# --------------------------------------------------------------------------
# ⚠️ WARNING - AUTO-GENERATED CODE - DO NOT EDIT ⚠️
# ⚙️ Generated by 'python -m opgen'
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# --------------------------------------------------------------------------
# pylint: disable=W0221,W0222,R0901,W0237
# mypy: disable-error-code=override
# ruff: noqa: D214, D402, D405, D411, D412, D416
# --------------------------------------------------------------------------

from __future__ import annotations

from typing import Optional, Sequence, Tuple, TypeVar, Union

from onnx import GraphProto, SparseTensorProto, TensorProto
from onnx.defs import get_schema
from typing_extensions import TypeAlias

from onnxscript.onnx_opset._impl.opset22 import Opset22
from onnxscript.onnx_types import (
    BFLOAT16,
    BOOL,
    COMPLEX64,
    COMPLEX128,
    DOUBLE,
    FLOAT,
    FLOAT4E2M1,
    FLOAT8E4M3FN,
    FLOAT8E4M3FNUZ,
    FLOAT8E5M2,
    FLOAT8E5M2FNUZ,
    FLOAT16,
    INT4,
    INT8,
    INT16,
    INT32,
    INT64,
    STRING,
    UINT4,
    UINT8,
    UINT16,
    UINT32,
    UINT64,
)
from onnxscript.values import Op, Opset


class Opset23(Opset22):
    def __new__(cls):
        return Opset.__new__(cls, "", 23)

    T1_Attention = TypeVar("T1_Attention", BFLOAT16, DOUBLE, FLOAT, FLOAT16)

    T2_Attention = TypeVar("T2_Attention", BFLOAT16, DOUBLE, FLOAT, FLOAT16)

    U_Attention = TypeVar(
        "U_Attention",
        BFLOAT16,
        BOOL,
        DOUBLE,
        FLOAT,
        FLOAT16,
        INT16,
        INT32,
        INT64,
        INT8,
        UINT16,
        UINT32,
        UINT64,
        UINT8,
    )

    def Attention(
        self,
        Q: T1_Attention,
        K: T1_Attention,
        V: T2_Attention,
        attn_mask: Optional[U_Attention] = None,
        past_key: Optional[T1_Attention] = None,
        past_value: Optional[T2_Attention] = None,
        *,
        is_causal: int = 0,
        kv_num_heads: Optional[int] = None,
        q_num_heads: Optional[int] = None,
        qk_matmul_output_mode: int = 0,
        scale: Optional[float] = None,
        softcap: float = 0.0,
        softmax_precision: Optional[int] = None,
    ) -> Tuple[T1_Attention, T1_Attention, T2_Attention, T1_Attention]:
        r"""[🌐 Attention(23)](https://onnx.ai/onnx/operators/onnx__Attention.html#attention-23 "Online Documentation")



        Computes scaled dot product attention on query, key and value tensors, using an optional attention mask if passed.

        This operator covers self and cross variants of the attention operation based on sequence lengths of K, Q and V.

        For self attention, `kv_sequence_length` equals to `q_sequence_length`.

        For cross attention, query and key might have different lengths.

        This operator also covers the 3 following variants based on the number of heads:
        1) Multi-headed Attention (MHA): Described in the paper https://arxiv.org/pdf/1706.03762, `q_num_heads = kv_num_heads`.
        2) Group-query Attention (GQA): Described in the paper https://arxiv.org/pdf/2305.13245, `q_num_heads > kv_num_heads`, `q_num_heads % kv_num_heads == 0`.
        3) Multi-query Attention (MQA): Described in the paper https://arxiv.org/pdf/1911.02150, `q_num_heads > kv_num_heads`, `kv_num_heads=1`.

        Attention bias to be added is calculated based on `attn_mask` input and `is_causal attribute`, only one of which can be provided.
        1) If `is_causal` is set to `1`, the attention masking is a lower triangular matrix when the mask is a square matrix. The attention masking has the form of the upper left causal bias due to the alignment.
        2) `attn_mask`: A boolean mask where a value of `True` indicates that the element should take part in attention or a float mask of the same type as query, key, value that is added to the attention score.

