input: Write a function to decode a TensorFlow tensor of type string representing LibSVM input into tensors. The function 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 2:0.4 3:0.3', '2 1:0.1']), 3, tf.float32, tf.int32) == (tf.Tensor([1, 2]), tf.Tensor([[0, 1], [0, 2], [1, 0]]), tf.Tensor([0.4, 0.3, 0.1]), tf.Tensor([2, 3]))
  assert decode_libsvm(tf.constant(['1 2:0.4 3:0.3', '2 1:0.1']), 3, tf.float64, tf.int64) == (tf.Tensor([1, 2]), tf.Tensor([[0, 1], [0, 2], [1, 0]]), tf.Tensor([0.4, 0.3, 0.1]), tf.Tensor([2, 3]))
  assert decode_libsvm(tf.constant(['1 2:0.4 3:0.3', '2 1:0.1']), 3, tf.int32, tf.int32) == (tf.Tensor([1, 2]), tf.Tensor([[0, 1], [0, 2], [1, 0]]), tf.Tensor([0.4, 0.3, 0.1]), tf.Tensor([2, 3]))
  assert decode_libsvm(tf.constant(['1 2:0.4 3:0.3', '2 1:0.1']), 3, tf.int64, tf.int64) == (tf.Tensor([1, 2]), tf.Tensor([[0, 1], [0, 2], [1, 0]]), tf.Tensor([0.4, 0.3, 0.1]), tf.Tensor([2, 3]))
  assert decode_libsvm(tf.constant([]), 3, tf.float32, tf.int64) == (tf.Tensor([]), tf.Tensor([[], []]), tf.Tensor([]), tf.Tensor([0, 3]))
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))
              values.append(tf.strings.to_number(val, out_type=dtype))
          return label, indices, values
  
      labels, indices, values = tf.map_fn(parse_line, input, dtype=(label_dtype, tf.int64, dtype))
      labels = tf.stack(labels)
      indices = tf.ragged.stack(indices).flat_values
      values = tf.ragged.stack(values).flat_values
      feature_indices = tf.transpose(tf.stack([tf.range(tf.size(indices)) // num_features, indices]))
      feature_shape = tf.shape(input, out_type=tf.int64)
      feature_shape = tf.concat([feature_shape, [num_features]], axis=0)
      return labels, feature_indices, values, feature_shape