How to use the tensorflowjs.converters.graph_rewrite_util.node_from_map function in tensorflowjs

neg_alpha_op = None
    for name in mul_op.input:
      op = graph_rewrite_util.node_from_map(input_node_map, name)
      if op.op == 'Const':
        neg_alpha_op = op
        break

    if not neg_alpha_op:
      continue

    alpha_tensor_name = neg_alpha_op.name
    _create_alpha_node(neg_alpha_op, updated_alpha)

    relu_neg_input_op = None
    for name in mul_op.input:
      op = graph_rewrite_util.node_from_map(input_node_map, name)
      if op.op == 'Relu':
        relu_neg_input_op = op
        break

    if (not relu_neg_input_op or len(relu_neg_input_op.input) != 1 or
        relu_neg_input_op.op != 'Relu'):
      continue

    # This detects a Neg op followed by a separated Relu op.
    neg_input_op = graph_rewrite_util.node_from_map(
        input_node_map, relu_neg_input_op.input[0])
    if (not neg_input_op or len(neg_input_op.input) != 1 or
        neg_input_op.op != 'Neg'):
      continue
    final_input_op = neg_input_op

bias = graph_rewrite_util.node_from_map(input_node_map, add_op.input[1])
      if conv_op.op not in ["Conv2D", "DepthwiseConv2dNative"]:
        # Follow the second input of the add to get to the conv.
        conv_op = graph_rewrite_util.node_from_map(
            input_node_map, add_op.input[1])
        bias = graph_rewrite_util.node_from_map(input_node_map, add_op.input[0])
    if bias and bias.op != 'Const':
      tf_logging.warning("The bias %s after the conv %s was not a constant. "
                         "Maybe because freeze_graph wasn't "
                         "run first?" % (bias.name, conv_op.name))
      continue
    if conv_op.op not in ["Conv2D", "DepthwiseConv2dNative"]:
      tf_logging.warning("Didn't find expected Conv2D or DepthwiseConv2dNative"
                         " input to '%s'" % node.name)
      continue
    weights_op = graph_rewrite_util.node_from_map(
        input_node_map, conv_op.input[1])
    if weights_op.op != "Const":
      tf_logging.warning("Didn't find expected conv Constant input to '%s',"
                         " found %s instead. Maybe because freeze_graph wasn't"
                         " run first?" % (conv_op.name, weights_op))
      continue
    weights = graph_rewrite_util.values_from_const(weights_op)
    if conv_op.op == "Conv2D":
      channel_count = weights.shape[3]
    elif conv_op.op == "DepthwiseConv2dNative":
      channel_count = weights.shape[2] * weights.shape[3]

    mean_op = graph_rewrite_util.node_from_map(
        input_node_map,
        node.input[INPUT_ORDER[node.op].index("mean_op")])
    if mean_op.op != "Const":

for node in input_graph_def.node:
    if (node.op not in ('Add', 'AddV2') or len(node.input) != 2):
      continue

    relu_input_op = graph_rewrite_util.node_from_map(
        input_node_map, node.input[0])
    if (not relu_input_op or relu_input_op.op != 'Relu'):
      continue

    mul_op = graph_rewrite_util.node_from_map(input_node_map, node.input[1])
    if (not mul_op or mul_op.op != 'Mul'):
      continue

    neg_alpha_op = None
    for name in mul_op.input:
      op = graph_rewrite_util.node_from_map(input_node_map, name)
      if op.op == 'Const':
        neg_alpha_op = op
        break

    if not neg_alpha_op:
      continue

    alpha_tensor_name = neg_alpha_op.name
    _create_alpha_node(neg_alpha_op, updated_alpha)

    relu_neg_input_op = None
    for name in mul_op.input:
      op = graph_rewrite_util.node_from_map(input_node_map, name)
      if op.op == 'Relu':
        relu_neg_input_op = op
        break

tf_logging.warning("Didn't find expected mean Constant input to '%s',"
                         " found %s instead. Maybe because freeze_graph wasn't"
                         " run first?" % (node.name, mean_op))
      continue
    mean_value = graph_rewrite_util.values_from_const(mean_op)
    if bias is not None:
      # Adjust the mean of the batchnorm based on the add op in-between the conv
      # and the batchnorm.
      mean_value = mean_value - graph_rewrite_util.values_from_const(bias)
    if mean_value.shape != (channel_count,):
      tf_logging.warning("Incorrect shape for mean, found %s, expected %s,"
                         " for node %s" % (str(mean_value.shape), str(
                             (channel_count,)), node.name))
      continue

    var_op = graph_rewrite_util.node_from_map(
        input_node_map,
        node.input[INPUT_ORDER[node.op].index("var_op")])
    if var_op.op != "Const":
      tf_logging.warning("Didn't find expected var Constant input to '%s',"
                         " found %s instead. Maybe because freeze_graph wasn't"
                         " run first?" % (node.name, var_op))
      continue
    var_value = graph_rewrite_util.values_from_const(var_op)
    if var_value.shape != (channel_count,):
      tf_logging.warning("Incorrect shape for var, found %s, expected %s,"
                         " for node %s" % (str(var_value.shape), str(
                             (channel_count,)), node.name))
      continue

