code: def __init__(
      self,
      num_init_features,
      kernel_size=7,
      padding=3,
      activation_fn=nn.ReLU(inplace=True),
  ):
      super(InputBlock, self).__init__()
      self.conv = nn.Conv2d(
          3,
          num_init_features,
          kernel_size=kernel_size,
          stride=2,
          padding=padding,
          bias=False,
      )
      self.bn = nn.BatchNorm2d(num_init_features, eps=0.001)
      self.act = activation_fn
      self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
  
imports: import os
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.model_zoo
  from collections import OrderedDict
  from .adaptive_avgmax_pool import adaptive_avgmax_pool2d
  from .convert_from_mxnet import convert_from_mxnet, has_mxnet
label_desc: Use image node_id 1 to create a new InputBlock object from inherited base classes: nn with example: obj = InputBlock(num_init_features, kernel_size, padding, activation_fn)