def convert_concat(params, w_name, scope_name, inputs, layers, weights, names):
print('<STR_LIT>')<EOL>concat_nodes = [layers[i] for i in inputs]<EOL>if len(concat_nodes) == <NUM_LIT:1>:<EOL><INDENT>layers[scope_name] = concat_nodes[<NUM_LIT:0>]<EOL>return<EOL><DEDENT>if names == '<STR_LIT>':<EOL><INDENT>tf_name = '<STR_LIT>' + random_string(<NUM_LIT:5>)<EOL><DEDENT>elif names == '<STR_LIT>':<EOL><INDENT>tf_name = w_name<EOL><DEDENT>else:<EOL><INDENT>tf_name = w_name + str(random.random())<EOL><DEDENT>cat = keras.layers.Concatenate(name=tf_name, axis=params['<STR_LIT>'])<EOL>layers[scope_name] = cat(concat_nodes)<EOL>
Convert concatenation.

Args:
    params: dictionary with layer parameters
    w_name: name prefix in state_dict
    scope_name: pytorch scope name
    inputs: pytorch node inputs
    layers: dictionary with keras tensors
    weights: pytorch state_dict
    names: use short names for keras layers