class_docstr: Layer that concatenates a list of inputs.
  
  It takes as input a list of tensors, all of the same shape except
  for the concatenation axis, and returns a single tensor that is the
  concatenation of all inputs.
  
  >>> 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(axis=1)([x, y])
  <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]]])>
  
  >>> x1 = tf.keras.layers.Dense(8)(np.arange(10).reshape(5, 2))
  >>> x2 = tf.keras.layers.Dense(8)(np.arange(10, 20).reshape(5, 2))
  >>> concatted = tf.keras.layers.Concatenate()([x1, x2])
  >>> concatted.shape
  TensorShape([5, 16])
docstr: Instantiates a 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(axis=1)([x, y])
  <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:
    axis: Axis along which to concatenate.
    **kwargs: standard layer keyword arguments.
docstr_tok: ["Instantiates","a","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","(","axis=1",")","(","[","x",",","y","]",")","<","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",":","axis",":","Axis","along","which","to","concatenate",".","*","*","kwargs",":","standard","layer","keyword","arguments","."]
code: def __init__(self, axis=-1, **kwargs):
      """Instantiates a 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(axis=1)([x, y])
      <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:
        axis: Axis along which to concatenate.
        **kwargs: standard layer keyword arguments.
      """
      super(Concatenate, self).__init__(**kwargs)
      self.axis = axis
      self.supports_masking = True
      self._reshape_required = False
  
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 1 to create a new Concatenate object from inherited base classes: _Merge with example: obj = Concatenate(axis)