              dtype=(label_dtype, dtypes.int64, dtype),
              back_prop=False
          )
  
          indices = tf.ragged.stack(indices).values
          values = tf.ragged.stack(values).values
          feature_shape = tf.shape(input, out_type=dtypes.int64)
          feature_shape = tf.concat([feature_shape, [num_features]], axis=0)
  
          return labels, indices, values, feature_shape