How to convert a pytorch script into tensorflow?

I am trying to convert a pytorch script into tensorflow, how may I do so? Do we do it line by line or does the overall structure change in tensorflow?

Please, someone help me with this and provide some usefull link for this!

The code refers to graph convolution network. I see that pytorch_geometric has predefined modules like MessagePassing from which GCNConv is inheriting.

Is there any similar module in tensorflow?

GCN Code:

import torch
from torch.nn import Parameter
from torch_scatter import scatter_add
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import remove_self_loops, add_self_loops

from inits import glorot, zeros
import pdb

class GCNConv(MessagePassing):
def __init__(self,
    super(GCNConv, self).__init__('add')

    self.in_channels = in_channels
    self.out_channels = out_channels
    self.improved = improved
    self.cached = cached
    self.cached_result = None

    self.weight = Parameter(torch.Tensor(in_channels, out_channels))

    if bias:
        self.bias = Parameter(torch.Tensor(out_channels))
        self.register_parameter('bias', None)


def reset_parameters(self):
    self.cached_result = None

def norm(edge_index, num_nodes, edge_weight, improved=False, dtype=None):
    if edge_weight is None:
        edge_weight = torch.ones((edge_index.size(1), ),
    edge_weight = edge_weight.view(-1)
    assert edge_weight.size(0) == edge_index.size(1)

    edge_index, edge_weight = remove_self_loops(edge_index, edge_weight)
    edge_index, _ = add_self_loops(edge_index, num_nodes=num_nodes)
    loop_weight = torch.full((num_nodes, ),
                             1 if not improved else 2,
    edge_weight =[edge_weight, loop_weight], dim=0)

    row, col = edge_index
    deg = scatter_add(edge_weight, col, dim=0, dim_size=num_nodes)
    deg_inv_sqrt = deg.pow(-1)
    deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0

    return edge_index, deg_inv_sqrt[col] * edge_weight

def forward(self, x, edge_index, edge_weight=None):
    x = torch.matmul(x, self.weight)

    if not self.cached or self.cached_result is None:
        edge_index, norm = self.norm(edge_index, x.size(0), edge_weight,
                                     self.improved, x.dtype)
        self.cached_result = edge_index, norm
    edge_index, norm = self.cached_result

    return self.propagate(edge_index, x=x, norm=norm)

def message(self, x_j, norm):
    return norm.view(-1, 1) * x_j

def update(self, aggr_out):
    if self.bias is not None:
        aggr_out = aggr_out + self.bias
    return aggr_out

def __repr__(self):
    return '{}({}, {})'.format(self.__class__.__name__, self.in_channels,

The script is of a graph convolutional network. (source: source code )

Kindly someone help me with this! Thanks

Check this; you can create an issue and discuss it further there.