parameters: (content, num_features, dtype=None, label_dtype=None)
return_statement: return sparse.SparseTensor(indices, values, shape), labels
docstring: Convert Libsvm records to a tensor of label and a tensor of feature.
  
      Args:
        content: A `Tensor` of type `string`. Each string is a record/row in
          the Libsvm format.
        num_features: The number of features.
        dtype: The type of the output feature tensor. Default to tf.float32.
        label_dtype: The type of the output label tensor. Default to tf.int64.
  
      Returns:
        features: A `SparseTensor` of the shape `[input_shape, num_features]`.
        labels: A `Tensor` of the same shape as content.
docstring_summary: Convert Libsvm records to a tensor of label and a tensor of feature.
docstring_tokens: Convert Libsvm records to a tensor of label and a tensor of feature .
function: def decode_libsvm(content, num_features, dtype=None, label_dtype=None):
      """Convert Libsvm records to a tensor of label and a tensor of feature.
  
      Args:
        content: A `Tensor` of type `string`. Each string is a record/row in
          the Libsvm format.
        num_features: The number of features.
        dtype: The type of the output feature tensor. Default to tf.float32.
        label_dtype: The type of the output label tensor. Default to tf.int64.
  
      Returns:
        features: A `SparseTensor` of the shape `[input_shape, num_features]`.
        labels: A `Tensor` of the same shape as content.
      """
      labels, indices, values, shape = core_ops.io_decode_libsvm(
          content, num_features, dtype=dtype, label_dtype=label_dtype
      )
      return sparse.SparseTensor(indices, values, shape), labels
function_tokens: def decode_libsvm ( content , num_features , dtype = None , label_dtype = None ) : labels , indices , values , shape = core_ops . io_decode_libsvm ( content , num_features , dtype = dtype , label_dtype = label_dtype ) return sparse . SparseTensor ( indices , values , shape ) , labels