input: Write a function to decode LibSVM input to tensors using TensorFlow ops. The function should be named `decode_libsvm` and 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)[0].numpy() == 1
  assert decode_libsvm(["1 1:1", "2 2:2"], 2)[1].numpy() == [0, 1, 1, 1]
  assert decode_libsvm(["1 1:1", "2 2:2"], 2)[2].numpy() == [1, 1]
  assert decode_libsvm(["1 1:1", "2 2:2"], 2)[3].numpy() == [2, 2]
  assert decode_libsvm([], 2)[0].numpy() == []
  assert decode_libsvm([], 2)[1].numpy() == []
  assert decode_libsvm([], 2)[2].numpy() == []
  assert decode_libsvm([], 2)[3].numpy() == []
output: 
  def decode_libsvm(input, num_features, dtype=tf.float32, label_dtype=tf.int64, name=None):
      with tf.name_scope(name or "decode_libsvm"):
          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),
              infer_shape=False
          )
  
          labels = tf.stack(labels)
          indices = tf.reshape(tf.concat(indices, axis=0), [-1])
          values = tf.reshape(tf.concat(values, axis=0), [-1])
          feature_shape = tf.shape(input, out_type=tf.int64) * tf.constant([1, num_features], dtype=tf.int64)
  
          return labels, indices, values, feature_shape
  