from anywhere, and executes the graphs it is sent without performing any checks.
Therefore, if you run a `tf.train.Server` in your network, anybody with access
to the network can execute arbitrary code with the privileges of the user
running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can

        return parser.getDocument();
    }

    private String getXmlString(final Node node) throws Exception {
        final TransformerFactory tf = TransformerFactory.newInstance();
        final javax.xml.transform.Transformer transformer = tf.newTransformer();
        transformer.setOutputProperty(OutputKeys.ENCODING, "UTF-8");
        transformer.setOutputProperty(OutputKeys.INDENT, "no");

  TF_DeleteTensor(t);
  EXPECT_EQ(2, product[0]);
  EXPECT_EQ(4, product[1]);
  EXPECT_EQ(6, product[2]);
  EXPECT_EQ(8, product[3]);

  // When we turn on grappler, confirm that the tf.Add has been rewritten into a
  // tf.Mul.
  // This capability of checking the executed op names is currently only enabled
  // for TFRT debug build, for performance and simplicity reasons.
  if (use_tfrt) {

                              &Deallocator, &deallocator_called);

  TF_Tensor* o = TF_TensorMaybeMove(t);
  ASSERT_TRUE(o == nullptr);  // It is unsafe to move memory TF might not own.
  TF_DeleteTensor(t);
  EXPECT_TRUE(deallocator_called);
}

TEST(CAPI, LibraryLoadFunctions) {
  // TODO(b/73318067): Fix linking for the GPU test generated by the

    TF_DeleteOutputList(func_outputs);
  }

  /**
   * We traced so far this function:
   *
   *   def two_adds_and_mm(A, B):
   *     my_add1 = A + B
   *     my_add2 = B + B
   *     mm = tf.MatMul(my_add1,my_add2)
   *     return my_add1, my_add2, mm
   *
   * Now we will execute this function with an eager context:
   *
   *   A =[[0, 1],[1, 0]]
   *   B =[[1, 0],[0, 1]]
   *

sc.kr
sc.leg.br
sc.ls
sc.tz
sc.ug
sc.us
scalebook.scw.cloud
scb
sch.ae
sch.id
sch.ir
sch.jo
sch.lk
sch.ly
sch.ng
sch.qa
sch.sa
sch.ss
sch.tf
sch.wf
sch.zm
schaeffler
schmidt
schokokeks.net
scholarships
school
school.ge
school.nz
school.za
schoolbus.jp
schools.nsw.edu.au
schule
schulplattform.de

sc.kr
sc.leg.br
sc.ls
sc.tz
sc.ug
sc.us
scalebook.scw.cloud
scb
sch.ae
sch.id
sch.ir
sch.jo
sch.lk
sch.ly
sch.ng
sch.qa
sch.sa
sch.ss
sch.tf
sch.wf
sch.zm
schaeffler
schmidt
schokokeks.net
scholarships
school
school.ge
school.nz
school.za
schoolbus.jp
schools.nsw.edu.au
schule
schulplattform.de

tc

// td : https://www.iana.org/domains/root/db/td.html
td

// tel: https://www.iana.org/domains/root/db/tel.html
// http://www.telnic.org/
tel

// tf : https://www.afnic.fr/wp-media/uploads/2022/12/afnic-naming-policy-2023-01-01.pdf
tf

// tg : https://www.iana.org/domains/root/db/tg.html
// http://www.nic.tg/
tg

// th : https://www.iana.org/domains/root/db/th.html

// This method is used to add a new input edge to 'dst', which must be a While
// op. The While op's "T" attribute must have already been updated to include
// the new edge. This is used to construct tf.while_loop gradients.
TF_CAPI_EXPORT extern void TF_AddWhileInputHack(TF_Graph* graph,
                                                TF_Output new_src,
                                                TF_Operation* dst,

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