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Mojo function

argmin

def argmin(input: TileTensor[Storage=input.Storage, address_space=input.address_space, linear_idx_type=input.linear_idx_type, element_size=input.element_size], axis: Int, output: TileTensor[Storage=output.Storage, address_space=output.address_space, linear_idx_type=output.linear_idx_type, element_size=output.element_size], ctx: Optional[DeviceContext] = None)

Finds the indices of the minimum element along the specified axis.

Args:

input (TileTensor[Storage=input.Storage, address_space=input.address_space, linear_idx_type=input.linear_idx_type, element_size=input.element_size]): The input tensor.
axis (Int): The axis.
output (TileTensor[Storage=output.Storage, address_space=output.address_space, linear_idx_type=output.linear_idx_type, element_size=output.element_size]): The output tensor.
ctx (Optional[DeviceContext]): The context to execute the work on.

def argmin(input: TileTensor[Storage=input.Storage, address_space=input.address_space, linear_idx_type=input.linear_idx_type, element_size=input.element_size], axis_buf: TileTensor[Storage=axis_buf.Storage, address_space=axis_buf.address_space, linear_idx_type=axis_buf.linear_idx_type, element_size=axis_buf.element_size], output: TileTensor[Storage=output.Storage, address_space=output.address_space, linear_idx_type=output.linear_idx_type, element_size=output.element_size], ctx: Optional[DeviceContext] = None) where (Int(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(axis_buf.LayoutType.__shape_types), [idx: __mlir_type.index] axis_buf.LayoutType.__shape_types[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(axis_buf.LayoutType.__shape_types), [idx: __mlir_type.index] axis_buf.LayoutType.__shape_types[idx]._ParamListType))[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(axis_buf.LayoutType.__shape_types), [idx: __mlir_type.index] axis_buf.LayoutType.__shape_types[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(axis_buf.LayoutType.__shape_types), [idx: __mlir_type.index] axis_buf.LayoutType.__shape_types[idx]._ParamListType))[idx]._ParamListType))[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(axis_buf.LayoutType.__shape_types), [idx: __mlir_type.index] axis_buf.LayoutType.__shape_types[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(axis_buf.LayoutType.__shape_types), [idx: __mlir_type.index] axis_buf.LayoutType.__shape_types[idx]._ParamListType))[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(#kgen.param_list.concat(#kgen.param_list.tabulate(len(axis_buf.LayoutType.__shape_types), [idx: __mlir_type.index] axis_buf.LayoutType.__shape_types[idx]._ParamListType))), [idx: __mlir_type.index] #kgen.param_list.concat(#kgen.param_list.tabulate(len(axis_buf.LayoutType.__shape_types), [idx: __mlir_type.index] axis_buf.LayoutType.__shape_types[idx]._ParamListType))[idx]._ParamListType))[idx]._ParamListType))[idx]._ParamListType)))) == Int(1))

Finds the indices of the minimum element along the specified axis.

Args:

input (TileTensor[Storage=input.Storage, address_space=input.address_space, linear_idx_type=input.linear_idx_type, element_size=input.element_size]): The input tensor.
axis_buf (TileTensor[Storage=axis_buf.Storage, address_space=axis_buf.address_space, linear_idx_type=axis_buf.linear_idx_type, element_size=axis_buf.element_size]): The axis tensor.
output (TileTensor[Storage=output.Storage, address_space=output.address_space, linear_idx_type=output.linear_idx_type, element_size=output.element_size]): The axis tensor.
ctx (Optional[DeviceContext]): The context to execute the work on.