class_docstr: Class that provides function to separate/combine tensors and nontensors
  parameters.
  
  Examples
  --------
  >>> import torch
  >>> from deepchem.utils.pytorch_utils import TensorNonTensorSeparator
  >>> a = torch.tensor([1.,2,3])
  >>> b = 4.
  >>> c = torch.tensor([5.,6,7], requires_grad=True)
  >>> params = [a, b, c]
  >>> separator = TensorNonTensorSeparator(params)
  >>> tensor_params = separator.get_tensor_params()
  >>> tensor_params
  [tensor([5., 6., 7.], requires_grad=True)]
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docstr: Returns the number of nontensor parameters.
  
  Returns
  -------
  int
      The number of nontensor parameters.
docstr_tok: ["Returns","the","number","of","nontensor","parameters",".","Returns","--","--","--","-","int","The","number","of","nontensor","parameters","."]
code: def nnontensors(self):
      """Returns the number of nontensor parameters.
  
      Returns
      -------
      int
          The number of nontensor parameters.
  
      """
      return len(self.nontensor_idxs)
  
file_path: deepchem/deepchem/utils/pytorch_utils.py
imports: import scipy
  import torch
  from typing import Callable, Union, List, Generator, Tuple
label_desc: Use image node_id 4 for calling the TensorNonTensorSeparator obj's underlying member method code with example usage: obj.nnontensors() and returns: len