# File content is auto-generated. Do not modify. # pylint: skip-file from ._internal import NDArrayBase from ..base import _Null def _CachedOp(*data, **kwargs): r""" Parameters ---------- data : NDArray[] input data list out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _CrossDeviceCopy(out=None, name=None, **kwargs): r"""Special op to copy data cross device Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _CustomFunction(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _Div(lhs=None, rhs=None, out=None, name=None, **kwargs): r"""Divides arguments element-wise. The storage type of ``elemwise_div`` output is always dense Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _DivScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r"""Divide an array with a scalar. ``_div_scalar`` only operates on data array of input if input is sparse. For example, if input of shape (100, 100) has only 2 non zero elements, i.e. input.data = [5, 6], scalar = nan, it will result output.data = [nan, nan] instead of 10000 nans. Defined in src/operator/tensor/elemwise_binary_scalar_op_basic.cc:L171 Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _Equal(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _EqualScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _Greater(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _GreaterEqualScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _GreaterScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _Greater_Equal(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _Hypot(lhs=None, rhs=None, out=None, name=None, **kwargs): r"""Given the "legs" of a right triangle, return its hypotenuse. Defined in src/operator/tensor/elemwise_binary_op_extended.cc:L79 Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _HypotScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _Lesser(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _LesserEqualScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _LesserScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _Lesser_Equal(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _LogicalAndScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _LogicalOrScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _LogicalXorScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _Logical_And(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _Logical_Or(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _Logical_Xor(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _Maximum(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _MaximumScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _Minimum(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _MinimumScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _Minus(lhs=None, rhs=None, out=None, name=None, **kwargs): r"""Subtracts arguments element-wise. The storage type of ``elemwise_sub`` output depends on storage types of inputs - elemwise_sub(row_sparse, row_sparse) = row_sparse - elemwise_sub(csr, csr) = csr - elemwise_sub(default, csr) = default - elemwise_sub(csr, default) = default - elemwise_sub(default, rsp) = default - elemwise_sub(rsp, default) = default - otherwise, ``elemwise_sub`` generates output with default storage Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _MinusScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _Mod(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _ModScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _Mul(lhs=None, rhs=None, out=None, name=None, **kwargs): r"""Multiplies arguments element-wise. The storage type of ``elemwise_mul`` output depends on storage types of inputs - elemwise_mul(default, default) = default - elemwise_mul(row_sparse, row_sparse) = row_sparse - elemwise_mul(default, row_sparse) = row_sparse - elemwise_mul(row_sparse, default) = row_sparse - elemwise_mul(csr, csr) = csr - otherwise, ``elemwise_mul`` generates output with default storage Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _MulScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r"""Multiply an array with a scalar. ``_mul_scalar`` only operates on data array of input if input is sparse. For example, if input of shape (100, 100) has only 2 non zero elements, i.e. input.data = [5, 6], scalar = nan, it will result output.data = [nan, nan] instead of 10000 nans. Defined in src/operator/tensor/elemwise_binary_scalar_op_basic.cc:L149 Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _NDArray(*data, **kwargs): r"""Stub for implementing an operator implemented in native frontend language with ndarray. Parameters ---------- data : NDArray[] Input data for the custom operator. info : ptr, required out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _Native(*data, **kwargs): r"""Stub for implementing an operator implemented in native frontend language. Parameters ---------- data : NDArray[] Input data for the custom operator. info : ptr, required need_top_grad : boolean, optional, default=1 Whether this layer needs out grad for backward. Should be false for loss layers. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _NoGradient(out=None, name=None, **kwargs): r"""Place holder for variable who cannot perform gradient Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _NotEqualScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _Not_Equal(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _Plus(lhs=None, rhs=None, out=None, name=None, **kwargs): r"""Adds arguments element-wise. The storage type of ``elemwise_add`` output depends on storage types of inputs - elemwise_add(row_sparse, row_sparse) = row_sparse - elemwise_add(csr, csr) = csr - elemwise_add(default, csr) = default - elemwise_add(csr, default) = default - elemwise_add(default, rsp) = default - elemwise_add(rsp, default) = default - otherwise, ``elemwise_add`` generates output with default storage Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _PlusScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _Power(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _PowerScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _RDivScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _RMinusScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _RModScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _RPowerScalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _add(lhs=None, rhs=None, out=None, name=None, **kwargs): r"""Adds arguments element-wise. The storage type of ``elemwise_add`` output depends on storage types of inputs - elemwise_add(row_sparse, row_sparse) = row_sparse - elemwise_add(csr, csr) = csr - elemwise_add(default, csr) = default - elemwise_add(csr, default) = default - elemwise_add(default, rsp) = default - elemwise_add(rsp, default) = default - otherwise, ``elemwise_add`` generates output with default storage Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _arange(start=_Null, stop=_Null, step=_Null, repeat=_Null, infer_range=_Null, ctx=_Null, dtype=_Null, out=None, name=None, **kwargs): r"""Return evenly spaced values within a given interval. Similar to Numpy Parameters ---------- start : double, required Start of interval. The interval includes this value. The default start value is 0. stop : double or None, optional, default=None End of interval. The interval does not include this value, except in some cases where step is not an integer and floating point round-off affects the length of out. step : double, optional, default=1 Spacing between values. repeat : int, optional, default='1' The repeating time of all elements. E.g repeat=3, the element a will be repeated three times --> a, a, a. infer_range : boolean, optional, default=0 Whether to infer the stop position from the start, step, repeat, and output tensorsize. ctx : string, optional, default='' Context of output, in format [cpu|gpu|cpu_pinned](n).Only used for imperative calls. dtype : {'float16', 'float32', 'float64', 'int32', 'int64', 'int8', 'uint8'},optional, default='float32' Target data type. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_Activation(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_BatchNorm(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_BatchNorm_v1(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_BilinearSampler(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_CachedOp(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_Concat(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_Convolution(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_Convolution_v1(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_Correlation(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_Crop(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_Custom(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_CustomFunction(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_Deconvolution(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_Dropout(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_Embedding(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_FullyConnected(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_GridGenerator(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_IdentityAttachKLSparseReg(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_InstanceNorm(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_L2Normalization(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_LRN(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_LayerNorm(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_LeakyReLU(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_MakeLoss(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_Pad(