code: def concatenate(inputs, axis=-1, **kwargs):
      """Functional interface to the `Concatenate` layer.
  
      >>> x = np.arange(20).reshape(2, 2, 5)
      >>> print(x)
      [[[ 0  1  2  3  4]
        [ 5  6  7  8  9]]
       [[10 11 12 13 14]
        [15 16 17 18 19]]]
      >>> y = np.arange(20, 30).reshape(2, 1, 5)
      >>> print(y)
      [[[20 21 22 23 24]]
       [[25 26 27 28 29]]]
      >>> tf.keras.layers.concatenate([x, y],
      ...                             axis=1)
      <tf.Tensor: shape=(2, 3, 5), dtype=int64, numpy=
      array([[[ 0,  1,  2,  3,  4],
            [ 5,  6,  7,  8,  9],
            [20, 21, 22, 23, 24]],
           [[10, 11, 12, 13, 14],
            [15, 16, 17, 18, 19],
            [25, 26, 27, 28, 29]]])>
  
      Args:
          inputs: A list of input tensors (at least 2).
          axis: Concatenation axis.
          **kwargs: Standard layer keyword arguments.
  
      Returns:
          A tensor, the concatenation of the inputs alongside axis `axis`.
      """
      return Concatenate(axis=axis, **kwargs)(inputs)
  
imports: from tensorflow.python.keras import backend
  from tensorflow.python.keras.engine import base_layer_utils
  from tensorflow.python.keras.engine.base_layer import Layer
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn
label_desc: Use image node_id 7 for calling a global function with example usage: concatenate(inputs, axis) and returns: Concatenate