How to use the hls4ml.converters.pytorch_to_hls.PyTorchDataReader function in hls4ml

def pytorch_to_hls(yamlConfig):

    ######################
    ##  Do translation
    ######################

    print('Interpreting Model')
    reader = PyTorchDataReader(yamlConfig)

    core_layers = ['Linear']
    skip_layers = ['Dropout', 'Flatten']
    activation_layers = ['ReLU', 'Sigmoid', 'Tanh', 'SELU', 'LeakyReLU', 'Softmax', 'Softplus', 'Softsign']
    supported_layers = core_layers + skip_layers + activation_layers

    #This is a list of dictionaries to hold all the layer info we need to generate HLS
    layer_list = []

    #Loop through layers
    print('Topology:')
    modelstr = repr(reader.torch_model).split('\n')
    for pytorch_layer in modelstr:
        layer_match = re.match(r'\((\d)\): (\w+)\((.*)\)', pytorch_layer.strip())
        if layer_match is None:
            continue

layer['name'] = layer['activation'] + '_' + str(layer_idx)

        layer_list.append(layer)

    input_layer = {}
    input_layer['name'] = 'input1'
    input_layer['class_name'] = 'InputLayer'
    input_layer['input_shape'] = [layer_list[0]['n_in']]
    layer_list.insert(0, input_layer)


    #################
    ## Generate HLS
    #################

    reader = PyTorchDataReader(yamlConfig)
    print('Creating HLS model')
    hls_model = HLSModel(yamlConfig, reader, layer_list)
    optimizers = ['eliminate_linear_activation', 'merge_batch_norm_quantized_tanh', 'quantize_dense_output']
    optimize_model(hls_model, optimizers)
    return hls_model