input: Write a function to decode LibSVM input to tensors using TensorFlow ops. The function should be named `decode_libsvm` and 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 returns 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", "0 1:3 2:4"], 2, dtype=tf.float32, label_dtype=tf.int32) == (label=tensor([1, 0]), feature_indices=tensor([[0, 1], [0, 2], [1, 1], [1, 2]]), feature_values=tensor([1., 2., 3., 4.]), feature_shape=tensor([2, 2]))
  assert decode_libsvm(["1.2 1:1 2:2", "0.5 1:3 2:4"], 2, dtype=tf.float64, label_dtype=tf.float32) == (label=tensor([1.2, 0.5]), feature_indices=tensor([[0, 1], [0, 2], [1, 1], [1, 2]]), feature_values=tensor([1., 2., 3., 4.]), feature_shape=tensor([2, 2]))
  assert decode_libsvm([], 2, dtype=tf.int32, label_dtype=tf.int64) == (label=tensor([]), feature_indices=tensor([], shape=(0, 2)), feature_values=tensor([]), feature_shape=tensor([0, 2]))
  assert decode_libsvm(["1 1:1 2:2", "2 1:3 2:4"], 2, dtype=tf.float32, label_dtype=tf.int32) == (label=tensor([1, 2]), feature_indices=tensor([[0, 1], [0, 2], [1, 1], [1, 2]]), feature_values=tensor([1., 2., 3., 4.]), feature_shape=tensor([2, 2]))
  assert decode_libsvm(["1.2 1:1 2:2", "2.5 1:3 2:4"], 2, dtype=tf.float64, label_dtype=tf.float64) == (label=tensor([1.2, 2.5]), feature_indices=tensor([[0, 1], [0, 2], [1, 1], [1, 2]]), feature_values=tensor([1., 2., 3., 4.]), feature_shape=tensor([2, 2]))
output: def decode_libsvm(input, num_features, dtype=tf.float32, label_dtype=tf.int64, name=None):
      def parse_libsvm(line):
          tokens = tf.strings.split([line], ' ')
          label = tf.strings.to_number(tokens[0], out_type=label_dtype)
          indices = []
          values = []
          for token in tokens[1:]:
              idx, val = tf.strings.split([token], ':')
              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_list, values_list = tf.map_fn(
          parse_libsvm,
          input,
          dtype=(label_dtype, tf.int64, dtype),
          name=name
      )
  
      labels = tf.stack(labels)
      indices = tf.concat([tf.expand_dims(tf.range(tf.shape(indices_list)[0], dtype=tf.int64), 1), tf.stack(indices_list, axis=1)], axis=1)
      values = tf.concat(values_list, axis=0)
      feature_shape = tf.constant([tf.shape(input)[0], num_features], dtype=tf.int64)
  
      return labels, indices, values, feature_shape