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 _build_shared_graph(self):
      with tf.variable_scope(self._scope, reuse=self._reuse):
          outputs = sum(
              [cell.get_outputs() for cell in self._module_list], []
          )
          self._outputs.append(
              tf.concat(values=outputs, axis=self._axis)
          )
          self._loss = 0.0
  
imports: import tensorflow
  from openrec.tf1.legacy.modules.fusions import Fusion
label_desc: Use image node_id 2 for calling the Concat obj's underlying member method code with example usage: obj._build_shared_graph() without return types