        Both past and present state key/values are optional. They shall be used together, and not allowed to use only one of them.
        The following pattern is applied to the Q, K and V inputs after appropriate reshaping of K and V inputs based on sequence lengths and num heads provided:

        ::

              The following pattern is applied by this operator:
                  Q          K          V
                  |          |          |
            Q*sqrt(scale) K*sqrt(scale) |
                  |          |          |
                  |       Transpose     |
                  |          |          |
                  ---MatMul---          |
                        |               |
             at_mask---Add              |
                        |               |
              softcap (if provided)     |
                        |               |
                     Softmax            |
                        |               |
                        -----MatMul------
                               |
                               Y





        Args:
            Q: Query tensor. 4D tensor with shape `(batch_size, q_num_heads,
                q_sequence_length, head_size)` or 3D tensor with shape `(batch_size,
                q_sequence_length, q_hidden_size)`. For cases with a 3D input tensor,
                `q_hidden_size = q_num_heads * head_size`

            K: Key tensor. 4D tensor with shape `(batch_size, kv_num_heads,
                kv_sequence_length, head_size)` or 3D tensor with shape `(batch_size,
                kv_sequence_length, k_hidden_size)`. For cases with a 3D input tensor,
                `k_hidden_size = kv_num_heads * head_size`

            V: Value tensor. 4D tensor with shape `(batch_size, kv_num_heads,
                kv_sequence_length, v_head_size)` or 3D tensor with shape `(batch_size,
                kv_sequence_length, v_hidden_size)`. For cases with a 3D input tensor,
                `v_hidden_size = kv_num_heads * v_head_size`

            attn_mask: (optional) Attention mask. Shape must be broadcastable to 4D
                tensor with shape `(batch_size, q_num_heads, q_sequence_length,
                total_sequence_length)` where `total_sequence_length =
                past_sequence_length + kv_sequence_length.` Two types of masks are
                supported. A boolean mask where a value of `True` indicates that the
                element should take part in attention. Also supports a float mask of the
                same type as query, key, value that is added to the attention score.

            past_key: (optional) past state cache for key with shape `(batch_size,
                kv_num_heads, past_sequence_length, head_size)`

            past_value: (optional) past state cache for value with shape `(batch_size,
                kv_num_heads, past_sequence_length, v_head_size)`

            is_causal: If set to `1`, the attention masking is a lower triangular matrix
                when the mask is a square matrix. The attention masking has the form of
                the upper left causal bias due to the alignment.

            kv_num_heads: Number of heads of key and value. Must be used with 3D inputs
                of Q, K and V.

            q_num_heads: Number of heads of query. Must be used with 3D inputs of Q, K
                and V.

            qk_matmul_output_mode: If set to `0`, qk_matmul_output is the output of qk
                matmul. If set to `1`, qk_matmul_output includes the addition of the
                attention mask to the output of qk matmul. If set to `2`,
                qk_matmul_output is the output after the softcap operation. If set to
                `3`, qk_matmul_output is the output after the softmax operation. Default
                value is 0.

            scale: Scaling factor applied to $Q*K^T$. Default value is
                `1/sqrt(head_size)`. To prevent [numerical
                overflow](https://tinyurl.com/sudb9s96), scale `Q`, `K` by `sqrt(scale)`
                before matmul.

            softcap: Softcap value for attention weights. Default value is 0.

            softmax_precision: The floating-point precision used in softmax computation.
                If softmax precision is not provided, the same precision as the input of
                softmax (Q and K) is used.
        """

        schema = get_schema("Attention", 23, "")
        op = Op(self, "Attention", schema)
        return op(
            *self._prepare_inputs(schema, Q, K, V, attn_mask, past_key, past_value),
            is_causal=is_causal,
            kv_num_heads=kv_num_heads,
            q_num_heads=q_num_heads,
            qk_matmul_output_mode=qk_matmul_output_mode,
            scale=scale,
            softcap=softcap,
            softmax_precision=softmax_precision,
        )