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_Pooling(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_Pooling_v1(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_RNN(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_ROIAlign(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_ROIPooling(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_SVMOutput(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_SequenceLast(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_SequenceMask(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_SequenceReverse(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_SliceChannel(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_Softmax(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_SoftmaxActivation(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_SoftmaxOutput(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_SparseEmbedding(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_SpatialTransformer(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_SwapAxis(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_UpSampling(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward__CrossDeviceCopy(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward__NDArray(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward__Native(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward__contrib_DeformableConvolution(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward__contrib_DeformablePSROIPooling(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward__contrib_MultiBoxDetection(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward__contrib_MultiBoxPrior(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward__contrib_MultiBoxTarget(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward__contrib_MultiProposal(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward__contrib_PSROIPooling(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward__contrib_Proposal(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward__contrib_SyncBatchNorm(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward__contrib_count_sketch(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward__contrib_fft(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward__contrib_ifft(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_abs(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_add(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_arccos(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_arccosh(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_arcsin(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_arcsinh(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_arctan(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_arctanh(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_batch_dot(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_broadcast_add(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_broadcast_div(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_broadcast_hypot(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_broadcast_maximum(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_broadcast_minimum(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_broadcast_mod(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_broadcast_mul(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_broadcast_power(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_broadcast_sub(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_cast(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_cbrt(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_clip(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_cond(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_contrib_AdaptiveAvgPooling2D(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_contrib_BilinearResize2D(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_contrib_bipartite_matching(is_ascend=_Null, threshold=_Null, topk=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- is_ascend : boolean, optional, default=0 Use ascend order for scores instead of descending. Please set threshold accordingly. threshold : float, required Ignore matching when score < thresh, if is_ascend=false, or ignore score > thresh, if is_ascend=true. topk : int, optional, default='-1' Limit the number of matches to topk, set -1 for no limit out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_contrib_box_iou(format=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- format : {'center', 'corner'},optional, default='corner' The box encoding type. "corner" means boxes are encoded as [xmin, ymin, xmax, ymax], "center" means boxes are encodes as [x, y, width, height]. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_contrib_box_nms(overlap_thresh=_Null, valid_thresh=_Null, topk=_Null, coord_start=_Null, score_index=_Null, id_index=_Null, force_suppress=_Null, in_format=_Null, out_format=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- overlap_thresh : float, optional, default=0.5 Overlapping(IoU) threshold to suppress object with smaller score. valid_thresh : float, optional, default=0 Filter input boxes to those whose scores greater than valid_thresh. topk : int, optional, default='-1' Apply nms to topk boxes with descending scores, -1 to no restriction. coord_start : int, optional, default='2' Start index of the consecutive 4 coordinates. score_index : int, optional, default='1' Index of the scores/confidence of boxes. id_index : int, optional, default='-1' Optional, index of the class categories, -1 to disable. force_suppress : boolean, optional, default=0 Optional, if set false and id_index is provided, nms will only apply to boxes belongs to the same category in_format : {'center', 'corner'},optional, default='corner' The input box encoding type. "corner" means boxes are encoded as [xmin, ymin, xmax, ymax], "center" means boxes are encodes as [x, y, width, height]. out_format : {'center', 'corner'},optional, default='corner' The output box encoding type. "corner" means boxes are encoded as [xmin, ymin, xmax, ymax], "center" means boxes are encodes as [x, y, width, height]. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_copy(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_cos(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_cosh(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_ctc_loss(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_degrees(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_diag(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_div(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_div_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_dot(transpose_a=_Null, transpose_b=_Null, forward_stype=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- transpose_a : boolean, optional, default=0 If true then transpose the first input before dot. transpose_b : boolean, optional, default=0 If true then transpose the second input before dot. forward_stype : {None, 'csr', 'default', 'row_sparse'},optional, default='None' The desired storage type of the forward output given by user, if thecombination of input storage types and this hint does not matchany implemented ones, the dot operator will perform fallback operationand still produce an output of the desired storage type. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_expm1(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_foreach(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_gamma(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_gammaln(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_gather_nd(data=None, indices=None, shape=_Null, out=None, name=None, **kwargs): r"""Accumulates data according to indices and get the result. It's the backward of `gather_nd`. Given `data` with shape `(Y_0, ..., Y_{K-1}, X_M, ..., X_{N-1})` and indices with shape `(M, Y_0, ..., Y_{K-1})`, the output will have shape `(X_0, X_1, ..., X_{N-1})`, where `M <= N`. If `M == N`, data shape should simply be `(Y_0, ..., Y_{K-1})`. The elements in output is defined as follows:: output[indices[0, y_0, ..., y_{K-1}], ..., indices[M-1, y_0, ..., y_{K-1}], x_M, ..., x_{N-1}] += data[y_0, ..., y_{K-1}, x_M, ..., x_{N-1}] all other entries in output are 0 or the original value if AddTo is triggered. Examples:: data = [2, 3, 0] indices = [[1, 1, 0], [0, 1, 0]] shape = (2, 2) _backward_gather_nd(data, indices, shape) = [[0, 0], [2, 3]] # Same as scatter_nd # The difference between scatter_nd and scatter_nd_acc is the latter will accumulate # the values that point to the same index. data = [2, 3, 0] indices = [[1, 1, 0], [1, 1, 0]] shape = (2, 2) _backward_gather_nd(data, indices, shape) = [[0, 0], [0, 5]] Parameters ---------- data : NDArray data indices : NDArray indices shape : Shape(tuple), required Shape of output. