code: def reshape(incoming, new_shape, name="Reshape"):
      """Reshape.
  
      A layer that reshape the incoming layer tensor output to the desired shape.
  
      Arguments:
          incoming: A `Tensor`. The incoming tensor.
          new_shape: A list of `int`. The desired shape.
          name: A name for this layer (optional).
  
      """
  
      with tf.name_scope(name) as scope:
          inference = incoming
          if isinstance(inference, list):
              inference = tf.concat(0, inference)
              inference = tf.cast(inference, tf.float32)
          inference = tf.reshape(inference, shape=new_shape)
  
      inference.scope = scope
  
      # Track output tensor.
      tf.add_to_collection(
          tf.GraphKeys.LAYER_TENSOR + "/" + name, inference
      )
  
      return inference
  
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 5 for calling a global function with example usage: reshape(incoming, new_shape, name) and returns: inference