input: 
  def decode_libsvm(input: tf.Tensor, num_features: int, dtype: tf.DType = tf.float32, label_dtype: tf.DType = tf.int64, name: str = None) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.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].
      
      >>> decode_libsvm(tf.constant(["1 2:2.0", "2 3:3.0"], dtype=tf.string), 3)
      (<tf.Tensor: shape=(2,), dtype=int64, numpy=array([1, 2], dtype=int64)>, <tf.Tensor: shape=(2, 2), dtype=int64, numpy=array([[0, 1], [1, 2]], dtype=int64)>, <tf.Tensor: shape=(2,), dtype=float32, numpy=array([2., 3.], dtype=float32)>, <tf.Tensor: shape=(2,), dtype=int64, numpy=array([2, 2], dtype=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.strings.split(input, sep=" ")
          labels = tf.strings.to_number(split_input.values[0::2], out_type=label_dtype)
          
          # Split the feature part into indices and values
          features = split_input.values[1::2]
          feature_split = tf.strings.split(features, sep=":")
          feature_indices = tf.strings.to_number(feature_split.values[0::2], out_type=tf.int64)
          feature_values = tf.strings.to_number(feature_split.values[1::2], out_type=dtype)
          
          # Reshape feature indices to 2D tensor
          feature_indices = tf.reshape(feature_indices, [-1, 1])
          row_indices = tf.range(tf.shape(input)[0], dtype=tf.int64)
          row_indices = tf.reshape(row_indices, [-1, 1])
          row_indices = tf.tile(row_indices, [1, tf.shape(feature_indices)[0] // tf.shape(input)[0]])
          row_indices = tf.reshape(row_indices, [-1, 1])
          feature_indices = tf.concat([row_indices, feature_indices], axis=1)
          
          # Create feature shape tensor
          feature_shape = tf.stack([tf.shape(input)[0], num_features])
          
          return labels, feature_indices, feature_values, feature_shape
  