    T1_Cast = TypeVar(
        "T1_Cast",
        BFLOAT16,
        BOOL,
        DOUBLE,
        FLOAT,
        FLOAT16,
        FLOAT4E2M1,
        FLOAT8E4M3FN,
        FLOAT8E4M3FNUZ,
        FLOAT8E5M2,
        FLOAT8E5M2FNUZ,
        INT16,
        INT32,
        INT4,
        INT64,
        INT8,
        STRING,
        UINT16,
        UINT32,
        UINT4,
        UINT64,
        UINT8,
    )

    T2_Cast: TypeAlias = Union[
        BFLOAT16,
        BOOL,
        DOUBLE,
        FLOAT,
        FLOAT16,
        FLOAT4E2M1,
        FLOAT8E4M3FN,
        FLOAT8E4M3FNUZ,
        FLOAT8E5M2,
        FLOAT8E5M2FNUZ,
        INT16,
        INT32,
        INT4,
        INT64,
        INT8,
        STRING,
        UINT16,
        UINT32,
        UINT4,
        UINT64,
        UINT8,
    ]

    def Cast(self, input: T1_Cast, *, saturate: int = 1, to: int) -> T2_Cast:
        r"""[🌐 Cast(23)](https://onnx.ai/onnx/operators/onnx__Cast.html#cast-23 "Online Documentation")


        The operator casts the elements of a given input tensor to a data type
        specified by the 'to' argument and returns an output tensor of the same size in
        the converted type. The 'to' argument must be one of the data types specified
        in the 'DataType' enum field in the TensorProto message.

        Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations
        (e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may
        yield result 100. There are some string literals reserved for special floating-point values;
        "+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively.
        Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly,
        this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors
        to string tensors, plain floating-point representation (such as "314.15926") would be used.
        Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases
        of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior.

        Conversion from a numerical type to any numerical type is always allowed.
        User must be aware of precision loss and value change caused by range difference between two types.
        For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting
        an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type.

        In more detail, the conversion among numerical types should follow these rules
        if the destination type is not a float 8 type.

        * Casting from floating point to:
          * floating point: +/- infinity if OOR (out of range).
          * fixed point: undefined if OOR.
          * bool: +/- 0.0 to False; all else to True.
        * Casting from fixed point to:
          * floating point: +/- infinity if OOR. (+ infinity in the case of uint)
          * fixed point: when OOR, discard higher bits and reinterpret (with respect to two's complement representation for
            signed types). For example, 200 (int16) -> -56 (int8).
          * bool: zero to False; nonzero to True.
        * Casting from bool to:
          * floating point: `{1.0, 0.0}`.
          * fixed point: `{1, 0}`.
          * bool: no change.

        Float 8 type were introduced to speed up the training of
        deep models. By default the conversion of a float *x* obeys
        to the following rules. `[x]` means the value rounded to
        the target mantissa width.

        | x                 | E4M3FN   | E4M3FNUZ | E5M2     | E5M2FNUZ |
        | ----------------- | -------- | -------- | -------- | -------- |
        | 0                 | 0        | 0        | 0        | 0        |
        | -0                | -0       | 0        | -0       | 0        |
        | NaN               | NaN      | NaN      | NaN      | NaN      |
        | Inf               | FLT_MAX  | NaN      | FLT_MAX  | NaN      |
        | -Inf              | -FLT_MAX | NaN      | -FLT_MAX | NaN      |
        | \[x\] > FLT_MAX   | FLT_MAX  | FLT_MAX  | FLT_MAX  | FLT_MAX  |
        | \[x\] \< -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX |
        | else              | RNE      | RNE      | RNE      | RNE      |

