code: def forward(self, x):
      pooled_x = []
      for global_pool in self.global_pools:
          pooled_x.append(global_pool(x))
      pooled_x = torch.cat(pooled_x, dim=1).flatten(start_dim=1)
      channel_attention_weights = self.attention_transform(pooled_x)[
          ..., None, None
      ]
      return channel_attention_weights * x
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the ChannelAttention obj's underlying member method code with example usage: obj.forward(x) and returns: unknown