input: 
  from typing import List, Tuple
  import tensorflow as tf
  
  def decode_libsvm(input: tf.Tensor, num_features: int, dtype: tf.DType = tf.float32, label_dtype: tf.DType = tf.int64, name: str = None) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor]:
      """Convert LibSVM input to tensors. The output consists of
      a label and a feature tensor. The shape of the label tensor
      is the same as input and the shape of the feature tensor is
      `[input_shape, num_features]`.
  
      Args:
          input: A `Tensor` of type `string`. Each string is a record in the LibSVM format.
          num_features: An `int` that is `>= 1`. The number of features.
          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`.
          name: A name for the operation (optional).
  
      Returns:
          A tuple of `Tensor` objects (label, feature_indices, feature_values, feature_shape).
  
          label: A `Tensor` of type `label_dtype`. A tensor of the same shape as input.
          feature_indices: A `Tensor` of type `int64`. A 2-D int64 tensor of dense_shape [N, ndims].
          feature_values: A `Tensor` of type `dtype`. A 1-D tensor of any type and dense_shape [N].
          feature_shape: A `Tensor` of type `int64`. A 1-D int64 tensor of dense_shape [ndims].
  
      >>> decode_libsvm(tf.constant(["1 1:0.5 2:1.5"]), 3, tf.float32, tf.int64)
      (<tf.Tensor: shape=(1,), dtype=int64, numpy=array([1], dtype=int64)>,
       <tf.Tensor: shape=(2, 2), dtype=int64, numpy=
       array([[0, 1],
              [0, 2]], dtype=int64)>,
       <tf.Tensor: shape=(2,), dtype=float32, numpy=array([0.5, 1.5], dtype=float32)>,
       <tf.Tensor: shape=(2,), dtype=int64, numpy=array([1, 3], dtype=int64)>)
      """
      # Implementation code would go here
  
output:     
      with tf.name_scope(name, "decode_libsvm", [input]) as scope:
          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([0, 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),
              name="map_parse_line"
          )
  
          feature_indices = tf.reshape(tf.concat(indices_list, axis=0), [-1, 2])
          feature_values = tf.reshape(tf.concat(values_list, axis=0), [-1])
          feature_shape = tf.constant([tf.shape(input)[0], num_features], dtype=tf.int64)
  
          return labels, feature_indices, feature_values, feature_shape
  