        The behavior changes if the parameter 'saturate' is set to False.
        The rules then become:

        | x                 | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ |
        | ----------------- | ------ | -------- | ---- | -------- |
        | 0                 | 0      | 0        | 0    | 0        |
        | -0                | -0     | 0        | -0   | 0        |
        | NaN               | NaN    | NaN      | NaN  | NaN      |
        | -NaN              | -NaN   | NaN      | -NaN | NaN      |
        | Inf               | NaN    | NaN      | Inf  | NaN      |
        | -Inf              | -NaN   | NaN      | -Inf | NaN      |
        | \[x\] > FLT_MAX   | NaN    | NaN      | Inf  | NaN      |
        | \[x\] \< -FLT_MAX | NaN    | NaN      | -Inf | NaN      |
        | else              | RNE    | RNE      | RNE  | RNE      |


        Args:
            input: (differentiable) Input tensor to be cast.

            saturate: The parameter defines how the conversion behaves if an input value
                is out of range of the destination type. It only applies for float 8
                conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz).
                It is true by default. All cases are fully described in two tables
                inserted in the operator description.

            to: The data type to which the elements of the input tensor are cast.
                Strictly must be one of the types from DataType enum in TensorProto
        """

        schema = get_schema("Cast", 23, "")
        op = Op(self, "Cast", schema)
        return op(*self._prepare_inputs(schema, input), saturate=saturate, to=to)

    T1_CastLike = TypeVar(
        "T1_CastLike",
        BFLOAT16,
        BOOL,
        DOUBLE,
        FLOAT,
        FLOAT16,
        FLOAT4E2M1,
        FLOAT8E4M3FN,
        FLOAT8E4M3FNUZ,
        FLOAT8E5M2,
        FLOAT8E5M2FNUZ,
        INT16,
        INT32,
        INT4,
        INT64,
        INT8,
        STRING,
        UINT16,
        UINT32,
        UINT4,
        UINT64,
        UINT8,
    )

    T2_CastLike = TypeVar(
        "T2_CastLike",
        BFLOAT16,
        BOOL,
        DOUBLE,
        FLOAT,
        FLOAT16,
        FLOAT4E2M1,
        FLOAT8E4M3FN,
        FLOAT8E4M3FNUZ,
        FLOAT8E5M2,
        FLOAT8E5M2FNUZ,
        INT16,
        INT32,
        INT4,
        INT64,
        INT8,
        STRING,
        UINT16,
        UINT32,
        UINT4,
        UINT64,
        UINT8,
    )

    def CastLike(
        self, input: T1_CastLike, target_type: T2_CastLike, *, saturate: int = 1
    ) -> T2_CastLike:
        r"""[🌐 CastLike(23)](https://onnx.ai/onnx/operators/onnx__CastLike.html#castlike-23 "Online Documentation")


        The operator casts the elements of a given input tensor (the first input) to
        the same data type as the elements of the second input tensor.
        See documentation of the Cast operator for further details.


        Args:
            input: (differentiable) Input tensor to be cast.

            target_type: (non-differentiable) The (first) input tensor will be cast to
                produce a tensor of the same type as this (second input) tensor.

            saturate: The parameter defines how the conversion behaves if an input value
                is out of range of the destination type. It only applies for float 8
                conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz).
                It is true by default. Please refer to operator Cast description for
                further details.
        """

        schema = get_schema("CastLike", 23, "")
        op = Op(self, "CastLike", schema)
        return op(*self._prepare_inputs(schema, input, target_type), saturate=saturate)

    T_Constant: TypeAlias = Union[
        BFLOAT16,
        BOOL,
        COMPLEX128,
        COMPLEX64,
        DOUBLE,
        FLOAT,
        FLOAT16,
        FLOAT4E2M1,
        FLOAT8E4M3FN,
        FLOAT8E4M3FNUZ,
        FLOAT8E5M2,
        FLOAT8E5M2FNUZ,
        INT16,
        INT32,
        INT4,
        INT64,
        INT8,
        STRING,
        UINT16,
        UINT32,
        UINT4,
        UINT64,
        UINT8,
    ]

    def Constant(
        self,
        *,
        sparse_value: Optional[SparseTensorProto] = None,
        value: Optional[TensorProto] = None,
        value_float: Optional[float] = None,
        value_floats: Optional[Sequence[float]] = None,
        value_int: Optional[int] = None,
        value_ints: Optional[Sequence[int]] = None,
        value_string: Optional[str] = None,
        value_strings: Optional[Sequence[str]] = None,
    ) -> T_Constant:
        r"""[🌐 Constant(23)](https://onnx.ai/onnx/operators/onnx__Constant.html#constant-23 "Online Documentation")


