code: def transform_get_item_tensor(constraint, counter):
      """
      When the index is a tuple, then the output will be a tensor
      TODO: we have to check if this is the case for all HF models
  
      The cases we are covering here are a tuple with one of:
       - slice with default argument
       - None
  
       None appends 1 to the input tensor dimensions
       so each occurrence of 'None' increases the rank by 1
  
       slice with default arguments does not change the rank
      """
      assert isinstance(constraint.index_tuple, tuple)
  
      # generate a result tensor of the expected size
      dims, counter = gen_tensor_dims(constraint.tensor_size, counter)
      nat_constraints = gen_nat_constraints(dims)
  
      # generate a place-holder list of the right rank
      # where "slice" does not contribute to the rank and "None" does
      none_c = constraint.index_tuple.count(None)
      resulting_tensor_dims = (none_c + len(dims)) * [None]
  
      dim_index = 0
      for i in range(len(constraint.index_tuple)):
          # append 1 to the right location of the resulting tensor
          if constraint.index_tuple[i] is None:
              resulting_tensor_dims[i] = 1
  
          elif constraint.index_tuple[i] == slice(None, None, None):
              pass
  
          else:
              raise NotImplementedError("Method not yet implemented")
  
      # append the remaining dimensions to the right location
      dim_index = 0
      for i in range(len(resulting_tensor_dims)):
          if resulting_tensor_dims[i] is None:
              resulting_tensor_dims[i] = dims[dim_index]
              dim_index += 1
  
      # check if the index is valid
      is_valid_index = valid_index_tensor(constraint.index_tuple, dims)
  
      # check if the resulting tensor is within bounds
      if len(resulting_tensor_dims) > 4:
          return F(), counter
  
      else:
          constraints = [
              BinConstraintT(
                  constraint.input_var, TensorType(dims), op_eq
              ),
              BinConstraintT(
                  constraint.res,
                  TensorType(resulting_tensor_dims),
                  op_eq,
              ),
              *nat_constraints,
              is_valid_index,
          ]
          return Conj(constraints), counter
  
imports: import copy
  import itertools
  from torch.fx.experimental.migrate_gradual_types.constraint_generator import BinConstraintT, MAX_TENSOR_RANK
  from torch.fx.experimental.migrate_gradual_types.constraint import T, BinConstraintD, Conj, Constraint, DVar, TVar, Transpose
  from torch.fx.experimental.migrate_gradual_types.constraint import Disj, TGreatestUpperBound
  from torch.fx.experimental.migrate_gradual_types.constraint import DGreatestUpperBound
  from torch.fx.experimental.migrate_gradual_types.constraint import CalcConv, CalcMaxPool
  from torch.fx.experimental.migrate_gradual_types.constraint import CalcProduct, CanReshape
  from torch.fx.experimental.migrate_gradual_types.constraint import ApplyBroadcasting, Prod, F, GetItem, GetItemTensor, IndexSelect
  from torch.fx.experimental.migrate_gradual_types.operation import op_eq, op_precision, op_leq, op_matching
  from torch.fx.experimental.migrate_gradual_types.operation import op_consistency, op_neq
  from torch.fx.experimental.migrate_gradual_types.operation import op_mul, op_add, op_sub, op_div, op_mod
  from torch.fx.experimental.migrate_gradual_types.util import gen_tensor_dims, gen_nat_constraints, gen_dvar
  from torch.fx.tensor_type import TensorType, Dyn
  from typing import Callable, Dict, List
label_desc: Use image node_id 7 for calling a global function with example usage: transform_get_item_tensor(constraint, counter) and returns: F,  counter, Conj,  counter