code: def nlc_to_nchw(x: Tensor, hw_shape: Sequence[int]) -> Tensor:
      """Convert [N, L, C] shape tensor to [N, C, H, W] shape tensor.
  
      Args:
          x (Tensor): The input tensor of shape [N, L, C] before conversion.
          hw_shape (Sequence[int]): The height and width of output feature map.
  
      Returns:
          Tensor: The output tensor of shape [N, C, H, W] after conversion.
      """
      H, W = hw_shape
      assert len(x.shape) == 3
      B, L, C = x.shape
      assert L == H * W, "The seq_len does not match H, W"
      return x.transpose(1, 2).reshape(B, C, H, W).contiguous()
  
imports: import math
  import warnings
  from typing import Optional, Sequence, Tuple, Union
  import torch
  import torch.nn.functional
  from mmcv.cnn import Linear, build_activation_layer, build_conv_layer, build_norm_layer
  from mmcv.cnn.bricks.drop import Dropout
  from mmengine.model import BaseModule, ModuleList
  from mmengine.utils import to_2tuple
  from torch import Tensor, nn
  from mmdet.registry import MODELS
  from mmdet.utils import OptConfigType, OptMultiConfig
label_desc: Use image node_id 1 for calling a global function with example usage: nlc_to_nchw(x, hw_shape) and returns: x