A map of modules from TF to PyTorch.
        This time I use a map to keep the PyTorch model as identical to the original PyTorch model as possible.

def build_tf_to_pytorch_map(model, config):
    """ A map of modules from TF to PyTorch.
        This time I use a map to keep the PyTorch model as identical to the original PyTorch model as possible.
    """
    tf_to_pt_map = {}

    if hasattr(model, 'transformer'):
        # We are loading in a TransfoXLLMHeadModel => we will load also the Adaptive Softmax
        tf_to_pt_map.update({
            "transformer/adaptive_softmax/cutoff_0/cluster_W": model.crit.cluster_weight,
            "transformer/adaptive_softmax/cutoff_0/cluster_b": model.crit.cluster_bias})
        for i, (out_l, proj_l, tie_proj) in enumerate(zip(
                                model.crit.out_layers,
                                model.crit.out_projs,
                                config.tie_projs)):
            layer_str = "transformer/adaptive_softmax/cutoff_%d/" % i
            if config.tie_weight:
                tf_to_pt_map.update({
                    layer_str + 'b': out_l.bias})
            else:
                raise NotImplementedError
                # I don't think this is implemented in the TF code
                tf_to_pt_map.update({
                    layer_str + 'lookup_table': out_l.weight,
                    layer_str + 'b': out_l.bias})
            if not tie_proj:
                tf_to_pt_map.update({
                    layer_str + 'proj': proj_l
                    })
        # Now load the rest of the transformer
        model = model.transformer

    # Embeddings
    for i, (embed_l, proj_l) in enumerate(zip(model.word_emb.emb_layers, model.word_emb.emb_projs)):
        layer_str = "transformer/adaptive_embed/cutoff_%d/" % i
        tf_to_pt_map.update({
            layer_str + 'lookup_table': embed_l.weight,
            layer_str + 'proj_W': proj_l
            })

    # Transformer blocks
    for i, b in enumerate(model.layers):
        layer_str = "transformer/layer_%d/" % i
        tf_to_pt_map.update({
            layer_str + "rel_attn/LayerNorm/gamma": b.dec_attn.layer_norm.weight,
            layer_str + "rel_attn/LayerNorm/beta": b.dec_attn.layer_norm.bias,
            layer_str + "rel_attn/o/kernel": b.dec_attn.o_net.weight,
            layer_str + "rel_attn/qkv/kernel": b.dec_attn.qkv_net.weight,
            layer_str + "rel_attn/r/kernel": b.dec_attn.r_net.weight,
            layer_str + "ff/LayerNorm/gamma": b.pos_ff.layer_norm.weight,
            layer_str + "ff/LayerNorm/beta": b.pos_ff.layer_norm.bias,
            layer_str + "ff/layer_1/kernel": b.pos_ff.CoreNet[0].weight,
            layer_str + "ff/layer_1/bias": b.pos_ff.CoreNet[0].bias,
            layer_str + "ff/layer_2/kernel": b.pos_ff.CoreNet[3].weight,
            layer_str + "ff/layer_2/bias": b.pos_ff.CoreNet[3].bias,
        })

    # Relative positioning biases
    if config.untie_r:
        r_r_list = []
        r_w_list = []
        for b in model.layers:
            r_r_list.append(b.dec_attn.r_r_bias)
            r_w_list.append(b.dec_attn.r_w_bias)
    else:
        r_r_list = [model.r_r_bias]
        r_w_list = [model.r_w_bias]
    tf_to_pt_map.update({
        'transformer/r_r_bias': r_r_list,
        'transformer/r_w_bias': r_w_list})
    return tf_to_pt_map