--lockfile=requirements.txt:./requirements_lock_3_12.txt
        --lockfile=requirements.txt:./ci/official/containers/linux_arm64/devel.requirements.txt
        --lockfile=requirements.txt:./ci/official/containers/linux_arm64/jax.requirements.txt

#include "tensorflow/core/platform/types.h"
#include "tensorflow/lite/toco/model_flags.pb.h"
#include "tensorflow/lite/toco/toco_flags.pb.h"

namespace tensorflow {

// Converts the given Jax model to a TF Lite FlatBuffer
// string according to the given model flags, toco flags and tags. Returns error
// status if it fails to convert the input.
Status ConvertJaxToTFLiteFlatBuffer(const std::string& input,

        }
        M2_ENTITIES_RESOURCE = bytes;

        // Set the context classloader the bootstrap classloader, to work around the way that JAXP locates implementation classes
        // This should ensure that the JAXP classes provided by the JVM are used, rather than some other implementation
        ClassLoader original = Thread.currentThread().getContextClassLoader();

      return %0 : tensor<1x1x4xf32>
    }
  )mlir";

  const OwningOpRef<ModuleOp> module_op =

            try {
                // If not set for >= JAXP 1.5 / Java8 won't allow referencing DTDs, e.g.
                // using http URLs, because Groovy's XmlParser requests FEATURE_SECURE_PROCESSING
                parser.setProperty(ACCESS_EXTERNAL_DTD, ALLOW_ANY_EXTERNAL_DTD);
            } catch (SAXNotRecognizedException ignore) {
                // property requires >= JAXP 1.5 / Java8
            }
            return parser;

reachable and exploitable through production-grade, benign models.

### Compilation

Compiling models via the recommended entry points described in
[XLA](https://www.tensorflow.org/xla) and
[JAX](https://jax.readthedocs.io/en/latest/jax-101/02-jitting.html)
documentation should be safe, while some of the testing and debugging tools that
come with the compiler are not designed to be used with untrusted data and

      builder.getNamedAttr("inputs", builder.getStringAttr(inputs)));
  // Jax wrapped the output nodes in a tuple, so it's pretty hard to us
  // to tell the output at this point, we will set the output at the export
  // phase.
  main_func->setAttr("tf.entry_function", builder.getDictionaryAttr(attrs));

  // StableHLO Quantizer is not supported for JAX input models, so
  // quantization_py_function_lib is set to nullptr.

    assignees.push(
        'DEKHTIARJonathan', 'meena-at-work', 'nluehr', 'pjannaty', 'poulsbo');
  } else if (
      lowercased_title.includes('nvidia') && domain.includes('nvidia.com')) {
    if (lowercased_title.includes('jax')) {
      assignees.push('hawkinsp', 'yashk2810', 'skye');
    }
    if (lowercased_title.includes('xla') || lowercased_title.includes('gpu')) {
      assignees.push('cheshire', 'gcforster', 'reedwm', 'chsigg', 'xla-rotation');

    %output_0, %min_1, %max_2, %histogram_3 = "tf.CustomAggregator"(%0) <{calibration_method = 1 : i32,...

	assert(t, AUB, aub4, "aub4")
	if A.Find(3) != aub4.Find(3) {
		t.Errorf("Failed aliasing/reuse check, A/aub4")
	}

	axb1 := A.Union(B, alwaysNil)
	assert(t, AXB, axb1, "axb1")
	axb2 := B.Union(A, alwaysNil)
	assert(t, AXB, axb2, "axb2")

	adb := A.Difference(B, alwaysNil)
	assert(t, ADB, adb, "adb")
	bda := B.Difference(A, nil)
	assert(t, BDA, bda, "bda")

	Ap1 := treePlus1([]int32{1, 2, 3, 4})