input: Write a function to decode LibSVM input to tensors using TensorFlow ops. The function should be named `decode_libsvm` and should accept the following arguments: `input` (a `Tensor` of type `string`), `num_features` (an `int` that is `>= 1`), `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`), and `name` (a name for the operation, optional). The function should return a tuple of `Tensor` objects `(label, feature_indices, feature_values, feature_shape)`, where `label` is a `Tensor` of type `label_dtype`, `feature_indices` is a `Tensor` of type `int64`, `feature_values` is a `Tensor` of type `dtype`, and `feature_shape` is a `Tensor` of type `int64`.
  
  assert decode_libsvm(tf.constant(["1 2:3.5 3:4.2"]), 3, dtype=tf.float32, label_dtype=tf.int32, name="decode_libsvm")==(tf.constant([1], dtype=tf.int32), tf.constant([[0, 1], [0, 2]], dtype=tf.int64), tf.constant([3.5, 4.2], dtype=tf.float32), tf.constant([1, 3], dtype=tf.int64))
  assert decode_libsvm(tf.constant(["2 1:1.1 2:2.2"]), 3, dtype=tf.float64, label_dtype=tf.int64, name="decode_libsvm")==(tf.constant([2], dtype=tf.int64), tf.constant([[0, 0], [0, 1]], dtype=tf.int64), tf.constant([1.1, 2.2], dtype=tf.float64), tf.constant([1, 3], dtype=tf.int64))
  assert decode_libsvm(tf.constant(["3 4:1 5:2"]), 6, dtype=tf.int32, label_dtype=tf.int64, name="decode_libsvm")==(tf.constant([3], dtype=tf.int64), tf.constant([[0, 1], [0, 2]], dtype=tf.int64), tf.constant([1, 2], dtype=tf.int32), tf.constant([1, 6], dtype=tf.int64))
  assert decode_libsvm(tf.constant(["4 1:1.5"]), 1, dtype=tf.float32, label_dtype=tf.int32, name="decode_libsvm")==(tf.constant([4], dtype=tf.int32), tf.constant([[0, 0]], dtype=tf.int64), tf.constant([1.5], dtype=tf.float32), tf.constant([1, 1], dtype=tf.int64))
  assert decode_libsvm(tf.constant(["5"]), 5, dtype=tf.float64, label_dtype=tf.int64, name="decode_libsvm")==(tf.constant([5], dtype=tf.int64), tf.constant([], dtype=tf.int64), tf.constant([], dtype=tf.float64), tf.constant([1, 5], dtype=tf.int64))
output: def decode_libsvm(input, num_features, dtype=tf.float32, label_dtype=tf.int64, name=None):
      def parse_line(line):
          parts = tf.strings.split(line, ' ')
          label = tf.strings.to_number(parts[0], out_type=label_dtype)
          indices = []
          values = []
          for part in parts[1:]:
              idx, val = tf.strings.split(part, ':')
              indices.append(tf.strings.to_number(idx, out_type=tf.int64) - 1)
              values.append(tf.strings.to_number(val, out_type=dtype))
          return label, indices, values
  
      labels, indices, values = tf.map_fn(
          parse_line, input,
          fn_output_signature=(label_dtype, tf.int64, dtype)
      )
  
      labels = tf.stack(labels)
      indices = tf.ragged.stack(indices).flat_values
      values = tf.ragged.stack(values).flat_values
      feature_shape = tf.constant([tf.shape(input)[0], num_features], dtype=tf.int64)
  
      return labels, indices, values, feature_shape