class_docstr: Applies a normalization layer followed by an activation layer.
  
  Args:
      opts: Command-line arguments.
      num_features: :math:`C` from an expected input of size :math:`(N, C, H, W)`.
  
  Shape:
      - Input: :math:`(N, C, H, W)`.
      - Output: :math:`(N, C, H, W)`.
class_docstr_tok: ["Applies","a","normalization","layer","followed","by","an","activation","layer",".","Args",":","opts",":","Command-line","arguments",".","num_features",":",":","math",":","`","C","`","from","an","expected","input","of","size",":","math",":","`","(","N",",","C",",","H",",","W",")","`",".","Shape",":","-","Input",":",":","math",":","`","(","N",",","C",",","H",",","W",")","`",".","-","Output",":",":","math",":","`","(","N",",","C",",","H",",","W",")","`","."]
code: def forward(self, x: Tensor) -> Tensor:
      return self.block(x)
  
imports: import argparse
  from typing import Optional, Tuple, Type, Union
  from torch import Tensor, nn
  from cvnets.layers.activation import build_activation_layer
  from cvnets.layers.base_layer import BaseLayer
  from cvnets.layers.normalization.layer_norm import LayerNorm, LayerNorm2D_NCHW
  from cvnets.layers.normalization_layers import get_normalization_layer
  from utils import logger
label_desc: Use image node_id 2 for calling the NormActLayer obj's underlying member method code with example usage: obj.forward(x) and returns: self