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) {

"""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"],

        return success();
      }
      // Insert tranposes to support scatter operations generated from
      // numpy-like slice operations:
      //   nd_array[:, [i,j]] = [i_values, j_values]
      //
      if (scatter_dims_to_operand_dims != inserted_window_dims) {
        // Support only dimension numbers generated by numpy-like slice
        // operations.
        return rewriter.notifyMatchFailure(

#### *Try your first TensorFlow program*

```shell
$ python
```

```python
>>> import tensorflow as tf
>>> tf.add(1, 2).numpy()
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
b'Hello, TensorFlow!'
```

For more examples, see the
[TensorFlow tutorials](https://www.tensorflow.org/tutorials/).

## Contribution guidelines

# limitations under the License.
# ==============================================================================
"""Tests for representative_dataset.py."""
import random

import numpy as np

from tensorflow.compiler.mlir.quantization.tensorflow.python import representative_dataset as repr_dataset
from tensorflow.core.protobuf import meta_graph_pb2
from tensorflow.python.client import session

          'with more than one signature key.'
      )


def _replace_tensors_by_numpy_ndarrays(
    repr_ds_map: rd.RepresentativeDatasetMapping,
) -> None:
  """Replaces tf.Tensors by their evaluated numpy arrays.

  This assumes that tf.Tensors in representative samples are created in the
  default Graph. It will raise an error if tensors are created in a different
  graph.

  Args:

#         "//tensorflow/python/util:compat",
#         "//tensorflow/tools/proto_splitter:constants",
#         "//tensorflow/tools/proto_splitter/python:saved_model",
#         "//third_party/py/numpy",
#         "@absl_py//absl:app",
#         "@absl_py//absl/flags",
#     ],
# )
#
# copybara:uncomment_end

# TODO(b/32673259): add a test to continuously validate these files.
filegroup(

    cryptography cycler Cython decorator google-api-python-client \
    google-auth google-auth-httplib2 grpcio httplib2 idna ipython-genutils \
    kiwisolver macholib matplotlib nose numpy packaging pandas pickleshare pip \
    prompt-toolkit protobuf psutil pyasn1 pyasn1-modules pycparser Pygments \
    pyparsing pyreadline python-dateutil pytz pywin32 requests rsa setuptools \

"""Defines CalibrationAlgorithm for calculating min and max values calculated by calibration method."""
import abc
import itertools
import logging

import numpy as np

from tensorflow.compiler.mlir.quantization.stablehlo import quantization_config_pb2 as stablehlo_quant_config_pb2