code: def _tile_batch(t, multiplier):
      """Core single-tensor implementation of tile_batch."""
      t = tf.convert_to_tensor(t, name="t")
      shape_t = tf.shape(t)
      if t.shape.ndims is None or t.shape.ndims < 1:
          raise ValueError("t must have statically known rank")
      tiling = [1] * (t.shape.ndims + 1)
      tiling[1] = multiplier
      tiled_static_batch_size = (
          t.shape[0] * multiplier if t.shape[0] is not None else None
      )
      tiled = tf.tile(tf.expand_dims(t, 1), tiling)
      tiled = tf.reshape(
          tiled, tf.concat(([shape_t[0] * multiplier], shape_t[1:]), 0)
      )
      tiled.set_shape(
          tf.TensorShape([tiled_static_batch_size]).concatenate(
              t.shape[1:]
          )
      )
      return tiled
  
imports: import collections
  import numpy
  import tensorflow
  from tensorflow_addons import options
  from tensorflow_addons.seq2seq import attention_wrapper
  from tensorflow_addons.seq2seq import decoder
  from tensorflow_addons.utils import keras_utils
  from tensorflow_addons.utils.resource_loader import LazySO
  from tensorflow_addons.utils.types import FloatTensorLike, TensorLike, Number
  from typeguard import typechecked
  from typing import Callable, Optional
label_desc: Use image node_id 1 for calling a global function with example usage: _tile_batch(t, multiplier) and returns: tiled