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(tf.constant(['1 1:1.0 2:2.0', '2 1:1.0 2:2.0']), 3, dtype=tf.float32, label_dtype=tf.int64)
      (label: <tf.Tensor: shape=(2,), dtype=int64, numpy=array([1, 2], dtype=int64)>,
       feature_indices: <tf.Tensor: shape=(4, 2), dtype=int64>,
       feature_values: <tf.Tensor: shape=(4,), dtype=float32>,
       feature_shape: <tf.Tensor: shape=(2,), dtype=int64>)
      """
output:     with tf.name_scope(name or "decode_libsvm"):
          input_tensor = tf.convert_to_tensor(input)
          def parse_libsvm_line(line):
              parts = tf.strings.split([line], ' ')
              label = tf.strings.to_number(parts[0], out_type=label_dtype)
              features = parts[1:]
              indices = tf.strings.to_number(tf.strings.split(features, ':')[0], out_type=tf.int64)
              values = tf.strings.to_number(tf.strings.split(features, ':')[1], out_type=dtype)
              return label, indices, values
  
          labels, indices, values = tf.map_fn(
              parse_libsvm_line,
              input_tensor,
              dtype=(label_dtype, tf.int64, dtype),
              back_prop=False
          )
  
          feature_indices = tf.RaggedTensor.from_row_splits(
              tf.reshape(indices, [-1]),
              tf.cumsum(tf.map_fn(lambda x: tf.size(x), indices, dtype=tf.int64))
          ).to_tensor()
  
          feature_values = tf.RaggedTensor.from_row_splits(
              tf.reshape(values, [-1]),
              tf.cumsum(tf.map_fn(lambda x: tf.size(x), values, dtype=tf.int64))
          ).to_tensor()
  
          feature_shape = tf.shape(feature_indices, out_type=tf.int64)
  
          return labels, feature_indices, feature_values, feature_shape