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

Args:

Returns:

IndexList: The output shape.

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