code: def error_calc(
      tensor, norm_tensor, weights, factors, sparsity, mask, mttkrp=None
  ):
      r"""Perform the error calculation. Different forms are used here depending upon
      the available information. If `mttkrp=None` or masking is being performed, then the
      full tensor must be constructed. Otherwise, the mttkrp is used to reduce the calculation cost.
  
      Parameters
      ----------
      tensor : tensor
      norm_tensor : float
          The l2 norm of tensor.
      weights : tensor
          The current CP weights
      factors : tensor
          The current CP factors
      sparsity : float or int
          Whether we allow for a sparse component
      mask : bool
          Whether masking is being performed.
      mttkrp : tensor or None
          The mttkrp product, if available.
  
      Returns
      -------
      unnorml_rec_error : float
          The unnormalized reconstruction error.
      tensor : tensor
          The tensor, in case it has been updated by masking.
      norm_tensor: float
          The tensor norm, in case it has been updated by masking.
      """
  
      # If we have to update the mask we already have to build the full tensor
      if (mask is not None) or (mttkrp is None):
          low_rank_component = cp_to_tensor((weights, factors))
  
          # Update the tensor based on the mask
          if mask is not None:
              tensor = tensor * mask + low_rank_component * (1 - mask)
              norm_tensor = tl.norm(tensor, 2)
  
          if sparsity:
              sparse_component = sparsify_tensor(
                  tensor - low_rank_component, sparsity
              )
          else:
              sparse_component = 0.0
  
          unnorml_rec_error = tl.norm(
              tensor - low_rank_component - sparse_component, 2
          )
      else:
          if sparsity:
              low_rank_component = cp_to_tensor((weights, factors))
              sparse_component = sparsify_tensor(
                  tensor - low_rank_component, sparsity
              )
  
              unnorml_rec_error = tl.norm(
                  tensor - low_rank_component - sparse_component, 2
              )
          else:
              # ||tensor - rec||^2 = ||tensor||^2 + ||rec||^2 - 2*<tensor, rec>
              factors_norm = cp_norm((weights, factors))
  
              # mttkrp and factor for the last mode. This is equivalent to the
              # inner product <tensor, factorization>
              iprod = tl.sum(
                  tl.sum(mttkrp * tl.conj(factors[-1]), axis=0)
              )
              unnorml_rec_error = tl.sqrt(
                  tl.abs(
                      norm_tensor**2 + factors_norm**2 - 2 * iprod
                  )
              )
  
      return unnorml_rec_error, tensor, norm_tensor
  
imports: import numpy
  import warnings
  import tensorly
  from ._base_decomposition import DecompositionMixin
  from ..random import random_cp
  from ..base import unfold
  from ..cp_tensor import cp_to_tensor, CPTensor, cp_norm, cp_normalize, validate_cp_rank
  from ..tenalg.svd import svd_interface
  from ..tenalg import unfolding_dot_khatri_rao
label_desc: Use image node_id 3 for calling a global function with example usage: error_calc(tensor, norm_tensor, weights, factors, sparsity, mask, mttkrp) and returns: unnorml_rec_error,  tensor,  norm_tensor