code: def serialize_tensor(tensor, name=None):
      r"""Transforms a Tensor into a serialized TensorProto proto.
  
      This operation transforms data in a `tf.Tensor` into a `tf.Tensor` of type
      `tf.string` containing the data in a binary string in little-endian format.
      This operation can transform scalar data and linear arrays, but it is most
      useful in converting multidimensional arrays into a format accepted by binary
      storage formats such as a `TFRecord` or `tf.train.Example`.
  
      See also:
      - `tf.io.parse_tensor`: inverse operation of `tf.io.serialize_tensor` that
      transforms a scalar string containing a serialized Tensor in little-endian
      format into a Tensor of a specified type.
      - `tf.ensure_shape`: `parse_tensor` cannot statically determine the shape of
      the parsed tensor. Use `tf.ensure_shape` to set the static shape when running
      under a `tf.function`
      - `.SerializeToString`, serializes a proto to a binary-string
  
      Example of serializing scalar data:
  
      >>> t = tf.constant(1)
      >>> tf.io.serialize_tensor(t)
      <tf.Tensor: shape=(), dtype=string, numpy=b'\x08...\x00'>
  
      Example of storing non-scalar data into a `tf.train.Example`:
  
      >>> t1 = [[1, 2]]
      >>> t2 = [[7, 8]]
      >>> nonscalar = tf.concat([t1, t2], 0)
      >>> nonscalar
      <tf.Tensor: shape=(2, 2), dtype=int32, numpy=
      array([[1, 2],
             [7, 8]], dtype=int32)>
  
      Serialize the data using `tf.io.serialize_tensor`.
  
      >>> serialized_nonscalar = tf.io.serialize_tensor(nonscalar)
      >>> serialized_nonscalar
      <tf.Tensor: shape=(), dtype=string, numpy=b'\x08...\x00'>
  
      Store the data in a `tf.train.Feature`.
  
      >>> feature_of_bytes = tf.train.Feature(
      ...   bytes_list=tf.train.BytesList(value=[serialized_nonscalar.numpy()]))
      >>> feature_of_bytes
      bytes_list {
        value: "\010...\000"
      }
  
      Put the `tf.train.Feature` message into a `tf.train.Example`.
  
      >>> features_for_example = {
      ...   'feature0': feature_of_bytes
      ... }
      >>> example_proto = tf.train.Example(
      ...   features=tf.train.Features(feature=features_for_example))
      >>> example_proto
      features {
        feature {
          key: "feature0"
          value {
            bytes_list {
              value: "\010...\000"
            }
          }
        }
      }
  
      Args:
        tensor: A `tf.Tensor`.
        name: string.  Optional name for the op.
  
      Returns:
        A Tensor of dtype string.
      """
      return gen_parsing_ops.serialize_tensor(tensor, name)
  
imports: from tensorflow.python.eager import context
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor
  from tensorflow.python.lib.io import python_io
  from tensorflow.python.ops import gen_data_flow_ops
  from tensorflow.python.ops import gen_io_ops
  from tensorflow.python.ops import gen_parsing_ops
  from tensorflow.python.ops.gen_io_ops import *
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 4 for calling a global function with example usage: serialize_tensor(tensor, name) and returns: gen_parsing_ops