input: from typing import List
  
  
  def decode_libsvm(input: List[str], num_features: int, dtype=_dtypes.float32, label_dtype=_dtypes.int64, name=None) -> tuple:
      """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.
          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(["1 3:0.5 7:0.7"], 10)
      ((<tf.Tensor: shape=(1,), dtype=int64, numpy=array([1])>, 
        <tf.Tensor: shape=(2, 2), dtype=int64, numpy=array([[3, 7]])>, 
        <tf.Tensor: shape=(2,), dtype=float32, numpy=array([0.5, 0.7])>, 
        <tf.Tensor: shape=(2,), dtype=int64, numpy=array([2])>))
      """
output:     import tensorflow as tf
  
      def parse_libsvm_line(line):
          parts = line.split()
          label = int(parts[0])
          features = {}
          for part in parts[1:]:
              index, value = part.split(':')
              features[int(index)] = float(value)
          return label, features
  
      labels = []
      feature_indices = []
      feature_values = []
      feature_shape = [len(input), num_features]
  
      for i, line in enumerate(input):
          label, features = parse_libsvm_line(line)
          labels.append(label)
          for index, value in features.items():
              feature_indices.append([i, index])
              feature_values.append(value)
  
      labels = tf.constant(labels, dtype=label_dtype)
      feature_indices = tf.constant(feature_indices, dtype=tf.int64)
      feature_values = tf.constant(feature_values, dtype=dtype)
      feature_shape = tf.constant(feature_shape, dtype=tf.int64)
  
      return labels, feature_indices, feature_values, feature_shape