Mojo function

conv_transpose_shape

conv_transpose_shape[dtype: DType, strides_type: DType, dilations_type: DType, pads_type: DType, output_pads_type: DType, single_thread_blocking_override: Bool](input: LayoutTensor[dtype, layout, origin, address_space=address_space, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment], kernel: LayoutTensor[dtype, layout, origin, address_space=address_space, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment], strides: LayoutTensor[strides_type, layout, origin, address_space=address_space, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment], dilations: LayoutTensor[dilations_type, layout, origin, address_space=address_space, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment], pads: LayoutTensor[pads_type, layout, origin, address_space=address_space, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment], output_pads: LayoutTensor[output_pads_type, layout, origin, address_space=address_space, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment]) -> IndexList[layout.rank()]

Compute the output shape of a conv-transpose operation, and assert the inputs are compatible.

Parameters:

dtype (DType): Element type of the input and kernel tensor.
strides_type (DType): Element type of the strides tensor.
dilations_type (DType): Element type of the dilations tensor.
pads_type (DType): Element type of the pads tensor.
output_pads_type (DType): Element type of the output_pads tensor.
single_thread_blocking_override (Bool): If True, then the operation is run synchronously using a single thread.

Args:

input (LayoutTensor[dtype, layout, origin, address_space=address_space, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment]): The input tensor.
kernel (LayoutTensor[dtype, layout, origin, address_space=address_space, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment]): The kernel tensor.
strides (LayoutTensor[strides_type, layout, origin, address_space=address_space, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment]): The strides tensor.
dilations (LayoutTensor[dilations_type, layout, origin, address_space=address_space, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment]): The dilations tensor.
pads (LayoutTensor[pads_type, layout, origin, address_space=address_space, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment]): The paddings tensor.
output_pads (LayoutTensor[output_pads_type, layout, origin, address_space=address_space, element_layout=element_layout, layout_int_type=layout_int_type, linear_idx_type=linear_idx_type, masked=masked, alignment=alignment]): The output paddings tensor.

Returns:

The output shape.