code: def pad_and_concat(
      values, axis, rank=None, pad_axis=None, pad_constant_values=0
  ):
      """Concats tensors along one dimension. Pads each of other dimensions of
      the tensors to the corresponding maximum size if necessary.
  
      Args:
          values: A list of Tensors of the same rank.
          axis (int): A Python int. Dimension along which to concatenate.
          rank (int, optional): Rank of the tensors. If `None`, inferred
              automatically from :attr:`values`.
          pad_axis (int or list, optional): A Python int or a list of int.
              Dimensions to pad. Paddings are only added to the end of
              corresponding dimensions. If `None`, all dimensions except the
              :attr:`axis` dimension are padded.
          pad_constant_values: The scalar pad value to use. Must be same type
              as the tensors.
  
      Returns:
          A `Tensor` resulting from padding and concatenation of the input
          tensors.
  
      Raises:
          ValueError: If :attr:`rank` is `None` and cannot be inferred from
              :attr:`values`.
  
  
      Example:
  
          .. code-block:: python
  
              a = tf.ones([1, 2])
              b = tf.ones([2, 3])
  
              c = pad_and_concat([a,b], 0)
              # c.shape == [3, 3]
              # c == [[1, 1, 0],
              #       [1, 1, 1],
              #       [1, 1, 1]]
  
              d = pad_and_concat([a,b], 1)
              # d.shape == [2, 5]
              # d == [[1, 1, 1, 1, 1]
              #       [0, 0, 1, 1, 1]]
      """
      if rank is None:
          for value in values:
              rank = get_rank(value)
              if rank is not None:
                  break
      if rank is None:
          raise ValueError("Cannot determine the rank of the tensors")
  
      def _pad_to_size(value, axis_, size):
          """Pads the :attr:`axis_` of a tensor :attr:`value` to the given
          :attr:`size`. Only pads to the end.
  
          Args:
              value: A Tensor.
              axis_: A Python int.
              size: A scalar int Tensor or Python int.
          """
          paddings = np.zeros([rank, 2], dtype=np.int32)
          paddings[axis_, 1] = 1
          paddings = paddings * (size - tf.shape(value)[axis_])
          return tf.pad(
              value,
              paddings,
              mode="CONSTANT",
              constant_values=pad_constant_values,
          )
  
      if pad_axis is None:
          pad_axis = [r for r in range(rank) if r != axis]
  
      pad_axis = (
          pad_axis
          if isinstance(pad_axis, (list, tuple))
          else [pad_axis]
      )
  
      for pa in pad_axis:
          max_dim_size = tf.reduce_max(
              [tf.shape(v)[pa] for v in values]
          )
          for i, v in enumerate(values):
              values[i] = _pad_to_size(v, pa, max_dim_size)
  
      return tf.concat(values, axis)
  
file_path: texar/texar/tf/utils/shapes.py
imports: import numpy
  import tensorflow
  from tensorflow.python.util import nest
  from tensorflow.python.ops import rnn
  from tensorflow.python.framework import ops
label_desc: Use image node_id 10 for calling a global function with example usage: pad_and_concat(values, axis, rank, pad_axis, pad_constant_values) and returns: tf, tf