input: Write a function to decode a LibSVM input into tensors using TensorFlow. 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(['1 1:1 2:2', '0 3:3 4:4'], 4, tf.float32, tf.int64).label.numpy() == [1, 0]
  assert decode_libsvm(['1 1:1 2:2', '0 3:3 4:4'], 4, tf.float32, tf.int64).feature_indices.numpy() == [[0, 1], [0, 2], [1, 3], [1, 4]]
  assert decode_libsvm(['1 1:1 2:2', '0 3:3 4:4'], 4, tf.float32, tf.int64).feature_values.numpy() == [1.0, 2.0, 3.0, 4.0]
  assert decode_libsvm(['1 1:1 2:2', '0 3:3 4:4'], 4, tf.float32, tf.int64).feature_shape.numpy() == [2, 4]
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_list, values_list = tf.map_fn(
          parse_line, input, dtype=(label_dtype, tf.int64, dtype),
          infer_shape=False, name='map_parse_line'
      )
  
      labels = tf.stack(labels)
      indices = tf.ragged.stack(indices_list).flat_values
      values = tf.ragged.stack(values_list).flat_values
      feature_indices = tf.stack([
          tf.repeat(tf.range(tf.shape(indices_list, out_type=tf.int64)[0]), tf.shape(indices_list, out_type=tf.int64)[1]),
          indices
      ], axis=1)
      feature_shape = tf.constant([tf.shape(input, out_type=tf.int64)[0], num_features], dtype=tf.int64)
  
      return labels, feature_indices, values, feature_shape