code: def remove_pad(x, pad_remover, mode):
      """Remove padding by concatenating all dimension into one.
  
      Args:
        x (tf.Tensor): input of shape [batch_size, length, depth]
        pad_remover (obj): a PadRemover object
        mode (ModeKeys): infer, train or eval. If inference, the padding remover is
          not applied
  
      Returns:
        tf.Tensor of shape [1,length_nonpad,depth] where
          length_nonpad <= batch_size*length
      """
      # Concatenate all tokens (without padding)
      x = expert_utils.flatten_all_but_last(x)
  
      # Remove padding for training and eval
      if mode != ModeKeys.PREDICT:
          # This is a hack to allows inference when the <go> token
          # is detected as padding and removed. This works for now because there is
          # no padding at inference.
          x = pad_remover.remove(x)
  
      x = tf.expand_dims(x, axis=0)  # Now batch_size=1
      return x
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  from six.moves import range
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import expert_utils
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 4 for calling a global function with example usage: remove_pad(x, pad_remover, mode) and returns: x