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(['1 1:1 2:2', '0 1:2 3:3'], 3)
      (Tensor('label:0', shape=(2,), dtype=int64), Tensor('feature_indices:0', shape=(4, 2), dtype=int64), Tensor('feature_values:0', shape=(4,), dtype=float32), Tensor('feature_shape:0', shape=(2,), dtype=int64))
      """
  
output:     with ops.name_scope(name, "decode_libsvm", [input]) as name:
          input = ops.convert_to_tensor(input, name="input")
          splits = array_ops.string_split(input, delimiter=" ")
          labels = parsing_ops.string_to_number(splits.values[0::2], out_type=label_dtype)
          labels = array_ops.reshape(labels, array_ops.shape(input))
          
          features = splits.values[1::2]
          feature_splits = array_ops.string_split(features, delimiter=":")
          feature_indices = parsing_ops.string_to_number(feature_splits.values[0::2], out_type=_dtypes.int64)
          feature_values = parsing_ops.string_to_number(feature_splits.values[1::2], out_type=dtype)
          
          feature_indices = array_ops.reshape(feature_indices, [-1, 1])
          feature_values = array_ops.reshape(feature_values, [-1])
          feature_shape = constant_op.constant([num_features], dtype=_dtypes.int64)
          
          return labels, feature_indices, feature_values, feature_shape