input: 
  def decode_libsvm(input: Tensor, num_features: int, dtype: Optional[tf.DType] = tf.float32, label_dtype: Optional[tf.DType] = tf.int64, name: Optional[str] = None) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
      """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].
      
      Example:
      >>> decode_libsvm(["1 2:1.0", "2 3:1.0"], 3)
      (Tensor<int64>, Tensor<int64>, Tensor<float32>, Tensor<int64>)
      """
  
output:     
      with tf.name_scope(name, "decode_libsvm", [input]) as scope:
          input = tf.convert_to_tensor(input, name="input")
          
          # Split the input strings into labels and features
          split_input = tf.string_split(input, delimiter=" ")
          labels = tf.string_to_number(split_input.indices[:, 0], out_type=label_dtype)
          
          # Split the features into indices and values
          features = tf.string_split(split_input.values[1:], delimiter=":")
          feature_indices = tf.string_to_number(features.indices[:, 0], out_type=tf.int64)
          feature_values = tf.string_to_number(features.values[:, 1], out_type=dtype)
          
          # Create the feature shape tensor
          feature_shape = tf.constant([tf.shape(input)[0], num_features], dtype=tf.int64)
          
          return labels, feature_indices, feature_values, feature_shape
  