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_hard_sigmoid(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_hypot(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_hypot_scalar(lhs=None, rhs=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input scalar : float scalar value out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_linalg_gelqf(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_linalg_gemm(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_linalg_gemm2(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_linalg_potrf(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_linalg_potri(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_linalg_sumlogdiag(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_linalg_syevd(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_linalg_syrk(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_linalg_trmm(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_linalg_trsm(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_linear_reg_out(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_log(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_log10(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_log1p(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_log2(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_log_softmax(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_logistic_reg_out(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_mae_reg_out(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_max(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_maximum(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_maximum_scalar(lhs=None, rhs=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input scalar : float scalar value out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_mean(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_min(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_minimum(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_minimum_scalar(lhs=None, rhs=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input scalar : float scalar value out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_mod(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_mod_scalar(lhs=None, rhs=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input scalar : float scalar value out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_mul(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_mul_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_nanprod(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_nansum(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_norm(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_pick(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_power(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_power_scalar(lhs=None, rhs=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input scalar : float scalar value out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_prod(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_radians(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_rcbrt(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_rdiv_scalar(lhs=None, rhs=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input scalar : float scalar value out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_reciprocal(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_relu(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_repeat(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_reverse(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_rmod_scalar(lhs=None, rhs=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input scalar : float scalar value out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_rpower_scalar(lhs=None, rhs=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input scalar : float scalar value out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_rsqrt(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_sample_multinomial(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_sigmoid(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_sign(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_sin(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_sinh(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_slice(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_slice_axis(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_slice_like(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_smooth_l1(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_softmax(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_softmax_cross_entropy(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_softmin(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_softsign(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_sparse_retain(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_sqrt(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_square(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_square_sum(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_squeeze(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_stack(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_sub(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_sum(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_take(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_tan(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_tanh(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_tile(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_topk(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_where(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _backward_while_loop(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _broadcast_backward(out=None, name=None, **kwargs): r""" Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _cond(*data, **kwargs): r"""Run a if-then-else using user-defined condition and computation From:src/operator/control_flow.cc:1379 Parameters ---------- cond : Symbol Input graph for the condition. then_branch : Symbol Input graph for the then branch. else_branch : Symbol Input graph for the else branch. data : NDArray[] The input arrays that include data arrays and states. num_outputs : int, required The number of outputs of the subgraph. cond_input_locs : tuple of <>, required The locations of cond's inputs in the given inputs. then_input_locs : tuple of <>, required The locations of then's inputs in the given inputs. else_input_locs : tuple of <>, required The locations of else's inputs in the given inputs. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _copy(data=None, out=None, name=None, **kwargs): r"""Returns a copy of the input. From:src/operator/tensor/elemwise_unary_op_basic.cc:200 Parameters ---------- data : NDArray The input array. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _copyto(data=None, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray input data out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _crop_assign(lhs=None, rhs=None, begin=_Null, end=_Null, step=_Null, out=None, name=None, **kwargs): r"""Assign the rhs to a cropped subset of lhs. Requirements ------------ - output should be explicitly given and be the same as lhs. - lhs and rhs are of the same data type, and on the same device. From:src/operator/tensor/matrix_op.cc:440 Parameters ---------- lhs : NDArray Source input rhs : NDArray value to assign begin : Shape(tuple), required starting indices for the slice operation, supports negative indices. end : Shape(tuple), required ending indices for the slice operation, supports negative indices. step : Shape(tuple), optional, default=[] step for the slice operation, supports negative values. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _crop_assign_scalar(data=None, scalar=_Null, begin=_Null, end=_Null, step=_Null, out=None, name=None, **kwargs): r"""(Assign the scalar to a cropped subset of the input. Requirements ------------ - output should be explicitly given and be the same as input ) From:src/operator/tensor/matrix_op.cc:465 Parameters ---------- data : NDArray Source input scalar : float, optional, default=0 The scalar value for assignment. begin : Shape(tuple), required starting indices for the slice operation, supports negative indices. end : Shape(tuple), required ending indices for the slice operation, supports negative indices. step : Shape(tuple), optional, default=[] step for the slice operation, supports negative values. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _cvcopyMakeBorder(src=None, top=_Null, bot=_Null, left=_Null, right=_Null, type=_Null, value=_Null, values=_Null, out=None, name=None, **kwargs): r"""Pad image border with OpenCV. Parameters ---------- src : NDArray source image top : int, required Top margin. bot : int, required Bottom margin. left : int, required Left margin. right : int, required Right margin. type : int, optional, default='0' Filling type (default=cv2.BORDER_CONSTANT). value : double, optional, default=0 (Deprecated! Use ``values`` instead.) Fill with single value. values : tuple of , optional, default=[] Fill with value(RGB[A] or gray), up to 4 channels. