input: 
  from typing import 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]`.
  
      >>> decode_libsvm(tf.constant(["1 1:1 2:2"]), 2)
      (label: Tensor("DecodeLibsvm/label:0", shape=(1,), dtype=int64),
       feature_indices: Tensor("DecodeLibsvm/feature_indices:0", shape=(2, 2), dtype=int64),
       feature_values: Tensor("DecodeLibsvm/feature_values:0", shape=(2,), dtype=float32),
       feature_shape: Tensor("DecodeLibsvm/feature_shape:0", shape=(2,), dtype=int64))
  
      >>> decode_libsvm(tf.constant(["1 1:1 2:2"]), 2, dtype=tf.float64, label_dtype=tf.int32)
      (label: Tensor("DecodeLibsvm_1/label:0", shape=(1,), dtype=int32),
       feature_indices: Tensor("DecodeLibsvm_1/feature_indices:0", shape=(2, 2), dtype=int64),
       feature_values: Tensor("DecodeLibsvm_1/feature_values:0", shape=(2,), dtype=float64),
       feature_shape: Tensor("DecodeLibsvm_1/feature_shape:0", shape=(2,), dtype=int64))
      """
      pass
  
output:     
      with tf.name_scope(name, "DecodeLibsvm", [input]) as scope:
          input = tf.convert_to_tensor(input, dtype=tf.string, name="input")
          
          # Split the input by spaces
          tokens = tf.string_split(input, delimiter=" ")
          
          # Extract the label
          label = tf.string_to_number(tokens.values[0], out_type=label_dtype, name="label")
          label = tf.reshape(label, [-1], name="label_reshape")
          
          # Extract the feature indices and values
          feature_tokens = tokens.values[1:]
          feature_parts = tf.string_split(feature_tokens, delimiter=":")
          feature_indices = tf.string_to_number(feature_parts.values[::2], out_type=tf.int64, name="feature_indices")
          feature_values = tf.string_to_number(feature_parts.values[1::2], out_type=dtype, name="feature_values")
          
          # Reshape feature indices to [num_features, 2]
          feature_indices = tf.reshape(feature_indices, [-1, 2], name="feature_indices_reshape")
          
          # Create the feature shape tensor
          feature_shape = tf.constant([tf.shape(input)[0], num_features], dtype=tf.int64, name="feature_shape")
          
          return label, feature_indices, feature_values, feature_shape
  