code: def testConcatLargeNumberOfTensors(self):
      if "CPU" in self.device:
          self.skipTest(
              "This test can time out on CPU, so we will just allow "
              "other backends to catch this specific error."
          )
      if (
          pywrap_sanitizers.is_asan_enabled()
          or pywrap_sanitizers.is_tsan_enabled()
          or pywrap_sanitizers.is_msan_enabled()
          or pywrap_sanitizers.is_ubsan_enabled()
      ):
          self.skipTest("This test can time out on *SAN.")
      with self.session():
          with self.test_scope():
              for concat_dim in range(2):
                  params = {}
                  p = []
                  shape = np.array([7, 13])
                  num_tensors = 1001
                  for i in np.arange(num_tensors):
                      input_shape = shape
                      placeholder = array_ops.placeholder(
                          dtypes.float32, shape=input_shape
                      )
                      p.append(placeholder)
                      params[placeholder] = np.random.rand(
                          *input_shape
                      ).astype(np.float32)
  
                  concat_inputs = p
                  c = array_ops.concat(concat_inputs, concat_dim)
                  result = c.eval(feed_dict=params)
  
                  self.assertEqual(result.shape, c.get_shape())
                  cur_offset = 0
  
                  for i in np.arange(num_tensors):
                      # The index into the result is the ':' along all dimensions
                      # except the concat_dim. slice(0, size) is used for ':', and
                      # a list of slices is used to index into result.
                      index = [
                          slice(0, params[p[i]].shape[j])
                          for j in np.arange(2)
                      ]
                      index[concat_dim] = slice(
                          cur_offset,
                          cur_offset + params[p[i]].shape[concat_dim],
                      )
                      cur_offset += params[p[i]].shape[concat_dim]
                      self.assertAllEqual(
                          result[tuple(index)], params[p[i]]
                      )
  
imports: import numpy
  from tensorflow.compiler.tests import xla_test
  from tensorflow.python import pywrap_sanitizers
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import gen_array_ops
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import math_ops
  from tensorflow.python.platform import googletest
label_desc: Use image node_id 18 for calling the ConcatTest obj's underlying member method code with example usage: obj.testConcatLargeNumberOfTensors() without return types