def convert_maxpool3(params, w_name, scope_name, inputs, layers, weights, names):
print('<STR_LIT>')<EOL>if names == '<STR_LIT>':<EOL><INDENT>tf_name = '<STR_LIT:P>' + random_string(<NUM_LIT:7>)<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>if '<STR_LIT>' in params:<EOL><INDENT>height, width, depth = params['<STR_LIT>']<EOL><DEDENT>else:<EOL><INDENT>height, width, depth = params['<STR_LIT>']<EOL><DEDENT>if '<STR_LIT>' in params:<EOL><INDENT>stride_height, stride_width, stride_depth = params['<STR_LIT>']<EOL><DEDENT>else:<EOL><INDENT>stride_height, stride_width, stride_depth = params['<STR_LIT>']<EOL><DEDENT>if '<STR_LIT>' in params:<EOL><INDENT>padding_h, padding_w, padding_d, _, _ = params['<STR_LIT>']<EOL><DEDENT>else:<EOL><INDENT>padding_h, padding_w, padding_d = params['<STR_LIT>']<EOL><DEDENT>input_name = inputs[<NUM_LIT:0>]<EOL>if padding_h > <NUM_LIT:0> and padding_w > <NUM_LIT:0> and padding_d > <NUM_LIT:0>:<EOL><INDENT>padding_name = tf_name + '<STR_LIT>'<EOL>padding_layer = keras.layers.ZeroPadding3D(<EOL>padding=(padding_h, padding_w, padding_d),<EOL>name=padding_name<EOL>)<EOL>layers[padding_name] = padding_layer(layers[inputs[<NUM_LIT:0>]])<EOL>input_name = padding_name<EOL><DEDENT>pooling = keras.layers.MaxPooling3D(<EOL>pool_size=(height, width, depth),<EOL>strides=(stride_height, stride_width, stride_depth),<EOL>padding='<STR_LIT>',<EOL>name=tf_name<EOL>)<EOL>layers[scope_name] = pooling(layers[input_name])<EOL>
Convert 3d Max pooling.

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