class_docstr: A deep-learning based Variational Monte Carlo method [1]_ for calculating the ab-initio
  solution of a many-electron system.
  
  This model must be pre-trained on the HF baseline and then can be used to improve on it using electronic interactions
  via the learned electron distance features. Ferminet models the orbital values using envelope functions and the
  calculated electron features, which can then be used to approximate the wavefunction for better sampling of electrons.
  
  Example
  -------
  >>> import numpy as np
  >>> import deepchem as dc
  >>> from deepchem.models.torch_models.ferminet import Ferminet
  >>> import torch
  >>> H2_molecule =  torch.Tensor([[0, 0, 0.748], [0, 0, 0]])
  >>> H2_charge = torch.Tensor([[1], [1]])
  >>> model = Ferminet(nucleon_pos=H2_molecule, nuclear_charge=H2_charge, spin=0, batch_size=1)
  >>> electron = np.random.rand(1, 2*3)
  >>> wavefunction = model.forward(electron)
  
  References
  ----------
  .. [1] Spencer, James S., et al. Better, Faster Fermionic Neural Networks. arXiv:2011.07125, arXiv, 13 Nov. 2020. arXiv.org, http://arxiv.org/abs/2011.07125.
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docstr: forward function
  
  Parameters:
  -----------
  input: torch.Tensor
      contains the sampled electrons coordinate in the shape (batch_size,number_of_electrons*3)
  
  Returns:
  --------
  psi: torch.Tensor
      contains the wavefunction - 'psi' value. It is in the shape (batch_size), where each row corresponds to the solution of one of the batches
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code: def forward(self, input: torch.Tensor) -> torch.Tensor:
      """
      forward function
  
      Parameters:
      -----------
      input: torch.Tensor
          contains the sampled electrons coordinate in the shape (batch_size,number_of_electrons*3)
  
      Returns:
      --------
      psi: torch.Tensor
          contains the wavefunction - 'psi' value. It is in the shape (batch_size), where each row corresponds to the solution of one of the batches
      """
      # creating one and two electron features
      eps = torch.tensor(1e-36)
      self.input = input.reshape((self.batch_size, -1, 3))
      two_electron_vector = self.input.unsqueeze(
          1
      ) - self.input.unsqueeze(2)
      two_electron_distance = torch.linalg.norm(
          two_electron_vector + eps, dim=3
      ).unsqueeze(3)
      two_electron = torch.cat(
          (two_electron_vector, two_electron_distance), dim=3
      )
      two_electron = torch.reshape(
          two_electron,
          (
              self.batch_size,
              self.total_electron,
              self.total_electron,
              -1,
          ),
      )
      one_electron_vector = self.input.unsqueeze(
          1
      ) - self.nucleon_pos.unsqueeze(1)
      one_electron_distance = torch.linalg.norm(
          one_electron_vector, dim=3
      )
      one_electron = torch.cat(
          (one_electron_vector, one_electron_distance.unsqueeze(-1)),
          dim=3,
      )
      one_electron = torch.reshape(
          one_electron.permute(0, 2, 1, 3),
          (self.batch_size, self.total_electron, -1),
      )
      one_electron_vector_permuted = one_electron_vector.permute(
          0, 2, 1, 3
      ).float()
  
      one_electron, _ = self.ferminet_layer[0].forward(
          one_electron.to(torch.float32), two_electron.to(torch.float32)
      )
      (
          self.psi,
          self.psi_up,
          self.psi_down,
      ) = self.ferminet_layer_envelope[0].forward(
          one_electron, one_electron_vector_permuted
      )
      return self.psi
  
imports: import logging
  from typing import List, Optional, Tuple
  from rdkit import Chem
  import numpy
  from deepchem.utils.molecule_feature_utils import ALLEN_ELECTRONEGATIVTY
  from deepchem.models.torch_models import TorchModel
  import torch
  from deepchem.models.torch_models.layers import FerminetElectronFeature, FerminetEnvelope
  from deepchem.utils.electron_sampler import ElectronSampler
label_desc: Use image node_id 2 for calling the Ferminet obj's underlying member method code with example usage: obj.forward(input) and returns: self