        This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value,
        or value_* must be specified.


        Args:
            sparse_value: The value for the elements of the output tensor in sparse
                format.

            value: The value for the elements of the output tensor.

            value_float: The value for the sole element for the scalar, float32, output
                tensor.

            value_floats: The values for the elements for the 1D, float32, output
                tensor.

            value_int: The value for the sole element for the scalar, int64, output
                tensor.

            value_ints: The values for the elements for the 1D, int64, output tensor.

            value_string: The value for the sole element for the scalar, UTF-8 string,
                output tensor.

            value_strings: The values for the elements for the 1D, UTF-8 string, output
                tensor.
        """

        schema = get_schema("Constant", 23, "")
        op = Op(self, "Constant", schema)
        return op(
            sparse_value=sparse_value,
            value=value,
            value_float=value_float,
            value_floats=value_floats,
            value_int=value_int,
            value_ints=value_ints,
            value_string=value_string,
            value_strings=value_strings,
        )

    T1_ConstantOfShape: TypeAlias = INT64

    T2_ConstantOfShape: TypeAlias = Union[
        BFLOAT16,
        BOOL,
        DOUBLE,
        FLOAT,
        FLOAT16,
        FLOAT4E2M1,
        FLOAT8E4M3FN,
        FLOAT8E4M3FNUZ,
        FLOAT8E5M2,
        FLOAT8E5M2FNUZ,
        INT16,
        INT32,
        INT4,
        INT64,
        INT8,
        UINT16,
        UINT32,
        UINT4,
        UINT64,
        UINT8,
    ]

    def ConstantOfShape(
        self, input: T1_ConstantOfShape, *, value: Optional[TensorProto] = None
    ) -> T2_ConstantOfShape:
        r"""[🌐 ConstantOfShape(23)](https://onnx.ai/onnx/operators/onnx__ConstantOfShape.html#constantofshape-23 "Online Documentation")


        Generate a tensor with given value and shape.


        Args:
            input: 1D tensor. The shape of the expected output tensor. If empty tensor
                is given, the output would be a scalar. All values must be >= 0.

            value: (Optional) The value of the output elements.Should be a one-element
                tensor. If not specified, it defaults to a tensor of value 0 and
                datatype float32
        """

        schema = get_schema("ConstantOfShape", 23, "")
        op = Op(self, "ConstantOfShape", schema)
        return op(*self._prepare_inputs(schema, input), value=value)

    T1_DequantizeLinear = TypeVar(
        "T1_DequantizeLinear",
        FLOAT4E2M1,
        FLOAT8E4M3FN,
        FLOAT8E4M3FNUZ,
        FLOAT8E5M2,
        FLOAT8E5M2FNUZ,
        INT16,
        INT32,
        INT4,
        INT8,
        UINT16,
        UINT4,
        UINT8,
    )

    T2_DequantizeLinear = TypeVar("T2_DequantizeLinear", BFLOAT16, FLOAT, FLOAT16)

    T3_DequantizeLinear: TypeAlias = Union[BFLOAT16, FLOAT, FLOAT16]

    def DequantizeLinear(
        self,
        x: T1_DequantizeLinear,
        x_scale: T2_DequantizeLinear,
        x_zero_point: Optional[T1_DequantizeLinear] = None,
        *,
        axis: int = 1,
        block_size: int = 0,
        output_dtype: int = 0,
    ) -> T3_DequantizeLinear:
        r"""[🌐 DequantizeLinear(23)](https://onnx.ai/onnx/operators/onnx__DequantizeLinear.html#dequantizelinear-23 "Online Documentation")


        The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the
        full-precision tensor. The dequantization formula is `y = (x - x_zero_point) * x_scale`. `x_scale` and `x_zero_point`
        must have the same shape, determining the quantization's granularity: a scalar for per-tensor/per-layer quantization,
        a 1-D tensor for per-axis quantization, or have a rank identical to the input for blocked quantization.
        See QuantizeLinear for details on quantization granularity.

