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(tf.constant(["1 1:1 2:2"]), 2, dtype=tf.float32, label_dtype=tf.int64)==(label, feature_indices, feature_values, feature_shape), where `label`, `feature_indices`, `feature_values`, and `feature_shape` are appropriately shaped tensors.
  assert decode_libsvm(tf.constant([""]), 1, dtype=tf.int32, label_dtype=tf.float32)==(label, feature_indices, feature_values, feature_shape), where `label`, `feature_indices`, `feature_values`, and `feature_shape` are appropriately shaped tensors.
  assert decode_libsvm(tf.constant(["2 1:1.5 2:2.5"]), 3, dtype=tf.float64, label_dtype=tf.float64)==(label, feature_indices, feature_values, feature_shape), where `label`, `feature_indices`, `feature_values`, and `feature_shape` are appropriately shaped tensors.
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),
          infer_shape=False, name=name
      )
  
      feature_indices = tf.ragged.stack(indices).flat_values
      feature_values = tf.ragged.stack(values).flat_values
      feature_shape = tf.shape(feature_indices, out_type=tf.int64)
  
      return labels, feature_indices, feature_values, feature_shape