class_docstr: Front Convolutional Layers
  
  Args:
      dim (`int`):
          Dimension of features
      h (`int`):
          Height of input.
      in_f (`int`):
          Padding parameter.
      in_t (`int`):
          Padding parameter.
      fuller (`bool=False`):
          Whether to use group in conv2 layer.
      activation (`str`):
          Activation layer.
      strides (`list of tuple`):
          A list of stride tuples.
      relu_after_bn (`bool`):
          Whether to add ReLU after batch norm.
class_docstr_tok: ["Front","Convolutional","Layers","Args",":","dim","(","`","int","`",")",":","Dimension","of","features","h","(","`","int","`",")",":","Height","of","input",".","in_f","(","`","int","`",")",":","Padding","parameter",".","in_t","(","`","int","`",")",":","Padding","parameter",".","fuller","(","`","bool=False","`",")",":","Whether","to","use","group","in","conv2","layer",".","activation","(","`","str","`",")",":","Activation","layer",".","strides","(","`","list","of","tuple","`",")",":","A","list","of","stride","tuples",".","relu_after_bn","(","`","bool","`",")",":","Whether","to","add","ReLU","after","batch","norm","."]
code: def forward(self, x):
      x = x.unsqueeze(1)
      for _, conv in enumerate(self.convs):
          x = conv(x)
      return x
  
file_path: towhee/towhee/models/nnfp/nnfp.py
imports: import torch
  from torch import nn
  from towhee.models.layers.conv2d_separable import SeparableConv2d
label_desc: Use image node_id 3 for calling the FrontConv obj's underlying member method code with example usage: obj.forward(x) and returns: x