class_docstr: The Concat module outputs the concatenation of the outputs from multiple modules.
  
  Parameters
  ----------
  module_list: list
      The list of modules.
  scope: str, optional
      Scope for module variables.
  reuse: bool, optional
      Whether or not to reuse module variables.
class_docstr_tok: ["The","Concat","module","outputs","the","concatenation","of","the","outputs","from","multiple","modules",".","Parameters","--","--","--","--","--","module_list",":","list","The","list","of","modules",".","scope",":","str",",","optional","Scope","for","module","variables",".","reuse",":","bool",",","optional","Whether","or","not","to","reuse","module","variables","."]
code: def __init__(self, module_list, axis=1, scope=None, reuse=False):
      self._module_list = module_list
      self._axis = axis
  
      super(Concat, self).__init__(
          l2_reg=None, scope=scope, reuse=reuse
      )
  
imports: import tensorflow
  from openrec.tf1.legacy.modules.fusions import Fusion
label_desc: Use image node_id 1 to create a new Concat object from inherited base classes: Fusion with example: obj = Concat(module_list, axis, scope, reuse)