input: Write a function to decode LibSVM input to tensors using TensorFlow. The function should take 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(['1 1:1', '2 2:2'], 2) == [(1, [[0, 1]]), (2, [[1, 2]])]
  assert decode_libsvm(['1 1:1', '2 2:2'], 2, dtype=tf.float64) == [(1, [[0, 1.0]]), (2, [[1, 2.0]])]
  assert decode_libsvm(['1 1:1', '2 2:2'], 2, label_dtype=tf.int32) == [(1, [[0, 1]]), (2, [[1, 2]])]
  assert decode_libsvm(['1 1:1', '2 2:2'], 2, dtype=tf.int32, label_dtype=tf.int32) == [(1, [[0, 1]]), (2, [[1, 2]])]
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)
          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_line, input, dtype=(label_dtype, tf.int64, dtype))
      feature_shape = tf.constant([num_features], dtype=tf.int64)
      return labels, indices, values, feature_shape