`signature_keys` contains more than one signature key,
      `representative_datsaet` should be a mapping that maps each signature keys
      to the corresponding representative dataset.
    force_graph_mode_calibration: If set to true, it forces calibration in graph
      model instead of eager mode when the context is in eager mode.

  Raises:
    ValueError iff:

# For e.g. this file will be originally placed under tensorflow/_api/v1 which
# does not have "python", "core" directories. Then, it will be copied
# to tensorflow/ which does have these two directories.
try:
  del python
except NameError:
  pass
try:
  del core
except NameError:
  pass
try:
  del compiler
except NameError:
  pass

# For e.g. this file will be originally placed under tensorflow/_api/v1 which
# does not have "python", "core" directories. Then, it will be copied
# to tensorflow/ which does have these two directories.

try:
  del python
except NameError:
  pass
try:
  del core
except NameError:
  pass
try:
  del compiler
except NameError:
  pass

    raise ValueError(
        'Representative dataset is not a mapping (got: '
        f'{type(representative_dataset)}), but there is more than one '
        'signature key provided. Please provide a map of '
        '{signature_key -> dataset} with more than one signature key.'
    )
  else:
    representative_dataset_map = {
        list(signature_def_map.keys())[0]: representative_dataset,
    }

             'compilation to reduce the compilation time?'),
      'Eigen strong inline overridden.', 'Not overriding eigen strong inline, '
      'some compilations could take more than 20 mins.'):
    # Due to a known MSVC compiler issue
    # https://github.com/tensorflow/tensorflow/issues/10521
    # Overriding eigen strong inline speeds up the compiling of

from tensorflow.core.framework import attr_value_pb2
from tensorflow.core.framework import graph_pb2
from tensorflow.core.framework import node_def_pb2
from tensorflow.core.framework import tensor_pb2
from tensorflow.core.framework import tensor_shape_pb2
from tensorflow.core.framework import types_pb2
from tensorflow.python.client import session

_losses = _KerasLazyLoader(
    globals(), submodule="losses", name="losses", mode="v2")
_metrics = _KerasLazyLoader(
    globals(), submodule="metrics", name="metrics", mode="v2")
_optimizers = _KerasLazyLoader(
    globals(), submodule="optimizers", name="optimizers", mode="v2")
_initializers = _KerasLazyLoader(
    globals(), submodule="initializers", name="initializers", mode="v2")
setattr(_current_module, "losses", _losses)

                  name='y', shape=y_signature, dtype=dtypes.float32
              ),
          ]
      )
      def einsum_without_kernel(
          self, x: core.Tensor, y: core.Tensor
      ) -> Mapping[str, core.Tensor]:
        return self._einsum(x, y)

      def _einsum(self, x, y):

        out = tensorflow.einsum(equation, x, y)
        if self._bias is not None:

from tensorflow.compiler.mlir.quantization.tensorflow.python import py_function_lib
from tensorflow.compiler.mlir.quantization.tensorflow.python import save_model
from tensorflow.core.protobuf import meta_graph_pb2

# Mapping of signature def key -> SignatureDef.
_SignatureDefMap = Mapping[str, meta_graph_pb2.SignatureDef]


def _serialize_signature_def_map(
    signature_def_map: _SignatureDefMap,

import tensorflow  # pylint: disable=unused-import

from tensorflow.compiler.mlir.quantization.tensorflow.python import representative_dataset as repr_dataset
from tensorflow.core.framework import function_pb2
from tensorflow.core.framework import graph_pb2
from tensorflow.core.framework import node_def_pb2
from tensorflow.python.client import session
from tensorflow.python.eager import def_function
from tensorflow.python.framework import dtypes