keras-team/keras · error · ValueError

{error_preamble} For layer '{class_name}', received `{method

Error message

{error_preamble} For layer '{class_name}', received `{method_name}()` argument `{name}`, but `call()` does not have argument `{expected_call_arg}`.

What it means

Keras maps each <name>_shape parameter of build/compute_output_shape to a call() argument named <name> by stripping the _shape suffix. This error fires when the stripped name has no matching parameter in the layer's call() signature, so the shape value has nowhere to be applied.

Source

Thrown at keras/src/layers/layer.py:2063

    # Multiple args: check that all names line up.
    kwargs = {}
    for name in expected_names:
        method_name = target_fn.__name__
        error_preamble = (
            f"For a `{method_name}()` method with more than one argument, all "
            "arguments should have a `_shape` suffix and match an argument "
            f"from `call()`. E.g. `{method_name}(self, foo_shape, bar_shape)` "
        )
        if not name.endswith("_shape"):
            raise ValueError(
                f"{error_preamble} For layer '{class_name}', "
                f"Received `{method_name}()` argument "
                f"`{name}`, which does not end in `_shape`."
            )
        expected_call_arg = utils.removesuffix(name, "_shape")
        if expected_call_arg not in call_spec.arguments_dict:
            raise ValueError(
                f"{error_preamble} For layer '{class_name}', "
                f"received `{method_name}()` argument "
                f"`{name}`, but `call()` does not have argument "
                f"`{expected_call_arg}`."
            )
        if name in shapes_dict:
            kwargs[name] = shapes_dict[name]

    return kwargs


class CallContext:
    def __init__(self, entry_layer):
        self.entry_layer = entry_layer

    def get_value(self, arg_name, default=None):
        """Get the context value for `arg_name`, or `default` if unset."""
        return getattr(self, arg_name, default)

View on GitHub (pinned to 7a34a03db6)

Solutions

1. Add the matching argument to call(): def call(self, inputs, aux) so aux_shape maps to aux
2. Remove the orphaned *_shape parameter from build/compute_output_shape
3. Keep the two signatures in sync: for every extra call() argument, accept <arg>_shape in build/compute_output_shape

Example fix

# before
class MyLayer(keras.layers.Layer):
    def call(self, inputs):
        ...
    def compute_output_shape(self, input_shape, aux_shape):
        ...  # ValueError

# after
class MyLayer(keras.layers.Layer):
    def call(self, inputs, aux=None):
        ...
    def compute_output_shape(self, input_shape, aux_shape=None):
        ...

import inspect
call_args = set(inspect.signature(MyLayer.call).parameters) - {'self'}
shape_args = {p.removesuffix('_shape') for p in inspect.signature(MyLayer.compute_output_shape).parameters if p != 'self'}
assert shape_args <= call_args, shape_args - call_args

When it happens

Trigger: Defining compute_output_shape(self, input_shape, aux_shape) when call(self, inputs) has no aux parameter; renaming a call() argument without updating compute_output_shape; leftover *_shape params after refactoring.

Common situations: Copy-pasting a compute_output_shape override from another layer whose call() signature differs; version upgrades where call() signatures changed; stale overrides after deleting a call() argument.

Related errors

• {error_preamble} For layer '{class_name}', Received `{method
• A `Concatenate` layer should be called on a list of inputs.
• Unknown activation function '{activation}' cannot be seriali
• Could not interpret activation function identifier: {identif
• ConvNeXt does not support the `channels_first` image data fo

AI-assisted analysis of keras-team/keras@7a34a03db6 (2026-08-25). Data as JSON: /api/errors/4af21681b918ce46. Report an issue: GitHub.