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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: TileTensor[dtype, LayoutType, origin, address_space=address_space, linear_idx_type=linear_idx_type, element_shape_types=element_shape_types], kernel: TileTensor[dtype, LayoutType, origin, address_space=address_space, linear_idx_type=linear_idx_type, element_shape_types=element_shape_types], strides: TileTensor[strides_type, LayoutType, origin, address_space=address_space, linear_idx_type=linear_idx_type, element_shape_types=element_shape_types], dilations: TileTensor[dilations_type, LayoutType, origin, address_space=address_space, linear_idx_type=linear_idx_type, element_shape_types=element_shape_types], pads: TileTensor[pads_type, LayoutType, origin, address_space=address_space, linear_idx_type=linear_idx_type, element_shape_types=element_shape_types], output_pads: TileTensor[output_pads_type, LayoutType, origin, address_space=address_space, linear_idx_type=linear_idx_type, element_shape_types=element_shape_types]) -> IndexList[TileTensor[dtype, LayoutType, origin, address_space=address_space, linear_idx_type=linear_idx_type, element_shape_types=element_shape_types].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:

Returns:

IndexList: The output shape.

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