Zeroslike

Zeroslike is a function used in numerical computing to create a new array or tensor filled with zeros that matches the shape of a given input. The concept is widely implemented across libraries, where the function is typically named zeros_like, though terminology may vary. Its core purpose is to produce an output with the same dimensions as the input, while optionally controlling data type and device.

In NumPy, zeros_like(a, dtype=None, order='K', subok=True) returns an array of zeros with the same shape as a, inheriting a's dtype unless dtype is specified. In PyTorch, torch.zeros_like(input, dtype=None, device=None, layout=None, memory_format=None) returns a new tensor of zeros with the same size as input and optional dtype or device. TensorFlow provides tf.zeros_like(input, dtype=None, name=None) with similar behavior, returning a tensor of zeros matching input's shape. JAX offers jnp.zeros_like in its functional array library as well.

Common usage includes allocating an output array for in-place operations, initializing gradients, or setting up buffers that must conform to a given shape without manually repeating dimensions. Some libraries preserve layout or device characteristics, while others require explicit dtype or device arguments to override the input.

Related functions include ones_like, full_like, and zeros with an explicit shape. zeros_like differs from zeros in that zeros requires a shape argument, while zeros_like derives the shape from the input. Potential pitfalls include mismatched dtypes, which can lead to unintended casts, and issues when the input has a non-standard dtype or device that is not compatible with the target environment.