code: def test_tensor_train(
      *, data, svd, test_flags, backend_fw, fn_name, on_device
  ):
      input_dtype, x, rank = data
      results = helpers.test_function(
          backend_to_test=backend_fw,
          test_flags=test_flags,
          fn_name=fn_name,
          on_device=on_device,
          input_dtypes=input_dtype,
          input_tensor=x,
          rank=rank,
          svd=svd,
          test_values=False,
      )
  
      ret_np, ret_from_gt_np = results
  
      factors = helpers.flatten_and_to_np(
          ret=ret_np, backend=backend_fw
      )
      factors_gt = helpers.flatten_and_to_np(
          ret=ret_from_gt_np, backend=test_flags.ground_truth_backend
      )
  
      for f, f_gt in zip(factors, factors_gt):
          assert np.prod(f.shape) == np.prod(f_gt.shape)
  
imports: import math
  from hypothesis import strategies
  from hypothesis import assume
  import numpy
  import pytest
  import itertools
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_test, BackendHandler
  import ivy
label_desc: Use image node_id 51 for calling a global function with example usage: test_tensor_train() without return types