class_docstr: Custom Head for GATE.
  
  Args:
      input_dim (int): Input dimension of the head
      hparams (DictConfig): Config of the model
class_docstr_tok: ["Custom","Head","for","GATE",".","Args",":","input_dim","(","int",")",":","Input","dimension","of","the","head","hparams","(","DictConfig",")",":","Config","of","the","model"]
code: def _get_head_from_config(self):
      _head_callable = getattr(blocks, self.hparams.head)
      return _head_callable(
          in_units=self.input_dim,
          output_dim=self.hparams.output_dim,
          config=_head_callable._config_template(
              **self.hparams.head_config
          ),
      )
  
imports: import torch
  import torch.nn
  from omegaconf import DictConfig
  from pytorch_tabular.models.common.heads import blocks
  from pytorch_tabular.models.common.layers import Add, Embedding1dLayer, GatedFeatureLearningUnit, NeuralDecisionTree
  from pytorch_tabular.models.common.layers.activations import entmax15, entmoid15, sparsemax, sparsemoid, t_softmax
  from pytorch_tabular.utils import get_logger
  from ..base_model import BaseModel
label_desc: Use image node_id 2 for calling the CustomHead obj's underlying member method code with example usage: obj._get_head_from_config() and returns: _head_callable