input: def decode_libsvm(input, num_features, dtype=_dtypes.float32, label_dtype=_dtypes.int64, name=None):
      """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(tf.constant(['1:-1 2:2.5', '1:0 2:1']), 3)
      (<tf.Tensor: shape=(2,), dtype=int64, numpy=array([1, 1], dtype=int64)>,
       <tf.Tensor: shape=(4, 2), dtype=int64, numpy=
       array([[0, 1],
              [0, 2],
              [1, 1],
              [1, 2]], dtype=int64)>,
       <tf.Tensor: shape=(4,), dtype=float32, numpy=array([-1. ,  2.5,  0. ,  1. ], dtype=float32)>,
       <tf.Tensor: shape=(2,), dtype=int64, numpy=array([2, 2], dtype=int64)>)
  
      >>> decode_libsvm(tf.constant(['1:-1 2:2.5', '1:0 2:1']), 3, dtype=tf.int32, label_dtype=tf.float32)
      (<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 1.], dtype=float32)>,
       <tf.Tensor: shape=(4, 2), dtype=int64, numpy=
       array([[0, 1],
              [0, 2],
              [1, 1],
              [1, 2]], dtype=int64)>,
       <tf.Tensor: shape=(4,), dtype=int32, numpy=array([-1,  2,  0,  1], dtype=uint16)>,
       <tf.Tensor: shape=(2,), dtype=int64, numpy=array([2, 2], dtype=int64)>)
      """
output:     
      with ops.name_scope(name, "decode_libsvm", [input]) as name:
          input = ops.convert_to_tensor(input, name="input")
          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:]:
                  index, value = tf.strings.split([part], ':')
                  indices.append(tf.strings.to_number(index, out_type=tf.int64))
                  values.append(tf.strings.to_number(value, out_type=dtype))
              return label, indices, values
          
          labels, indices, values = tf.map_fn(
              _parse_line,
              input,
              dtype=(label_dtype, tf.int64, dtype),
              back_prop=False
          )
          
          indices_tensor = tf.stack(indices, axis=1)
          values_tensor = tf.stack(values, axis=1)
          feature_indices = tf.reshape(indices_tensor, [-1, 2])
          feature_values = tf.reshape(values_tensor, [-1])
          feature_shape = tf.shape(input, out_type=tf.int64)
          
          return labels, feature_indices, feature_values, feature_shape
  