t3 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
    sq1 = add([t1])
    sq2 = add([t1, t2])
    sq3 = add([t1, t2, t3])
    self.assertAllEqual(sq1.numpy().reshape(-1), [1, 2, 3, 4])
    self.assertAllEqual(sq2.numpy().reshape(-1), [2, 4, 6, 8])
    self.assertAllEqual(sq3.numpy().reshape(-1), [3, 6, 9, 12])

  def testBiasedDense(self):
    biased_dense = def_function.function(gen_composite_ops.my_biased_dense)

setuptools
wheel
cloudpickle
colorama>=0.4.4
matplotlib
pillow>=9.1.0
rich
absl-py
portpicker
six
opt-einsum
auditwheel
typing_extensions
importlib_metadata>=4.6
numpy==1.26.0;python_version=="3.12"
numpy==1.23.4;python_version=="3.11"
numpy==1.22.4;python_version<"3.11"
scipy==1.11.2;python_version=="3.12"
scipy==1.9.2;python_version=="3.11"
scipy==1.7.3;python_version<"3.11"
ml_dtypes>=0.2.0

NUMPY_ANNOTATIONS = {
    "numpy": package_annotation(
        additive_build_content = """\
filegroup(
    name = "includes",
    srcs = glob(["site-packages/numpy/core/include/**/*.h"]),
)
cc_library(
    name = "numpy_headers",
    hdrs = [":includes"],
    strip_include_prefix="site-packages/numpy/core/include/",
)
""",
    ),
}

pip_parse(
    name = "pypi",

    sq2 = gen_composite_ops.my_add_n([t1, t2])
    sq3 = gen_composite_ops.my_add_n([t1, t2, t3])
    self.assertAllEqual(sq1.numpy().reshape(-1), [1, 2, 3, 4])
    self.assertAllEqual(sq2.numpy().reshape(-1), [2, 4, 6, 8])
    self.assertAllEqual(sq3.numpy().reshape(-1), [3, 6, 9, 12])

  def testBiasedDense(self):
    t1 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])

# TODO(b/304751256): Adjust the numpy pin to a single version, when ready
numpy ~= 1.23.5 ; python_version <= "3.11"
numpy ~= 1.26.0 ; python_version >= "3.12"
wheel ~= 0.41.2
h5py >= 3.10.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.4.0, < 0.5.0

# Install tensorboard, and keras

namespace tensorflow {
namespace ops {

// Op: Mul()
// Summary: Returns x * y element-wise.
//
// Description:
//   *NOTE*: `Multiply` supports broadcasting. More about broadcasting
//   [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
Status Mul(AbstractContext* ctx, AbstractTensorHandle* const x,
           AbstractTensorHandle* const y, AbstractTensorHandle** z,
           const char* name, const char* raw_device_name) {

      "metadata": {
        "id": "c9JX9RJTPaoW"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.environ['KERAS_BACKEND'] = 'jax'\n",
        "import jax.numpy as jnp\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from keras_core.applications import ResNet50\n",
        "from jax.experimental import jax2tf"
      ]
    },
    {

"""Defines types required for representative datasets for quantization."""

from collections.abc import Collection, Sized
import os
from typing import Iterable, Mapping, Optional, Union

import numpy as np

from tensorflow.compiler.mlir.quantization.tensorflow import quantization_options_pb2
from tensorflow.core.protobuf import meta_graph_pb2
from tensorflow.python.client import session

        "//tensorflow/python/saved_model:tag_constants",
        "//tensorflow/python/training:checkpoint_utils",
        "//tensorflow/python/types:core",
        "//third_party/py/numpy",
        "@absl_py//absl/testing:parameterized",
    ],
)

pytype_strict_library(
    name = "quantize_model_test_base",
    testonly = 1,
    srcs = ["integration_test/quantize_model_test_base.py"],

// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package rand

import (
	"errors"
	"internal/byteorder"
	"math/bits"
)

// https://numpy.org/devdocs/reference/random/upgrading-pcg64.html
// https://github.com/imneme/pcg-cpp/commit/871d0494ee9c9a7b7c43f753e3d8ca47c26f8005

// A PCG is a PCG generator with 128 bits of internal state.