class_docstr: Vision transformer basic head.
  ::
                                    SequencePool
                                         ↓
                                      Dropout
                                         ↓
                                     Projection
                                         ↓
                                     Activation
  Args:
      in_features ('int'):
          Input channel size of the resnet head.
      out_features ('int'):
          Output channel size of the resnet head.
      seq_pool_type ('str'):
          Pooling type. It supports "cls", "mean " and "none". If set to
          "cls", it assumes the first element in the input is the cls token and
          returns it. If set to "mean", it returns the mean of the entire sequence.
      activation ('callable'):
          A callable that constructs vision transformer head activation layer,
          examples include: nn.ReLU, nn.Softmax, nn.Sigmoid, and None (not applying activation).
      dropout_rate ('float'):
          Dropout rate.
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      # Performs pooling.
      if self.seq_pool_model is not None:
          x = self.seq_pool_model(x)
      # Performs dropout.
      if self.dropout is not None:
          x = self.dropout(x)
      # Performs projection.
      x = self.proj(x)
      # Performs activation.
      if self.activation_model is not None:
          x = self.activation_model(x)
      return x
  
imports: import torch
  from torch import nn
  from towhee.models.layers.sequence_pool import SequencePool
label_desc: Use image node_id 2 for calling the VisionTransformerBasicHead obj's underlying member method code with example usage: obj.forward(x) and returns: x