def METHOD_NAME(weights, torch_block, hidden_size):
    # layernorm 1
    layer_norm_1 = weights[0][0][0]
    layer_norm_1_weight = np.asarray(layer_norm_1[0])
    layer_norm_1_bias = np.asarray(layer_norm_1[1])
    set_param(
        torch_block.attention.layer_norm,
        torch.tensor(layer_norm_1_weight),
        torch.tensor(layer_norm_1_bias),
    )
    # lsh weights + output
    attn_weights = weights[0][1]
    if len(attn_weights) < 4:
        set_layer_weights_in_torch_lsh(attn_weights, torch_block.attention, hidden_size)
    else:
        set_layer_weights_in_torch_local(attn_weights, torch_block.attention, hidden_size)
    # intermediate weighs
    intermediate_weights = weights[2][0][1][2]
    # Chunked Feed Forward
    if len(intermediate_weights) == 4:
        intermediate_weights = intermediate_weights[2]
    # layernorm 2
    layer_norm_2_weight = np.asarray(intermediate_weights[0][0])
    layer_norm_2_bias = np.asarray(intermediate_weights[0][1])
    set_param(
        torch_block.feed_forward.layer_norm,
        torch.tensor(layer_norm_2_weight),
        torch.tensor(layer_norm_2_bias),
    )
    # intermediate dense
    inter_dense_weight = np.asarray(intermediate_weights[1][0])
    inter_dense_bias = np.asarray(intermediate_weights[1][1])
    set_param(
        torch_block.feed_forward.dense.dense,
        torch.tensor(inter_dense_weight).transpose(0, 1).contiguous(),
        torch.tensor(inter_dense_bias),
    )
    # intermediate out
    out_dense_weight = np.asarray(intermediate_weights[4][0])
    out_dense_bias = np.asarray(intermediate_weights[4][1])
    set_param(
        torch_block.feed_forward.output.dense,
        torch.tensor(out_dense_weight).transpose(0, 1).contiguous(),
        torch.tensor(out_dense_bias),
    )