class_docstr: Simply converts the input records (1-D dense tensor) to a sparse tensor.
code: def decode_record(self, record: tf.Tensor) -> Dict[str, Any]:
      indices = tf.transpose(
          tf.stack(
              [
                  tf.range(tf.size(record), dtype=tf.int64),
                  tf.zeros(tf.size(record), dtype=tf.int64),
              ]
          )
      )
  
      return {
          "sparse_tensor": tf.SparseTensor(
              values=record,
              indices=indices,
              dense_shape=[tf.size(record), 1],
          )
      }
  
imports: from typing import Any, Dict
  import tensorflow
  from tfx_bsl.coders import tf_graph_record_decoder
label_desc: Use image node_id 1 for calling the SimpleDecoder obj's underlying member method code with example usage: obj.decode_record(record) and returns: dict