class_docstr: The projection head used by contrast learning.
  Args:
      dim_in (int): The dimensions of input features.
      proj_dim (int, optional): The output dimensions of projection head. Default: 256.
      proj (str, optional): The type of projection head, only support 'linear' and 'convmlp'. Default: 'convmlp'.
class_docstr_tok: ["The","projection","head","used","by","contrast","learning",".","Args",":","dim_in","(","int",")",":","The","dimensions","of","input","features",".","proj_dim","(","int",",","optional",")",":","The","output","dimensions","of","projection","head",".","Default",":","256.","proj","(","str",",","optional",")",":","The","type","of","projection","head",",","only","support","'linear","'","and","'convmlp","'",".","Default",":","'convmlp","'","."]
code: def forward(self, x):
      return F.normalize(self.proj(x), p=2, axis=1)
  
file_path: PaddleSeg/paddleseg/models/hrnet_contrast.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 2 for calling the ProjectionHead obj's underlying member method code with example usage: obj.forward(x) and returns: F