input: Write a function to decode LibSVM input to 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(tf.constant(["1 2:1.0 4:2.0"]), 5, dtype=tf.float32, label_dtype=tf.int32).label.numpy() == [1]
  assert decode_libsvm(tf.constant(["2 3:2.0 5:3.0"]), 5, dtype=tf.float64, label_dtype=tf.int64).label.numpy() == [2]
  assert decode_libsvm(tf.constant(["3 1:3.0 2:4.0"]), 5, dtype=tf.int32, label_dtype=tf.int32).label.numpy() == [3]
  assert decode_libsvm(tf.constant(["4 5:4.0 6:5.0"]), 7, dtype=tf.int64, label_dtype=tf.int64).label.numpy() == [4]
  assert decode_libsvm(tf.constant([]), 3, dtype=tf.float32, label_dtype=tf.int64).label.numpy() == []
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,
          fn_output_signature=(label_dtype, tf.int64, dtype)
      )
  
      labels = tf.stack(labels)
      indices = tf.reshape(tf.stack(indices), [-1])
      values = tf.reshape(tf.stack(values), [-1])
      feature_shape = tf.constant([tf.shape(input)[0], num_features], dtype=tf.int64)
  
      return labels, indices, values, feature_shape