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _cvimdecode(buf=None, flag=_Null, to_rgb=_Null, out=None, name=None, **kwargs): r"""Decode image with OpenCV. Note: return image in RGB by default, instead of OpenCV's default BGR. Parameters ---------- buf : NDArray Buffer containing binary encoded image flag : int, optional, default='1' Convert decoded image to grayscale (0) or color (1). to_rgb : boolean, optional, default=1 Whether to convert decoded image to mxnet's default RGB format (instead of opencv's default BGR). out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _cvimread(filename=_Null, flag=_Null, to_rgb=_Null, out=None, name=None, **kwargs): r"""Read and decode image with OpenCV. Note: return image in RGB by default, instead of OpenCV's default BGR. Parameters ---------- filename : string, required Name of the image file to be loaded. flag : int, optional, default='1' Convert decoded image to grayscale (0) or color (1). to_rgb : boolean, optional, default=1 Whether to convert decoded image to mxnet's default RGB format (instead of opencv's default BGR). out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _cvimresize(src=None, w=_Null, h=_Null, interp=_Null, out=None, name=None, **kwargs): r"""Resize image with OpenCV. Parameters ---------- src : NDArray source image w : int, required Width of resized image. h : int, required Height of resized image. interp : int, optional, default='1' Interpolation method (default=cv2.INTER_LINEAR). out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _div(lhs=None, rhs=None, out=None, name=None, **kwargs): r"""Divides arguments element-wise. The storage type of ``elemwise_div`` output is always dense Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _div_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r"""Divide an array with a scalar. ``_div_scalar`` only operates on data array of input if input is sparse. For example, if input of shape (100, 100) has only 2 non zero elements, i.e. input.data = [5, 6], scalar = nan, it will result output.data = [nan, nan] instead of 10000 nans. Defined in src/operator/tensor/elemwise_binary_scalar_op_basic.cc:L171 Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _equal(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _equal_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _eye(N=_Null, M=_Null, k=_Null, ctx=_Null, dtype=_Null, out=None, name=None, **kwargs): r"""Return a 2-D array with ones on the diagonal and zeros elsewhere. Parameters ---------- N : , required Number of rows in the output. M : , optional, default=0 Number of columns in the output. If 0, defaults to N k : , optional, default=0 Index of the diagonal. 0 (the default) refers to the main diagonal.A positive value refers to an upper diagonal.A negative value to a lower diagonal. ctx : string, optional, default='' Context of output, in format [cpu|gpu|cpu_pinned](n).Only used for imperative calls. dtype : {'float16', 'float32', 'float64', 'int32', 'int64', 'int8', 'uint8'},optional, default='float32' Target data type. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _foreach(*data, **kwargs): r"""Run a for loop over an NDArray with user-defined computation From:src/operator/control_flow.cc:1256 Parameters ---------- fn : Symbol Input graph. data : NDArray[] The input arrays that include data arrays and states. num_outputs : int, required The number of outputs of the subgraph. num_out_data : int, required The number of output data of the subgraph. in_state_locs : tuple of <>, required The locations of loop states among the inputs. in_data_locs : tuple of <>, required The locations of input data among the inputs. remain_locs : tuple of <>, required The locations of remaining data among the inputs. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _full(shape=_Null, ctx=_Null, dtype=_Null, value=_Null, out=None, name=None, **kwargs): r"""fill target with a scalar value Parameters ---------- shape : Shape(tuple), optional, default=[] The shape of the output ctx : string, optional, default='' Context of output, in format [cpu|gpu|cpu_pinned](n).Only used for imperative calls. dtype : {'float16', 'float32', 'float64', 'int32', 'int64', 'int8', 'uint8'},optional, default='float32' Target data type. value : double, required Value with which to fill newly created tensor out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _grad_add(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _greater(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _greater_equal(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _greater_equal_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _greater_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _histogram(data=None, bins=None, bin_cnt=_Null, range=_Null, out=None, name=None, **kwargs): r"""This operators implements the histogram function. Example:: x = [[0, 1], [2, 2], [3, 4]] histo, bin_edges = histogram(data=x, bin_bounds=[], bin_cnt=5, range=(0,5)) histo = [1, 1, 2, 1, 1] bin_edges = [0., 1., 2., 3., 4.] histo, bin_edges = histogram(data=x, bin_bounds=[0., 2.1, 3.]) histo = [4, 1] Defined in src/operator/tensor/histogram.cc:L132 Parameters ---------- data : NDArray Input ndarray bins : NDArray Input ndarray bin_cnt : int or None, optional, default='None' Number of bins for uniform case range : , optional, default=None The lower and upper range of the bins. if not provided, range is simply (a.min(), a.max()). values outside the range are ignored. the first element of the range must be less than or equal to the second. range affects the automatic bin computation as well. while bin width is computed to be optimal based on the actual data within range, the bin count will fill the entire range including portions containing no data. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _hypot(lhs=None, rhs=None, out=None, name=None, **kwargs): r"""Given the "legs" of a right triangle, return its hypotenuse. Defined in src/operator/tensor/elemwise_binary_op_extended.cc:L79 Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _hypot_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _identity_with_attr_like_rhs(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray First input. rhs : NDArray Second input. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _imdecode(mean=None, index=_Null, x0=_Null, y0=_Null, x1=_Null, y1=_Null, c=_Null, size=_Null, out=None, name=None, **kwargs): r"""Decode an image, clip to (x0, y0, x1, y1), subtract mean, and write to buffer Parameters ---------- mean : NDArray image mean index : int buffer position for output x0 : int x0 y0 : int y0 x1 : int x1 y1 : int y1 c : int channel size : int length of str_img out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _lesser(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _lesser_equal(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _lesser_equal_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _lesser_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _logical_and(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _logical_and_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _logical_or(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _logical_or_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _logical_xor(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _logical_xor_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _maximum(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _maximum_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _minimum(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _minimum_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _minus(lhs=None, rhs=None, out=None, name=None, **kwargs): r"""Subtracts arguments element-wise. The storage type of ``elemwise_sub`` output depends on storage types of inputs - elemwise_sub(row_sparse, row_sparse) = row_sparse - elemwise_sub(csr, csr) = csr - elemwise_sub(default, csr) = default - elemwise_sub(csr, default) = default - elemwise_sub(default, rsp) = default - elemwise_sub(rsp, default) = default - otherwise, ``elemwise_sub`` generates output with default storage Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _minus_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _mod(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _mod_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _mul(lhs=None, rhs=None, out=None, name=None, **kwargs): r"""Multiplies arguments element-wise. The storage type of ``elemwise_mul`` output depends on storage types of inputs - elemwise_mul(default, default) = default - elemwise_mul(row_sparse, row_sparse) = row_sparse - elemwise_mul(default, row_sparse) = row_sparse - elemwise_mul(row_sparse, default) = row_sparse - elemwise_mul(csr, csr) = csr - otherwise, ``elemwise_mul`` generates output with default storage Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _mul_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r"""Multiply an array with a scalar. ``_mul_scalar`` only operates on data array of input if input is sparse. For example, if input of shape (100, 100) has only 2 non zero elements, i.e. input.data = [5, 6], scalar = nan, it will result output.data = [nan, nan] instead of 10000 nans. Defined in src/operator/tensor/elemwise_binary_scalar_op_basic.cc:L149 Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _not_equal(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _not_equal_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _onehot_encode(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray Left operand to the function. rhs : NDArray Right operand to the function. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _ones(shape=_Null, ctx=_Null, dtype=_Null, out=None, name=None, **kwargs): r"""fill target with ones Parameters ---------- shape : Shape(tuple), optional, default=[] The shape of the output ctx : string, optional, default='' Context of output, in format [cpu|gpu|cpu_pinned](n).Only used for imperative calls. dtype : {'float16', 'float32', 'float64', 'int32', 'int64', 'int8', 'uint8'},optional, default='float32' Target data type. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _plus(lhs=None, rhs=None, out=None, name=None, **kwargs): r"""Adds arguments element-wise. The storage type of ``elemwise_add`` output depends on storage types of inputs - elemwise_add(row_sparse, row_sparse) = row_sparse - elemwise_add(csr, csr) = csr - elemwise_add(default, csr) = default - elemwise_add(csr, default) = default - elemwise_add(default, rsp) = default - elemwise_add(rsp, default) = default - otherwise, ``elemwise_add`` generates output with default storage Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _plus_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _power(lhs=None, rhs=None, out=None, name=None, **kwargs): r""" Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _power_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _random_exponential(lam=_Null, shape=_Null, ctx=_Null, dtype=_Null, out=None, name=None, **kwargs): r"""Draw random samples from an exponential distribution. Samples are distributed according to an exponential distribution parametrized by *lambda* (rate). Example:: exponential(lam=4, shape=(2,2)) = [[ 0.0097189 , 0.08999364], [ 0.04146638, 0.31715935]] Defined in src/operator/random/sample_op.cc:L115 Parameters ---------- lam : float, optional, default=1 Lambda parameter (rate) of the exponential distribution. shape : Shape(tuple), optional, default=[] Shape of the output. ctx : string, optional, default='' Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls. dtype : {'None', 'float16', 'float32', 'float64'},optional, default='None' DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None). out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _random_gamma(alpha=_Null, beta=_Null, shape=_Null, ctx=_Null, dtype=_Null, out=None, name=None, **kwargs): r"""Draw random samples from a gamma distribution. Samples are distributed according to a gamma distribution parametrized by *alpha* (shape) and *beta* (scale). Example:: gamma(alpha=9, beta=0.5, shape=(2,2)) = [[ 7.10486984, 3.37695289], [ 3.91697288, 3.65933681]] Defined in src/operator/random/sample_op.cc:L100 Parameters ---------- alpha : float, optional, default=1 Alpha parameter (shape) of the gamma distribution. beta : float, optional, default=1 Beta parameter (scale) of the gamma distribution. shape : Shape(tuple), optional, default=[] Shape of the output. ctx : string, optional, default='' Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls. dtype : {'None', 'float16', 'float32', 'float64'},optional, default='None' DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None). out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _random_generalized_negative_binomial(mu=_Null, alpha=_Null, shape=_Null, ctx=_Null, dtype=_Null, out=None, name=None, **kwargs): r"""Draw random samples from a generalized negative binomial distribution. Samples are distributed according to a generalized negative binomial distribution parametrized by *mu* (mean) and *alpha* (dispersion). *alpha* is defined as *1/k* where *k* is the failure limit of the number of unsuccessful experiments (generalized to real numbers). Samples will always be returned as a floating point data type. Example:: generalized_negative_binomial(mu=2.0, alpha=0.3, shape=(2,2)) = [[ 2., 1.], [ 6., 4.]] Defined in src/operator/random/sample_op.cc:L168 Parameters ---------- mu : float, optional, default=1 Mean of the negative binomial distribution. alpha : float, optional, default=1 Alpha (dispersion) parameter of the negative binomial distribution. shape : Shape(tuple), optional, default=[] Shape of the output. ctx : string, optional, default='' Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls. dtype : {'None', 'float16', 'float32', 'float64'},optional, default='None' DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None). out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _random_negative_binomial(k=_Null, p=_Null, shape=_Null, ctx=_Null, dtype=_Null, out=None, name=None, **kwargs): r"""Draw random samples from a negative binomial distribution. Samples are distributed according to a negative binomial distribution parametrized by *k* (limit of unsuccessful experiments) and *p* (failure probability in each experiment). Samples will always be returned as a floating point data type. Example:: negative_binomial(k=3, p=0.4, shape=(2,2)) = [[ 4., 7.], [ 2., 5.]] Defined in src/operator/random/sample_op.cc:L149 Parameters ---------- k : int, optional, default='1' Limit of unsuccessful experiments. p : float, optional, default=1 Failure probability in each experiment. shape : Shape(tuple), optional, default=[] Shape of the output. ctx : string, optional, default='' Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls. dtype : {'None', 'float16', 'float32', 'float64'},optional, default='None' DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None). out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _random_normal(loc=_Null, scale=_Null, shape=_Null, ctx=_Null, dtype=_Null, out=None, name=None, **kwargs): r"""Draw random samples from a normal (Gaussian) distribution. .. note:: The existing alias ``normal`` is deprecated. Samples are distributed according to a normal distribution parametrized by *loc* (mean) and *scale* (standard deviation). Example:: normal(loc=0, scale=1, shape=(2,2)) = [[ 1.89171135, -1.16881478], [-1.23474145, 1.55807114]] Defined in src/operator/random/sample_op.cc:L85 Parameters ---------- loc : float, optional, default=0 Mean of the distribution. scale : float, optional, default=1 Standard deviation of the distribution. shape : Shape(tuple), optional, default=[] Shape of the output. ctx : string, optional, default='' Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls. dtype : {'None', 'float16', 'float32', 'float64'},optional, default='None' DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None). out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _random_poisson(lam=_Null, shape=_Null, ctx=_Null, dtype=_Null, out=None, name=None, **kwargs): r"""Draw random samples from a Poisson distribution. Samples are distributed according to a Poisson distribution parametrized by *lambda* (rate). Samples will always be returned as a floating point data type. Example:: poisson(lam=4, shape=(2,2)) = [[ 5., 2.], [ 4., 6.]] Defined in src/operator/random/sample_op.cc:L132 Parameters ---------- lam : float, optional, default=1 Lambda parameter (rate) of the Poisson distribution. shape : Shape(tuple), optional, default=[] Shape of the output. ctx : string, optional, default='' Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls. dtype : {'None', 'float16', 'float32', 'float64'},optional, default='None' DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None). out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _random_uniform(low=_Null, high=_Null, shape=_Null, ctx=_Null, dtype=_Null, out=None, name=None, **kwargs): r"""Draw random samples from a uniform distribution. .. note:: The existing alias ``uniform`` is deprecated. Samples are uniformly distributed over the half-open interval *[low, high)* (includes *low*, but excludes *high*). Example:: uniform(low=0, high=1, shape=(2,2)) = [[ 0.60276335, 0.85794562], [ 0.54488319, 0.84725171]] Defined in src/operator/random/sample_op.cc:L66 Parameters ---------- low : float, optional, default=0 Lower bound of the distribution. high : float, optional, default=1 Upper bound of the distribution. shape : Shape(tuple), optional, default=[] Shape of the output. ctx : string, optional, default='' Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls. dtype : {'None', 'float16', 'float32', 'float64'},optional, default='None' DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None). out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _ravel_multi_index(data=None, shape=_Null, out=None, name=None, **kwargs): r"""Converts a batch of index arrays into an array of flat indices. The operator follows numpy conventions so a single multi index is given by a column of the input matrix. Examples:: A = [[3,6,6],[4,5,1]] ravel(A, shape=(7,6)) = [22,41,37] Defined in src/operator/tensor/ravel.cc:L41 Parameters ---------- data : NDArray Batch of multi-indices shape : Shape(tuple), optional, default=[] Shape of the array into which the multi-indices apply. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _rdiv_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _rminus_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _rmod_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _rnn_param_concat(*data, **kwargs): r""" Parameters ---------- data : NDArray[] List of arrays to concatenate dim : int, optional, default='1' the dimension to be concated. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _rpower_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _sample_exponential(lam=None, shape=_Null, dtype=_Null, out=None, name=None, **kwargs): r"""Concurrent sampling from multiple exponential distributions with parameters lambda (rate). The parameters of the distributions are provided as an input array. Let *[s]* be the shape of the input array, *n* be the dimension of *[s]*, *[t]* be the shape specified as the parameter of the operator, and *m* be the dimension of *[t]*. Then the output will be a *(n+m)*-dimensional array with shape *[s]x[t]*. For any valid *n*-dimensional index *i* with respect to the input array, *output[i]* will be an *m*-dimensional array that holds randomly drawn samples from the distribution which is parameterized by the input value at index *i*. If the shape parameter of the operator is not set, then one sample will be drawn per distribution and the output array has the same shape as the input array. Examples:: lam = [ 1.0, 8.5 ] // Draw a single sample for each distribution sample_exponential(lam) = [ 0.51837951, 0.09994757] // Draw a vector containing two samples for each distribution sample_exponential(lam, shape=(2)) = [[ 0.51837951, 0.19866663], [ 0.09994757, 0.50447971]] Defined in src/operator/random/multisample_op.cc:L284 Parameters ---------- lam : NDArray Lambda (rate) parameters of the distributions. shape : Shape(tuple), optional, default=[] Shape to be sampled from each random distribution. dtype : {'None', 'float16', 'float32', 'float64'},optional, default='None' DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None). out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _sample_gamma(alpha=None, beta=None, shape=_Null, dtype=_Null, out=None, name=None, **kwargs): r"""Concurrent