class_docstr: This class projects the input image to the same spatial dimensions as the feature map.
  For example, if the input image is 512 x512 x3 and spatial dimensions of feature map size are 56x56xF, then
  this class will generate an output of 56x56x3
docstr: :param samplingTimes: The rate at which you want to down-sample the image
docstr_tok: [":","param","samplingTimes",":","The","rate","at","which","you","want","to","down-sample","the","image"]
code: def __init__(self, samplingTimes):
      """
      :param samplingTimes: The rate at which you want to down-sample the image
      """
      super().__init__()
      self.pool = nn.ModuleList()
      for i in range(0, samplingTimes):
          # pyramid-based approach for down-sampling
          self.pool.append(nn.AvgPool2d(3, stride=2, padding=1))
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new InputProjectionA object from inherited base classes: nn with example: obj = InputProjectionA(samplingTimes)