Mojo function
tile
tile[type: DType, type_repeats: DType](input: LayoutTensor[type, layout, origin, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment], repeats: LayoutTensor[type_repeats, layout, origin, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment], output: LayoutTensor[type, layout, origin, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment])
Implements the Tile operator from the ONNX spec. This behaves like Numpy tile, but without broadcast.
Parameters:
- type (
DType): Type of the input and output tensors. - type_repeats (
DType): Type of the repeats tensor.
Args:
- input (
LayoutTensor[type, layout, origin, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment]): The input tensor. Currently <= 4 dimensions are supported. - repeats (
LayoutTensor[type_repeats, layout, origin, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment]): One-dimensional tensor that specifies the number of repeated copies along each of the input's dimensions. Length equals input tensor rank. - output (
LayoutTensor[type, layout, origin, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment]): The output tensor. Has the same dimensions and type as input.
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