code: def multi_target_data(name_list, shape, dtype=tf.float32):
      """Multi Target Data.
  
      Create and concatenate multiple placeholders. To be used when a regression
      layer uses targets from different sources.
  
      Arguments:
          name_list: list of `str`. The names of the target placeholders.
          shape: list of `int`. The shape of the placeholders.
          dtype: `tf.type`, Placeholder data type (optional). Default: float32.
  
      Return:
          A `Tensor` of the concatenated placeholders.
  
      """
      placeholders = []
      for i in range(len(name_list)):
          with tf.name_scope(name_list[i]):
              p = tf.placeholder(shape=shape, dtype=dtype, name="Y")
          if p not in tf.get_collection(tf.GraphKeys.TARGETS):
              tf.add_to_collection(tf.GraphKeys.TARGETS, p)
          placeholders.append(p)
  
      return tf.concat(placeholders, axis=0)
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  import tensorflow.compat.v1
  from tensorflow.python.framework import dtypes
  from tensorflow.python.ops import standard_ops
  import tflearn
  from tflearn import utils
  from tflearn import variables
  from tflearn import activations
  from tflearn import initializations
  from tflearn import regularizers
label_desc: Use image node_id 12 for calling a global function with example usage: multi_target_data(name_list, shape, dtype) and returns: tf