When handling tensor inputs that may contain invalid values, use transformational approaches rather than conditional checks. Conditional checks like if value < 0: may work with scalar values but fail with tensor objects during graph execution.
Instead of raising exceptions based on conditions:
# Not tensor-friendly - may fail during graph execution
if clip_norm < 0:
raise ValueError('clip_norm should be positive')
Use transformations to handle invalid values:
# Tensor-friendly approach
clip_norm = math_ops.maximum(clip_norm, 0) # Convert negative values to zero
For type validations, ensure you check all inputs comprehensively:
# Complete validation for both input and output types
if (not dtype.is_floating and not dtype.is_integer) or (not image.dtype.is_floating and not image.dtype.is_integer):
raise AttributeError('data type must be either floating point or integer')
When implementing such transformations, document the behavior clearly to inform users how edge cases are handled.
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