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 1:2 2:3']), 3)
      (<tf.Tensor 'DecodeLibsvm/label:0' shape=(1,) dtype=int64>,
       <tf.Tensor 'DecodeLibsvm/feature_indices:0' shape=(2, 2) dtype=int64>,
       <tf.Tensor 'DecodeLibsvm/feature_values:0' shape=(2,) dtype=float32>,
       <tf.Tensor 'DecodeLibsvm/feature_shape:0' shape=(2,) dtype=int64>)
      """
  
output:     
      with tf.name_scope(name, "DecodeLibsvm", [input]) as scope:
          input = tf.convert_to_tensor(input, name="input")
          
          # Split the input strings into labels and features
          split_lines = tf.string_split(input, delimiter='\n')
          split_elements = tf.string_split(split_lines.values, delimiter=' ')
          
          # Extract labels
          labels = tf.string_to_number(split_elements.values[0::2], out_type=label_dtype, name="label")
          labels = tf.reshape(labels, tf.shape(input))
          
          # Extract feature indices and values
          feature_strings = split_elements.values[1::2]
          feature_split = tf.string_split(feature_strings, delimiter=':')
          feature_indices = tf.string_to_number(feature_split.values[0::2], out_type=tf.int64)
          feature_values = tf.string_to_number(feature_split.values[1::2], out_type=dtype)
          
          # Reshape feature indices and values
          feature_indices = tf.reshape(feature_indices, [-1, 1])
          feature_values = tf.reshape(feature_values, [-1])
          
          # Create feature shape
          feature_shape = tf.constant([tf.shape(input)[0], num_features], dtype=tf.int64)
          
          return labels, feature_indices, feature_values, feature_shape
  