def build_graph(parameters):
    'Build a transpose_conv graph given `parameters`.'
    (input_shape, filter_shape) = get_tensor_shapes(parameters)
    input_tensor = tf.compat.v1.placeholder(dtype=tf.float32, name='input', shape=input_shape)
    filter_input = tf.compat.v1.placeholder(dtype=tf.float32, name='filter', shape=filter_shape)
    if (not parameters['fully_quantize']):
        input_tensors = [input_tensor, filter_input]
        conv_outputs = tf.nn.conv2d(input_tensor, filter_input, strides=parameters['strides'], padding=parameters['padding'], data_format=parameters['data_format'])
        out = tf.compat.v1.nn.conv2d_backprop_input(input_shape, filter_input, conv_outputs, strides=parameters['strides'], padding=parameters['padding'], data_format=parameters['data_format'])
    else:
        input_tensors = [input_tensor]
        filter_input = create_tensor_data(np.float32, filter_shape, min_value=(- 1), max_value=1)
        out = tf.nn.conv2d_transpose(input_tensor, filter_input, parameters['output_shape'], strides=parameters['strides'], padding=parameters['padding'], data_format=parameters['data_format'])
    return (input_tensors, [out])
Build a transpose_conv graph given `parameters`.
def build_graph(parameters):
    
    (input_shape, filter_shape) = get_tensor_shapes(parameters)
    input_tensor = tf.compat.v1.placeholder(dtype=tf.float32, name='input', shape=input_shape)
    filter_input = tf.compat.v1.placeholder(dtype=tf.float32, name='filter', shape=filter_shape)
    if (not parameters['fully_quantize']):
        input_tensors = [input_tensor, filter_input]
        conv_outputs = tf.nn.conv2d(input_tensor, filter_input, strides=parameters['strides'], padding=parameters['padding'], data_format=parameters['data_format'])
        out = tf.compat.v1.nn.conv2d_backprop_input(input_shape, filter_input, conv_outputs, strides=parameters['strides'], padding=parameters['padding'], data_format=parameters['data_format'])
    else:
        input_tensors = [input_tensor]
        filter_input = create_tensor_data(np.float32, filter_shape, min_value=(- 1), max_value=1)
        out = tf.nn.conv2d_transpose(input_tensor, filter_input, parameters['output_shape'], strides=parameters['strides'], padding=parameters['padding'], data_format=parameters['data_format'])
    return (input_tensors, [out])