sampling from multiple gamma distributions with parameters *alpha* (shape) and *beta* (scale). The parameters of the distributions are provided as input arrays. Let *[s]* be the shape of the input arrays, *n* be the dimension of *[s]*, *[t]* be the shape specified as the parameter of the operator, and *m* be the dimension of *[t]*. Then the output will be a *(n+m)*-dimensional array with shape *[s]x[t]*. For any valid *n*-dimensional index *i* with respect to the input arrays, *output[i]* will be an *m*-dimensional array that holds randomly drawn samples from the distribution which is parameterized by the input values at index *i*. If the shape parameter of the operator is not set, then one sample will be drawn per distribution and the output array has the same shape as the input arrays. Examples:: alpha = [ 0.0, 2.5 ] beta = [ 1.0, 0.7 ] // Draw a single sample for each distribution sample_gamma(alpha, beta) = [ 0. , 2.25797319] // Draw a vector containing two samples for each distribution sample_gamma(alpha, beta, shape=(2)) = [[ 0. , 0. ], [ 2.25797319, 1.70734084]] Defined in src/operator/random/multisample_op.cc:L282 Parameters ---------- alpha : NDArray Alpha (shape) parameters of the distributions. shape : Shape(tuple), optional, default=[] Shape to be sampled from each random distribution. dtype : {'None', 'float16', 'float32', 'float64'},optional, default='None' DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None). beta : NDArray Beta (scale) parameters of the distributions. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _sample_generalized_negative_binomial(mu=None, alpha=None, shape=_Null, dtype=_Null, out=None, name=None, **kwargs): r"""Concurrent sampling from multiple generalized negative binomial distributions with parameters *mu* (mean) and *alpha* (dispersion). The parameters of the distributions are provided as input arrays. Let *[s]* be the shape of the input arrays, *n* be the dimension of *[s]*, *[t]* be the shape specified as the parameter of the operator, and *m* be the dimension of *[t]*. Then the output will be a *(n+m)*-dimensional array with shape *[s]x[t]*. For any valid *n*-dimensional index *i* with respect to the input arrays, *output[i]* will be an *m*-dimensional array that holds randomly drawn samples from the distribution which is parameterized by the input values at index *i*. If the shape parameter of the operator is not set, then one sample will be drawn per distribution and the output array has the same shape as the input arrays. Samples will always be returned as a floating point data type. Examples:: mu = [ 2.0, 2.5 ] alpha = [ 1.0, 0.1 ] // Draw a single sample for each distribution sample_generalized_negative_binomial(mu, alpha) = [ 0., 3.] // Draw a vector containing two samples for each distribution sample_generalized_negative_binomial(mu, alpha, shape=(2)) = [[ 0., 3.], [ 3., 1.]] Defined in src/operator/random/multisample_op.cc:L293 Parameters ---------- mu : NDArray Means of the distributions. shape : Shape(tuple), optional, default=[] Shape to be sampled from each random distribution. dtype : {'None', 'float16', 'float32', 'float64'},optional, default='None' DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None). alpha : NDArray Alpha (dispersion) parameters of the distributions. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _sample_multinomial(data=None, shape=_Null, get_prob=_Null, dtype=_Null, out=None, name=None, **kwargs): r"""Concurrent sampling from multiple multinomial distributions. *data* is an *n* dimensional array whose last dimension has length *k*, where *k* is the number of possible outcomes of each multinomial distribution. This operator will draw *shape* samples from each distribution. If shape is empty one sample will be drawn from each distribution. If *get_prob* is true, a second array containing log likelihood of the drawn samples will also be returned. This is usually used for reinforcement learning where you can provide reward as head gradient for this array to estimate gradient. Note that the input distribution must be normalized, i.e. *data* must sum to 1 along its last axis. Examples:: probs = [[0, 0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1, 0]] // Draw a single sample for each distribution sample_multinomial(probs) = [3, 0] // Draw a vector containing two samples for each distribution sample_multinomial(probs, shape=(2)) = [[4, 2], [0, 0]] // requests log likelihood sample_multinomial(probs, get_prob=True) = [2, 1], [0.2, 0.3] Parameters ---------- data : NDArray Distribution probabilities. Must sum to one on the last axis. shape : Shape(tuple), optional, default=[] Shape to be sampled from each random distribution. get_prob : boolean, optional, default=0 Whether to also return the log probability of sampled result. This is usually used for differentiating through stochastic variables, e.g. in reinforcement learning. dtype : {'float16', 'float32', 'float64', 'int32', 'uint8'},optional, default='int32' DType of the output in case this can't be inferred. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _sample_negative_binomial(k=None, p=None, shape=_Null, dtype=_Null, out=None, name=None, **kwargs): r"""Concurrent sampling from multiple negative binomial distributions with parameters *k* (failure limit) and *p* (failure probability). The parameters of the distributions are provided as input arrays. Let *[s]* be the shape of the input arrays, *n* be the dimension of *[s]*, *[t]* be the shape specified as the parameter of the operator, and *m* be the dimension of *[t]*. Then the output will be a *(n+m)*-dimensional array with shape *[s]x[t]*. For any valid *n*-dimensional index *i* with respect to the input arrays, *output[i]* will be an *m*-dimensional array that holds randomly drawn samples from the distribution which is parameterized by the input values at index *i*. If the shape parameter of the operator is not set, then one sample will be drawn per distribution and the output array has the same shape as the input arrays. Samples will always be returned as a floating point data type. Examples:: k = [ 20, 49 ] p = [ 0.4 , 0.77 ] // Draw a single sample for each distribution sample_negative_binomial(k, p) = [ 15., 16.] // Draw a vector containing two samples for each distribution sample_negative_binomial(k, p, shape=(2)) = [[ 15., 50.], [ 16., 12.]] Defined in src/operator/random/multisample_op.cc:L289 Parameters ---------- k : NDArray Limits of unsuccessful experiments. shape : Shape(tuple), optional, default=[] Shape to be sampled from each random distribution. dtype : {'None', 'float16', 'float32', 'float64'},optional, default='None' DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None). p : NDArray Failure probabilities in each experiment. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _sample_normal(mu=None, sigma=None, shape=_Null, dtype=_Null, out=None, name=None, **kwargs): r"""Concurrent sampling from multiple normal distributions with parameters *mu* (mean) and *sigma* (standard deviation). The parameters of the distributions are provided as input arrays. Let *[s]* be the shape of the input arrays, *n* be the dimension of *[s]*, *[t]* be the shape specified as the parameter of the operator, and *m* be the dimension of *[t]*. Then the output will be a *(n+m)*-dimensional array with shape *[s]x[t]*. For any valid *n*-dimensional index *i* with respect to the input arrays, *output[i]* will be an *m*-dimensional array that holds randomly drawn samples from the distribution which is parameterized by the input values at index *i*. If the shape parameter of the operator is not set, then one sample will be drawn per distribution and the output array has the same shape as the input arrays. Examples:: mu = [ 0.0, 2.5 ] sigma = [ 1.0, 3.7 ] // Draw a single sample for each distribution sample_normal(mu, sigma) = [-0.56410581, 0.95934606] // Draw a vector containing two samples for each distribution sample_normal(mu, sigma, shape=(2)) = [[-0.56410581, 0.2928229 ], [ 0.95934606, 4.48287058]] Defined in src/operator/random/multisample_op.cc:L279 Parameters ---------- mu : NDArray Means of the distributions. shape : Shape(tuple), optional, default=[] Shape to be sampled from each random distribution. dtype : {'None', 'float16', 'float32', 'float64'},optional, default='None' DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None). sigma : NDArray Standard deviations of the distributions. