)

      def has_reshape(self) -> bool:
        return self.bias_fn() and self.bias_size != self.filters.shape[-1]

      @def_function.function
      def matmul(self, input_tensor: core.Tensor) -> Mapping[str, core.Tensor]:
        """Performs a matrix multiplication.

        Depending on self.bias_fn and self.activation_fn, it may add a bias
        term or go through the activaction function.

    """
    for sample_1, sample_2 in zip(repr_ds_1, repr_ds_2):
      self.assertCountEqual(sample_1.keys(), sample_2.keys())

      for input_key in sample_1:
        self._assert_tensorlike_all_close(
            sess, sample_1[input_key], sample_2[input_key]
        )

  def test_not_implemented_saver(self):
    with self.assertRaisesRegex(
        NotImplementedError, '"save" is not implemented.'
    ):

    }

    @InterceptGroovyCalls
    @InstanceMethod
    @WithExtensionReferences(toClass = ResourceGroovyMethods.class)
    public static void intercept_eachByte(
        @Receiver File self,
        int bufferLen,
        Closure<?> closure,
        @CallerClassName String consumer
    ) throws IOException {
        Instrumented.fileOpened(self, consumer);
        ResourceGroovyMethods.eachByte(self, bufferLen, closure);

    # So hist_mids[i] = (lower_bound + bin_width / 2) + bin_width * i
    first_mid = self._lower_bound + self._bin_width / 2
    last_mid = first_mid + (self._num_bins - 1) * self._bin_width
    self._hist_mids = np.linspace(first_mid, last_mid, self._num_bins)

  def _get_dequantized_hist_mids_after_quantize(
      self, quant_min: float, quant_max: float
  ) -> np.ndarray:

                public static int access_get_getMaxErrors(Task self) {
                    ${getDefaultDeprecation("Task", "maxErrors")}
                    return self.getMaxErrors().getOrElse(0);
                }

                public static void access_set_setMaxErrors(Task self, int arg0) {
                    ${getDefaultDeprecation("Task", "maxErrors")}
                    self.getMaxErrors().set(arg0);
                }
            }

        return self.modify(providerToDependency);
    }

    /**
     * Modifies a dependency.
     *
     * @param dependency dependency to modify
     * @return the modified dependency
     * @since 8.0
     */
    public static <D extends ModuleDependency> D call(DependencyModifier self, D dependency) {

                """
            ),
        ] = True,
    ):
        self.model = HTTPBaseModel(scheme="basic", description=description)
        self.scheme_name = scheme_name or self.__class__.__name__
        self.realm = realm
        self.auto_error = auto_error

    async def __call__(  # type: ignore
        self, request: Request
    ) -> Optional[HTTPBasicCredentials]:

class Depends:
    def __init__(
        self, dependency: Optional[Callable[..., Any]] = None, *, use_cache: bool = True
    ):
        self.dependency = dependency
        self.use_cache = use_cache

    def __repr__(self) -> str:
        attr = getattr(self.dependency, "__name__", type(self.dependency).__name__)
        cache = "" if self.use_cache else ", use_cache=False"
        return f"{self.__class__.__name__}({attr}{cache})"

                auth.
                """
            ),
        ] = None,
    ):
        self.grant_type = grant_type
        self.username = username
        self.password = password
        self.scopes = scope.split()
        self.client_id = client_id
        self.client_secret = client_secret


class OAuth2PasswordRequestFormStrict(OAuth2PasswordRequestForm):
    """

        validate that the sample has same set of input keys before saving.

    Raises:
      KeyError: If path_map and expected_input_key_map have different keys.
    """
    self.path_map: Mapping[str, os.PathLike[str]] = path_map
    self.expected_input_key_map: Mapping[str, Collection[str]] = {}
    if expected_input_key_map is not None:
      if set(path_map.keys()) != set(expected_input_key_map.keys()):
        raise KeyError(