    beta_op = graph_rewrite_util.node_from_map(
        input_node_map,

conv_op = graph_rewrite_util.node_from_map(
        input_node_map,
        node.input[INPUT_ORDER[node.op].index("conv_op")])
    # There might be an Add/BiasAdd op between the conv and the batchnorm,
    # which we can fold into the mean param of the batchnorm.
    if conv_op.op in ['BiasAdd', 'Add', 'AddV2']:
      add_op = conv_op
      # Follow the first input of the add to get to the conv.
      conv_op = graph_rewrite_util.node_from_map(
          input_node_map, add_op.input[0])
      bias = graph_rewrite_util.node_from_map(input_node_map, add_op.input[1])
      if conv_op.op not in ["Conv2D", "DepthwiseConv2dNative"]:
        # Follow the second input of the add to get to the conv.
        conv_op = graph_rewrite_util.node_from_map(
            input_node_map, add_op.input[1])
        bias = graph_rewrite_util.node_from_map(input_node_map, add_op.input[0])
    if bias and bias.op != 'Const':
      tf_logging.warning("The bias %s after the conv %s was not a constant. "
                         "Maybe because freeze_graph wasn't "
                         "run first?" % (bias.name, conv_op.name))
      continue
    if conv_op.op not in ["Conv2D", "DepthwiseConv2dNative"]:
      tf_logging.warning("Didn't find expected Conv2D or DepthwiseConv2dNative"
                         " input to '%s'" % node.name)
      continue
    weights_op = graph_rewrite_util.node_from_map(
        input_node_map, conv_op.input[1])
    if weights_op.op != "Const":
      tf_logging.warning("Didn't find expected conv Constant input to '%s',"
                         " found %s instead. Maybe because freeze_graph wasn't"
                         " run first?" % (conv_op.name, weights_op))
      continue

bias = None
    conv_op = graph_rewrite_util.node_from_map(
        input_node_map,
        node.input[INPUT_ORDER[node.op].index("conv_op")])
    # There might be an Add/BiasAdd op between the conv and the batchnorm,
    # which we can fold into the mean param of the batchnorm.
    if conv_op.op in ['BiasAdd', 'Add', 'AddV2']:
      add_op = conv_op
      # Follow the first input of the add to get to the conv.
      conv_op = graph_rewrite_util.node_from_map(
          input_node_map, add_op.input[0])
      bias = graph_rewrite_util.node_from_map(input_node_map, add_op.input[1])
      if conv_op.op not in ["Conv2D", "DepthwiseConv2dNative"]:
        # Follow the second input of the add to get to the conv.
        conv_op = graph_rewrite_util.node_from_map(
            input_node_map, add_op.input[1])
        bias = graph_rewrite_util.node_from_map(input_node_map, add_op.input[0])
    if bias and bias.op != 'Const':
      tf_logging.warning("The bias %s after the conv %s was not a constant. "
                         "Maybe because freeze_graph wasn't "
                         "run first?" % (bias.name, conv_op.name))
      continue
    if conv_op.op not in ["Conv2D", "DepthwiseConv2dNative"]:
      tf_logging.warning("Didn't find expected Conv2D or DepthwiseConv2dNative"
                         " input to '%s'" % node.name)
      continue
    weights_op = graph_rewrite_util.node_from_map(
        input_node_map, conv_op.input[1])
    if weights_op.op != "Const":
      tf_logging.warning("Didn't find expected conv Constant input to '%s',"
                         " found %s instead. Maybe because freeze_graph wasn't"

ValueError: If the graph is badly formed with duplicate node names.
  """
  input_node_map = {}
  nodes_to_skip = {}
  inputs_to_remove = []
  for node in input_graph_def.node:
    if node.name not in input_node_map:
      input_node_map[node.name] = node
    else:
      raise ValueError('Duplicate node names detected for ', node.name)

  for node in input_graph_def.node:
    if node.op != 'Prelu':
      continue

    fused_conv_op = graph_rewrite_util.node_from_map(
        input_node_map, node.input[0])
    if (not fused_conv_op or
        (fused_conv_op.op != '_FusedConv2D'
         and fused_conv_op.op != 'FusedDepthwiseConv2dNative') or
        len(fused_conv_op.attr['fused_ops'].list.s) > 1):
      continue

    alpha_tensor_name = node.input[1]

    fused_conv_op.input.extend([alpha_tensor_name])
    fused_conv_op.attr['fused_ops'].list.s.extend([b'Prelu'])
    fused_conv_op.attr['num_args'].i = fused_conv_op.attr['num_args'].i + 1
    node.op = 'Identity'
    node.input[:] = [node.input[0]]
    nodes_to_skip[node.name] = True
    inputs_to_remove.append(node)