keras-team/keras · error · ValueError

`inputs` should be a list. Received: inputs={inputs}

Error message

`inputs` should be a list. Received: inputs={inputs}

What it means

compute_mask() of merge layers validates that inputs is a list or tuple. If a single tensor (or other non-sequence) is passed alongside a mask, this error is raised before mask combination logic runs.

Source

Thrown at keras/src/layers/merging/base_merge.py:261

            output_shape = (list(batch_sizes)[0],) + output_shape
        else:
            output_shape = (None,) + output_shape
        return output_shape

    def compute_output_spec(self, inputs):
        output_shape = self.compute_output_shape([x.shape for x in inputs])
        output_sparse = all(x.sparse for x in inputs)
        return KerasTensor(
            output_shape, dtype=self.compute_dtype, sparse=output_sparse
        )

    def compute_mask(self, inputs, mask=None):
        if mask is None:
            return None
        if not isinstance(mask, (tuple, list)):
            raise ValueError(f"`mask` should be a list. Received: mask={mask}")
        if not isinstance(inputs, (tuple, list)):
            raise ValueError(
                f"`inputs` should be a list. Received: inputs={inputs}"
            )
        if len(mask) != len(inputs):
            raise ValueError(
                "The lists `inputs` and `mask` should have the same length. "
                f"Received: inputs={inputs} of length {len(inputs)}, and "
                f"mask={mask} of length {len(mask)}"
            )
        # Default implementation does an OR between the masks, which works
        # for `Add`, `Subtract`, `Average`, `Maximum`, `Minimum`, `Multiply`.
        if any(m is None for m in mask):
            return None
        output_mask = mask[0]
        for m in mask[1:]:
            output_mask = ops.logical_or(output_mask, m)
        return output_mask

    def get_config(self):

View on GitHub (pinned to 7a34a03db6)

Solutions

1. Pass inputs as a list matching the layer's input structure
2. Keep compute_mask call sites consistent with how __call__/build see inputs
3. Add input-structure assertions in custom merge subclasses

Example fix

# before
mask_out = merge_layer.compute_mask(x, mask=[m1, m2])

# after
mask_out = merge_layer.compute_mask([x1, x2], mask=[m1, m2])

assert isinstance(inputs, (list, tuple)), 'inputs must be a list when a mask is provided'

def is_input_list(x) -> bool:
    return isinstance(x, (list, tuple))

When it happens

Trigger: Calling compute_mask(x, mask=[m1, m2]) with a bare tensor x; custom subclasses invoking super().compute_mask with unwrapped inputs.

Common situations: Custom merge subclasses that forward masks; test code calling compute_mask directly; model surgery that changes input arity.

Related errors

• A merge layer should be called on a list of inputs. Received
• A merge layer should be called on a list of inputs. Received
• `mask` should be a list. Received: mask={mask}
• The lists `inputs` and `mask` should have the same length. R
• `inputs` should be a list. Received: inputs={inputs}

AI-assisted analysis of keras-team/keras@7a34a03db6 (2026-08-25). Data as JSON: /api/errors/0843c7790366509a. Report an issue: GitHub.