class_docstr: A layer that concats multiple tensors according to given axis.
  
  Parameters
  ----------
  concat_dim : int
      The dimension to concatenate.
  name : None or str
      A unique layer name.
  
  Examples
  ----------
  >>> class CustomModel(tl.models.Model):
  >>>     def __init__(self):
  >>>         super(CustomModel, self).__init__(name="custom")
  >>>         self.dense1 = tl.layers.Dense(in_channels=20, n_units=10, act=tf.nn.relu, name='relu1_1')
  >>>         self.dense2 = tl.layers.Dense(in_channels=20, n_units=10, act=tf.nn.relu, name='relu2_1')
  >>>         self.concat = tl.layers.Concat(concat_dim=1, name='concat_layer')
  
  >>>     def forward(self, inputs):
  >>>         d1 = self.dense1(inputs)
  >>>         d2 = self.dense2(inputs)
  >>>         outputs = self.concat([d1, d2])
  >>>         return outputs
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docstr: prev_layer : list of :class:`Layer`
      List of layers to concatenate.
docstr_tok: ["prev_layer",":","list","of",":","class",":","`","Layer","`","List","of","layers","to","concatenate","."]
code: def forward(self, inputs):
      """
  
      prev_layer : list of :class:`Layer`
          List of layers to concatenate.
      """
      outputs = tf.concat(inputs, self.concat_dim, name=self.name)
  
      return outputs
  
file_path: TensorLayer/tensorlayer/layers/merge.py
imports: import tensorflow
  from tensorlayer import logging
  from tensorlayer.layers.core import Layer
label_desc: Use image node_id 4 for calling the Concat obj's underlying member method code with example usage: obj.forward(inputs) and returns: outputs