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _sample_poisson(lam=None, shape=_Null, dtype=_Null, out=None, name=None, **kwargs): r"""Concurrent sampling from multiple Poisson distributions with parameters lambda (rate). The parameters of the distributions are provided as an input array. Let *[s]* be the shape of the input array, *n* be the dimension of *[s]*, *[t]* be the shape specified as the parameter of the operator, and *m* be the dimension of *[t]*. Then the output will be a *(n+m)*-dimensional array with shape *[s]x[t]*. For any valid *n*-dimensional index *i* with respect to the input array, *output[i]* will be an *m*-dimensional array that holds randomly drawn samples from the distribution which is parameterized by the input value at index *i*. If the shape parameter of the operator is not set, then one sample will be drawn per distribution and the output array has the same shape as the input array. Samples will always be returned as a floating point data type. Examples:: lam = [ 1.0, 8.5 ] // Draw a single sample for each distribution sample_poisson(lam) = [ 0., 13.] // Draw a vector containing two samples for each distribution sample_poisson(lam, shape=(2)) = [[ 0., 4.], [ 13., 8.]] Defined in src/operator/random/multisample_op.cc:L286 Parameters ---------- lam : NDArray Lambda (rate) parameters of the distributions. shape : Shape(tuple), optional, default=[] Shape to be sampled from each random distribution. dtype : {'None', 'float16', 'float32', 'float64'},optional, default='None' DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None). out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _sample_uniform(low=None, high=None, shape=_Null, dtype=_Null, out=None, name=None, **kwargs): r"""Concurrent sampling from multiple uniform distributions on the intervals given by *[low,high)*. The parameters of the distributions are provided as input arrays. Let *[s]* be the shape of the input arrays, *n* be the dimension of *[s]*, *[t]* be the shape specified as the parameter of the operator, and *m* be the dimension of *[t]*. Then the output will be a *(n+m)*-dimensional array with shape *[s]x[t]*. For any valid *n*-dimensional index *i* with respect to the input arrays, *output[i]* will be an *m*-dimensional array that holds randomly drawn samples from the distribution which is parameterized by the input values at index *i*. If the shape parameter of the operator is not set, then one sample will be drawn per distribution and the output array has the same shape as the input arrays. Examples:: low = [ 0.0, 2.5 ] high = [ 1.0, 3.7 ] // Draw a single sample for each distribution sample_uniform(low, high) = [ 0.40451524, 3.18687344] // Draw a vector containing two samples for each distribution sample_uniform(low, high, shape=(2)) = [[ 0.40451524, 0.18017688], [ 3.18687344, 3.68352246]] Defined in src/operator/random/multisample_op.cc:L277 Parameters ---------- low : NDArray Lower bounds of the distributions. shape : Shape(tuple), optional, default=[] Shape to be sampled from each random distribution. dtype : {'None', 'float16', 'float32', 'float64'},optional, default='None' DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None). high : NDArray Upper bounds of the distributions. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _sample_unique_zipfian(range_max=_Null, shape=_Null, out=None, name=None, **kwargs): r"""Draw random samples from an an approximately log-uniform or Zipfian distribution without replacement. This operation takes a 2-D shape `(batch_size, num_sampled)`, and randomly generates *num_sampled* samples from the range of integers [0, range_max) for each instance in the batch. The elements in each instance are drawn without replacement from the base distribution. The base distribution for this operator is an approximately log-uniform or Zipfian distribution: P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1) Additionaly, it also returns the number of trials used to obtain `num_sampled` samples for each instance in the batch. Example:: samples, trials = _sample_unique_zipfian(750000, shape=(4, 8192)) unique(samples[0]) = 8192 unique(samples[3]) = 8192 trials[0] = 16435 Defined in src/operator/random/unique_sample_op.cc:L66 Parameters ---------- range_max : int, required The number of possible classes. shape : Shape(tuple), optional, default=[] 2-D shape of the output, where shape[0] is the batch size, and shape[1] is the number of candidates to sample for each batch. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _scatter_elemwise_div(lhs=None, rhs=None, out=None, name=None, **kwargs): r"""Divides arguments element-wise. If the left-hand-side input is 'row_sparse', then only the values which exist in the left-hand sparse array are computed. The 'missing' values are ignored. The storage type of ``_scatter_elemwise_div`` output depends on storage types of inputs - _scatter_elemwise_div(row_sparse, row_sparse) = row_sparse - _scatter_elemwise_div(row_sparse, dense) = row_sparse - _scatter_elemwise_div(row_sparse, csr) = row_sparse - otherwise, ``_scatter_elemwise_div`` behaves exactly like elemwise_div and generates output with default storage Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _scatter_minus_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r"""Subtracts a scalar to a tensor element-wise. If the left-hand-side input is 'row_sparse' or 'csr', then only the values which exist in the left-hand sparse array are computed. The 'missing' values are ignored. The storage type of ``_scatter_minus_scalar`` output depends on storage types of inputs - _scatter_minus_scalar(row_sparse, scalar) = row_sparse - _scatter_minus_scalar(csr, scalar) = csr - otherwise, ``_scatter_minus_scalar`` behaves exactly like _minus_scalar and generates output with default storage Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _scatter_plus_scalar(data=None, scalar=_Null, out=None, name=None, **kwargs): r"""Adds a scalar to a tensor element-wise. If the left-hand-side input is 'row_sparse' or 'csr', then only the values which exist in the left-hand sparse array are computed. The 'missing' values are ignored. The storage type of ``_scatter_plus_scalar`` output depends on storage types of inputs - _scatter_plus_scalar(row_sparse, scalar) = row_sparse - _scatter_plus_scalar(csr, scalar) = csr - otherwise, ``_scatter_plus_scalar`` behaves exactly like _plus_scalar and generates output with default storage Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _scatter_set_nd(lhs=None, rhs=None, indices=None, shape=_Null, out=None, name=None, **kwargs): r"""This operator has the same functionality as scatter_nd except that it does not reset the elements not indexed by the input index `NDArray` in the input data `NDArray`. output should be explicitly given and be the same as lhs. .. note:: This operator is for internal use only. Examples:: data = [2, 3, 0] indices = [[1, 1, 0], [0, 1, 0]] out = [[1, 1], [1, 1]] _scatter_set_nd(lhs=out, rhs=data, indices=indices, out=out) out = [[0, 1], [2, 3]] Parameters ---------- lhs : NDArray source input rhs : NDArray value to assign indices : NDArray indices shape : Shape(tuple), required Shape of output. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _set_value(src=_Null, out=None, name=None, **kwargs): r""" Parameters ---------- src : real_t Source input to the function. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _shuffle(data=None, out=None, name=None, **kwargs): r"""Randomly shuffle the elements. This shuffles the array along the first axis. The order of the elements in each subarray does not change. For example, if a 2D array is given, the order of the rows randomly changes, but the order of the elements in each row does not change. Parameters ---------- data : NDArray Data to be shuffled. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _slice_assign(lhs=None, rhs=None, begin=_Null, end=_Null, step=_Null, out=None, name=None, **kwargs): r"""Assign the rhs to a cropped subset of lhs. Requirements ------------ - output should be explicitly given and be the same as lhs. - lhs and rhs are of the same data type, and on the same device. From:src/operator/tensor/matrix_op.cc:440 Parameters ---------- lhs : NDArray Source input rhs : NDArray value to assign begin : Shape(tuple), required starting indices for the slice operation, supports negative indices. end : Shape(tuple), required ending indices for the slice operation, supports negative indices. step : Shape(tuple), optional, default=[] step for the slice operation, supports negative values. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _slice_assign_scalar(data=None, scalar=_Null, begin=_Null, end=_Null, step=_Null, out=None, name=None, **kwargs): r"""(Assign the scalar to a cropped subset of the input. Requirements ------------ - output should be explicitly given and be the same as input ) From:src/operator/tensor/matrix_op.cc:465 Parameters ---------- data : NDArray Source input scalar : float, optional, default=0 The scalar value for assignment. begin : Shape(tuple), required starting indices for the slice operation, supports negative indices. end : Shape(tuple), required ending indices for the slice operation, supports negative indices. step : Shape(tuple), optional, default=[] step for the slice operation, supports negative values. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _square_sum(data=None, axis=_Null, keepdims=_Null, exclude=_Null, out=None, name=None, **kwargs): r"""Computes the square sum of array elements over a given axis for row-sparse matrix. This is a temporary solution for fusing ops square and sum together for row-sparse matrix to save memory for storing gradients. It will become deprecated once the functionality of fusing operators is finished in the future. Example:: dns = mx.nd.array([[0, 0], [1, 2], [0, 0], [3, 4], [0, 0]]) rsp = dns.tostype('row_sparse') sum = mx.nd._internal._square_sum(rsp, axis=1) sum = [0, 5, 0, 25, 0] Defined in src/operator/tensor/square_sum.cc:L63 Parameters ---------- data : NDArray The input axis : Shape or None, optional, default=None The axis or axes along which to perform the reduction. The default, `axis=()`, will compute over all elements into a scalar array with shape `(1,)`. If `axis` is int, a reduction is performed on a particular axis. If `axis` is a tuple of ints, a reduction is performed on all the axes specified in the tuple. If `exclude` is true, reduction will be performed on the axes that are NOT in axis instead. Negative values means indexing from right to left. keepdims : boolean, optional, default=0 If this is set to `True`, the reduced axes are left in the result as dimension with size one. exclude : boolean, optional, default=0 Whether to perform reduction on axis that are NOT in axis instead. