# -*- coding: utf-8 -*-
from xml.etree import cElementTree
from xml.etree.cElementTree import ParseError

if hasattr(cElementTree, 'ParseError'):
    _ETREE_EXCEPTIONS = (ParseError, AttributeError, ValueError, TypeError)
else:
    _ETREE_EXCEPTIONS = (SyntaxError, AttributeError, ValueError, TypeError)

_STS_NS = {'sts': 'https://sts.amazonaws.com/doc/2011-06-15/'}


class STSElement(object):

# See b/196254385 for more details.
try:
  if _tf_uses_legacy_keras:
    importlib.import_module("tf_keras.src.optimizers")
  else:
    importlib.import_module("keras.src.optimizers")
except (ImportError, AttributeError):
  pass

del importlib

# Delete modules that should be hidden from dir().
# Don't fail if these modules are not available.
# For e.g. this file will be originally placed under tensorflow/_api/v1 which

                    value = received_body.getlist(field.alias)
                else:
                    try:
                        value = received_body.get(field.alias)
                    except AttributeError:
                        errors.append(get_missing_field_error(loc))
                        continue
            if (
                value is None

    name = self._ssa_name('cst')
    cst_ty = _get_type_from_proto(None, attr_def)
    try:
      cst_val = _get_val_from_proto(cst_ty, attr_def.default_value)
    except AttributeError:
      raise AttributeError(
          f'attribute "{attr_def.name}" does not have default_value. If the '
          "attribute names from the API and OpDef don't match, please add it "
          'to _ATTRIBUTE_RENAMES.')

    def get_mean_square_error(x, y):
      ret = tensorflow.reduce_mean(tensorflow.square(tensorflow.subtract(x, y)))
      try:
        ret = ret.numpy()
      except AttributeError:
        ret = ret.eval()
      return ret

    min_max_mse = get_mean_square_error(original_output, min_max_output)
    average_min_max_mse = get_mean_square_error(

*   `tf.keras.mixed_precision`
    *   When using mixed precision, calling `RMSprop.apply_gradients` or
        `Nadam.apply_gradients` outside a `tf.function` does not work and will
        raise the AttributeError "Tensor.op is meaningless when eager execution
        is enabled". See this
        [issue](https://github.com/tensorflow/tensorflow/issues/45536) for
        details and a workaround.