TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, 5, s);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s)) << TF_Message(s);

  // Try to set an invalid shape (cannot change 2x3 to a 2x5).
  dims[1] = 5;
  TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s)) << TF_Message(s);

  // Test for a scalar.
  TF_Operation* three = ScalarConst(3, graph, s);

  TF_Tensor* t = TFE_TensorHandleResolve(h, status.get());
  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(status.get()));
  ASSERT_EQ(t, nullptr);
  ASSERT_EQ("Invalid handle", string(TF_Message(status.get())));

  TF_SetStatus(status.get(), TF_OK, "");

  const char* device_name = TFE_TensorHandleDeviceName(h, status.get());
  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(status.get()));
  ASSERT_EQ(device_name, nullptr);

To set a different version, use the `TF_PYTHON_VERSION` environment variable,
e.g.

```
export TF_PYTHON_VERSION=3.11
```

To specify the version via a Bazel command argument, use the following:

```
--repo_env=TF_PYTHON_VERSION=3.11
```

## Requirements updater

Requirements updater is a standalone tool, intended to simplify process of

  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(status.get()));
  ASSERT_EQ(device_type, nullptr);
  ASSERT_EQ("Invalid handle", string(TF_Message(status.get())));

  TF_SetStatus(status.get(), TF_OK, "");

  int device_id = TFE_TensorHandleDeviceID(h, status.get());
  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(status.get()));
  ASSERT_EQ(device_id, -1);

# input the branch name and paste the cherry-pick commit and click Run. A PR
# will be created.

name: Release Branch Cherrypick
on:
  workflow_dispatch:
    inputs:
      # We use this instead of the "run on branch" argument because GitHub looks
      # on that branch for a workflow.yml file, and we'd have to cherry-pick
      # this file into those branches.
      release_branch:
        description: 'Release branch name (e.g. r2.9)'

  DefineFunction("MyGrad2", &grad_func2);

  // func cannot be null
  TF_GraphCopyFunction(host_graph_, nullptr, func_, s_);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s_));
  EXPECT_EQ(string("'func' argument to TF_GraphCopyFunction cannot be null"),
            string(TF_Message(s_)));

  // Cannot change gradient
  TF_GraphCopyFunction(host_graph_, func_, grad_func1, s_);

        } else {
          return TF_SetStatus(status, TF_INVALID_ARGUMENT, "Not a directory!");
        }
      };
  GcsFileStat stat;
  MaybeAppendSlash(&dir);
  gcs_file->stat_cache->LookupOrCompute(dir, &stat, compute_func, status);
  if (TF_GetCode(status) != TF_OK && TF_GetCode(status) != TF_INVALID_ARGUMENT)
    return false;
  if (TF_GetCode(status) == TF_INVALID_ARGUMENT) {
    TF_SetStatus(status, TF_OK, "");
    return false;

*   Extends `tf.keras.layers.Lambda` layers to support multi-argument lambdas,
    and keyword arguments when calling the layer.
*   Functional models now get constructed if *any* tensor in a layer call's
    arguments/keyword arguments comes from a keras input. Previously the
    functional api would only work if all of the elements in the first argument
    to the layer came from a keras input.

  parser.add_argument('build_log',
                      help='Path to a build log.')
  parser.add_argument('--xml-out-path',
                      required=False,
                      help='Path to which to output '
                           'the JUnit-based XML with ResultStore links.')
  parser.add_argument('--print',
                      action='store_true', dest='print', default=False,

 # endif
 #endif
 
+/* Undefine (also defined in libc-symbols.h).  */
+#undef __attribute_copy__
+#if __GNUC_PREREQ (9, 0)
+/* Copies attributes from the declaration or type referenced by
+   the argument.  */
+# define __attribute_copy__(arg) __attribute__ ((__copy__ (arg)))
+#else
+# define __attribute_copy__(arg)
+#endif
+
 #endif	 /* sys/cdefs.h */
diff --git a/stdlib/setenv.c b/stdlib/setenv.c