class_docstr: This class is an activation layer that is used with model_type 2.
  
  Examples
  --------
  >>> from deepchem.models.dft.dftxc import ExpM1Activation
  >>> import torch
  >>> model = ExpM1Activation()
  >>> x = torch.tensor(2.5)
  >>> output = model(x)
class_docstr_tok: ["This","class","is","an","activation","layer","that","is","used","with","model_type","2",".","Examples","--","--","--","--",">",">",">","from","deepchem.models.dft.dftxc","import","ExpM1Activation",">",">",">","import","torch",">",">",">","model","=","ExpM1Activation","(",")",">",">",">","x","=","torch.tensor","(","2.5",")",">",">",">","output","=","model","(","x",")"]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      return torch.exp(x) - 1
  
file_path: deepchem/deepchem/models/dft/dftxc.py
imports: from deepchem.models.dft.scf import XCNNSCF
  import torch
  from deepchem.models.dft.nnxc import HybridXC
  from deepchem.models.losses import Loss, L2Loss
  from deepchem.models.torch_models.torch_model import TorchModel
  from typing import Tuple, Optional, List, Any
  import numpy
label_desc: Use image node_id 1 for calling the ExpM1Activation obj's underlying member method code with example usage: obj.forward(x) and returns: unknown