def convert_relu(params, w_name, scope_name, inputs, layers, weights, names):
print('<STR_LIT>')<EOL>if names == '<STR_LIT>':<EOL><INDENT>tf_name = '<STR_LIT>' + random_string(<NUM_LIT:4>)<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>relu = keras.layers.Activation('<STR_LIT:relu>', name=tf_name)<EOL>layers[scope_name] = relu(layers[inputs[<NUM_LIT:0>]])<EOL>
Convert relu layer.

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