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When implementing tensor-based algorithms, avoid assumptions about fixed tensor shapes by not relying on direct shape attributes (.shape, .ndim), which may return None in graph mode. Instead, use TensorFlow operations like tf.shape() and tf.rank() which work in both eager and graph execution modes. For validation, use TensorFlow’s conditional operations rather than Python conditionals.

For example, replace:

# Problematic code - may fail in graph mode
mat1_shape = mat1.shape
dense_rows = mat1_shape[-2]  # This might fail if shape is not known
if dense_shape[-2] != dense_rows:  # Python conditional won't work with tensors
    raise ValueError("Shapes don't match")

With:

# Robust code that works in both eager and graph modes
mat1_shape = array_ops.shape(mat1)
dense_rows = mat1_shape[-2]
condition = math_ops.equal(dense_shape[-2], dense_rows)
gen_logging_ops._assert(condition, ["Shapes don't match"], name="Assert")

This approach ensures algorithms work reliably regardless of whether shapes are statically known at graph construction time or only determined during execution.