original_docstring: 
      This function takes a model as an input and returns the number of
      trainable parameters.
  
      Args:collect
          model (AtomisticModel): model for which you want to count
                                  the trainable parameters
  
      Returns:
          params (int): number of trainable parameters for the model
      
docstring: This function takes a model as an input and returns the number of
  trainable parameters.
  
  collect
  model (AtomisticModel): model for which you want to count
  the trainable parameters
docstring_tokens: This function takes a model as an input and returns the number of trainable parameters . collect model ( AtomisticModel ) : model for which you want to count the trainable parameters
code: def count_params(model):
      """
      This function takes a model as an input and returns the number of
      trainable parameters.
  
      Args:collect
          model (AtomisticModel): model for which you want to count
                                  the trainable parameters
  
      Returns:
          params (int): number of trainable parameters for the model
      """
      model_parameters = filter(lambda p: p.requires_grad, model.parameters())
      params = sum([np.prod(p.size()) for p in model_parameters])
      return params
code_tokens: def count_params ( model ) : model_parameters = filter ( lambda p : p . requires_grad , model . parameters ( ) ) params = sum ( [ np . prod ( p . size ( ) ) for p in model_parameters ] ) return params
short_docstring: This function takes a model as an input and returns the number of
  trainable parameters.
short_docstring_tokens: This function takes a model as an input and returns the number of trainable parameters .
comment: """
      This function takes a model as an input and returns the number of
      trainable parameters.
  
      Args:collect
          model (AtomisticModel): model for which you want to count
                                  the trainable parameters
  
      Returns:
          params (int): number of trainable parameters for the model
      """