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _sub(lhs=None, rhs=None, out=None, name=None, **kwargs): r"""Subtracts arguments element-wise. The storage type of ``elemwise_sub`` output depends on storage types of inputs - elemwise_sub(row_sparse, row_sparse) = row_sparse - elemwise_sub(csr, csr) = csr - elemwise_sub(default, csr) = default - elemwise_sub(csr, default) = default - elemwise_sub(default, rsp) = default - elemwise_sub(rsp, default) = default - otherwise, ``elemwise_sub`` generates output with default storage Parameters ---------- lhs : NDArray first input rhs : NDArray second input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _unravel_index(data=None, shape=_Null, out=None, name=None, **kwargs): r"""Converts an array of flat indices into a batch of index arrays. The operator follows numpy conventions so a single multi index is given by a column of the output matrix. Examples:: A = [22,41,37] unravel(A, shape=(7,6)) = [[3,6,6],[4,5,1]] Defined in src/operator/tensor/ravel.cc:L65 Parameters ---------- data : NDArray Array of flat indices shape : Shape(tuple), optional, default=[] Shape of the array into which the multi-indices apply. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _while_loop(*data, **kwargs): r"""Run a while loop over with user-defined condition and computation From:src/operator/control_flow.cc:1317 Parameters ---------- cond : Symbol Input graph for the loop condition. func : Symbol Input graph for the loop body. data : NDArray[] The input arrays that include data arrays and states. num_outputs : int, required The number of outputs of the subgraph. num_out_data : int, required The number of outputs from the function body. max_iterations : int, required Maximum number of iterations. cond_input_locs : tuple of <>, required The locations of cond's inputs in the given inputs. func_input_locs : tuple of <>, required The locations of func's inputs in the given inputs. func_var_locs : tuple of <>, required The locations of loop_vars among func's inputs. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _zeros(shape=_Null, ctx=_Null, dtype=_Null, out=None, name=None, **kwargs): r"""fill target with zeros Parameters ---------- shape : Shape(tuple), optional, default=[] The shape of the output ctx : string, optional, default='' Context of output, in format [cpu|gpu|cpu_pinned](n).Only used for imperative calls. dtype : {'float16', 'float32', 'float64', 'int32', 'int64', 'int8', 'uint8'},optional, default='float32' Target data type. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) def _zeros_without_dtype(shape=_Null, ctx=_Null, dtype=_Null, out=None, name=None, **kwargs): r"""fill target with zeros without default dtype Parameters ---------- shape : Shape(tuple), optional, default=[] The shape of the output ctx : string, optional, default='' Context of output, in format [cpu|gpu|cpu_pinned](n).Only used for imperative calls. dtype : int, optional, default='-1' Target data type. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. """ return (0,) __all__ = ['_CachedOp', '_CrossDeviceCopy', '_CustomFunction', '_Div', '_DivScalar', '_Equal', '_EqualScalar', '_Greater', '_GreaterEqualScalar', '_GreaterScalar', '_Greater_Equal', '_Hypot', '_HypotScalar', '_Lesser', '_LesserEqualScalar', '_LesserScalar', '_Lesser_Equal', '_LogicalAndScalar', '_LogicalOrScalar', '_LogicalXorScalar', '_Logical_And', '_Logical_Or', '_Logical_Xor', '_Maximum', '_MaximumScalar', '_Minimum', '_MinimumScalar', '_Minus', '_MinusScalar', '_Mod', '_ModScalar', '_Mul', '_MulScalar', '_NDArray', '_Native', '_NoGradient', '_NotEqualScalar', '_Not_Equal', '_Plus', '_PlusScalar', '_Power', '_PowerScalar', '_RDivScalar', '_RMinusScalar', '_RModScalar', '_RPowerScalar', '_add', '_arange', '_backward_Activation', '_backward_BatchNorm', '_backward_BatchNorm_v1', '_backward_BilinearSampler', '_backward_CachedOp', '_backward_Concat', '_backward_Convolution', '_backward_Convolution_v1', '_backward_Correlation', '_backward_Crop', '_backward_Custom', '_backward_CustomFunction', '_backward_Deconvolution', '_backward_Dropout', '_backward_Embedding', '_backward_FullyConnected', '_backward_GridGenerator', '_backward_IdentityAttachKLSparseReg', '_backward_InstanceNorm', '_backward_L2Normalization', '_backward_LRN', '_backward_LayerNorm', '_backward_LeakyReLU', '_backward_MakeLoss', '_backward_Pad', '_backward_Pooling', '_backward_Pooling_v1', '_backward_RNN', '_backward_ROIAlign', '_backward_ROIPooling', '_backward_SVMOutput', '_backward_SequenceLast', '_backward_SequenceMask', '_backward_SequenceReverse', '_backward_SliceChannel', '_backward_Softmax', '_backward_SoftmaxActivation', '_backward_SoftmaxOutput', '_backward_SparseEmbedding', '_backward_SpatialTransformer', '_backward_SwapAxis', '_backward_UpSampling', '_backward__CrossDeviceCopy', '_backward__NDArray', '_backward__Native', '_backward__contrib_DeformableConvolution', '_backward__contrib_DeformablePSROIPooling', '_backward__contrib_MultiBoxDetection', '_backward__contrib_MultiBoxPrior', '_backward__contrib_MultiBoxTarget', '_backward__contrib_MultiProposal', '_backward__contrib_PSROIPooling', '_backward__contrib_Proposal', '_backward__contrib_SyncBatchNorm', '_backward__contrib_count_sketch', '_backward__contrib_fft', '_backward__contrib_ifft', '_backward_abs', '_backward_add', '_backward_arccos', '_backward_arccosh', '_backward_arcsin', '_backward_arcsinh', '_backward_arctan', '_backward_arctanh', '_backward_batch_dot', '_backward_broadcast_add', '_backward_broadcast_div', '_backward_broadcast_hypot', '_backward_broadcast_maximum', '_backward_broadcast_minimum', '_backward_broadcast_mod', '_backward_broadcast_mul', '_backward_broadcast_power', '_backward_broadcast_sub', '_backward_cast', '_backward_cbrt', '_backward_clip', '_backward_cond', '_backward_contrib_AdaptiveAvgPooling2D', '_backward_contrib_BilinearResize2D', '_backward_contrib_bipartite_matching', '_backward_contrib_box_iou', '_backward_contrib_box_nms', '_backward_copy', '_backward_cos', '_backward_cosh', '_backward_ctc_loss', '_backward_degrees', '_backward_diag', '_backward_div', '_backward_div_scalar', '_backward_dot', '_backward_expm1', '_backward_foreach', '_backward_gamma', '_backward_gammaln', '_backward_gather_nd', '_backward_hard_sigmoid', '_backward_hypot', '_backward_hypot_scalar', '_backward_linalg_gelqf', '_backward_linalg_gemm', '_backward_linalg_gemm2', '_backward_linalg_potrf', '_backward_linalg_potri', '_backward_linalg_sumlogdiag', '_backward_linalg_syevd', '_backward_linalg_syrk', '_backward_linalg_trmm', '_backward_linalg_trsm', '_backward_linear_reg_out', '_backward_log', '_backward_log10', '_backward_log1p', '_backward_log2', '_backward_log_softmax', '_backward_logistic_reg_out', '_backward_mae_reg_out', '_backward_max', '_backward_maximum', '_backward_maximum_scalar', '_backward_mean', '_backward_min', '_backward_minimum', '_backward_minimum_scalar', '_backward_mod', '_backward_mod_scalar', '_backward_mul', '_backward_mul_scalar', '_backward_nanprod', '_backward_nansum', '_backward_norm', '_backward_pick', '_backward_power', '_backward_power_scalar', '_backward_prod', '_backward_radians', '_backward_rcbrt', '_backward_rdiv_scalar', '_backward_reciprocal', '_backward_relu', '_backward_repeat', '_backward_reverse', '_backward_rmod_scalar', '_backward_rpower_scalar', '_backward_rsqrt', '_backward_sample_multinomial', '_backward_sigmoid', '_backward_sign', '_backward_sin', '_backward_sinh', '_backward_slice', '_backward_slice_axis', '_backward_slice_like', '_backward_smooth_l1', '_backward_softmax', '_backward_softmax_cross_entropy', '_backward_softmin', '_backward_softsign', '_backward_sparse_retain', '_backward_sqrt', '_backward_square', '_backward_square_sum', '_backward_squeeze', '_backward_stack', '_backward_sub', '_backward_sum', '_backward_take', '_backward_tan', '_backward_tanh', '_backward_tile', '_backward_topk', '_backward_where', '_backward_while_loop', '_broadcast_backward', '_cond', '_copy', '_copyto', '_crop_assign', '_crop_assign_scalar', '_cvcopyMakeBorder', '_cvimdecode', '_cvimread', '_cvimresize', '_div', '_div_scalar', '_equal', '_equal_scalar', '_eye', '_foreach', '_full', '_grad_add', '_greater', '_greater_equal', '_greater_equal_scalar', '_greater_scalar', '_histogram', '_hypot', '_hypot_scalar', '_identity_with_attr_like_rhs', '_imdecode', '_lesser', '_lesser_equal', '_lesser_equal_scalar', '_lesser_scalar', '_logical_and', '_logical_and_scalar', '_logical_or', '_logical_or_scalar', '_logical_xor', '_logical_xor_scalar', '_maximum', '_maximum_scalar', '_minimum', '_minimum_scalar', '_minus', '_minus_scalar', '_mod', '_mod_scalar', '_mul', '_mul_scalar', '_not_equal', '_not_equal_scalar', '_onehot_encode', '_ones', '_plus', '_plus_scalar', '_power', '_power_scalar', '_random_exponential', '_random_gamma', '_random_generalized_negative_binomial', '_random_negative_binomial', '_random_normal', '_random_poisson', '_random_uniform', '_ravel_multi_index', '_rdiv_scalar', '_rminus_scalar', '_rmod_scalar', '_rnn_param_concat', '_rpower_scalar', '_sample_exponential', '_sample_gamma', '_sample_generalized_negative_binomial', '_sample_multinomial', '_sample_negative_binomial', '_sample_normal', '_sample_poisson', '_sample_uniform', '_sample_unique_zipfian', '_scatter_elemwise_div', '_scatter_minus_scalar', '_scatter_plus_scalar', '_scatter_set_nd', '_set_value', '_shuffle', '_slice_assign', '_slice_assign_scalar', '_square_sum', '_sub', '_unravel_index', '_while_loop', '_zeros', '_zeros_without_dtype']