        `x_zero_point` and `x` must have the same type. `x` and `y` must have the same shape. In the case of dequantizing
        `int32`, there's no zero point (zero point is supposed to be 0).
        `zero-point` is usually not used in the case of float8 and 4-bit types quantization, but the dequantization formula remains the same
        for consistency. The output type is determined by the attribute `output_dtype`. If `output_dtype` is not supplied then the output type
        is the same as `x_scale`. The output type also determines the precision of the multiplication operation.



        Args:
            x: N-D quantized input tensor to be de-quantized.

            x_scale: Scale for input `x`. For per-tensor/layer dequantization the scale
                is a scalar, for per per-axis dequantization it is a 1-D Tensor and for
                blocked dequantization it has the same shape as the input, except for
                one dimension in which blocking is performed.

            x_zero_point: (optional) Zero point for input `x`. Shape must match x_scale.
                It's optional. Zero point is 0 when it's not specified.

            axis: (Optional) The axis of the dequantizing dimension of the input tensor.
                Used for per-axis and blocked quantization. Negative value means
                counting dimensions from the back. Accepted range is `[-r, r-1]` where
                `r = rank(input)`.

            block_size: (Optional) The size of the quantization block (number of times
                every scale is replicated). Used only for blocked quantization. The
                block size is a positive integer. Given `x` shape `(D0, ..., Di, ...,
                Dn)`, `y_scale` shape `(S0, ... Si, ...Sn)` and `axis=i`, the accepted
                range is `[ceil(Di/Si), ceil(Di/(Si-1))-1]`

            output_dtype: (Optional) The output data type. If not supplied, the output
                data type is inferred from `x_scale` data type (`T2`)
        """

        schema = get_schema("DequantizeLinear", 23, "")
        op = Op(self, "DequantizeLinear", schema)
        return op(
            *self._prepare_inputs(schema, x, x_scale, x_zero_point),
            axis=axis,
            block_size=block_size,
            output_dtype=output_dtype,
        )

    T_Flatten = TypeVar(
        "T_Flatten",
        BFLOAT16,
        BOOL,
        COMPLEX128,
        COMPLEX64,
        DOUBLE,
        FLOAT,
        FLOAT16,
        FLOAT4E2M1,
        FLOAT8E4M3FN,
        FLOAT8E4M3FNUZ,
        FLOAT8E5M2,
        FLOAT8E5M2FNUZ,
        INT16,
        INT32,
        INT4,
        INT64,
        INT8,
        STRING,
        UINT16,
        UINT32,
        UINT4,
        UINT64,
        UINT8,
    )

    def Flatten(self, input: T_Flatten, *, axis: int = 1) -> T_Flatten:
        r"""[🌐 Flatten(23)](https://onnx.ai/onnx/operators/onnx__Flatten.html#flatten-23 "Online Documentation")


        Flattens the input tensor into a 2D matrix. If input tensor has shape
        (d_0, d_1, ... d_n) then the output will have shape
        (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).


        Args:
            input: (differentiable) A tensor of rank >= axis.

            axis: Indicate up to which input dimensions (exclusive) should be flattened
                to the outer dimension of the output. The value for axis must be in the
                range [-r, r], where r is the rank of the input tensor. Negative value
                means counting dimensions from the back. When axis = 0, the shape of the
                output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input
                tensor is (d_0, d_1, ... d_n).
        """

        schema = get_schema("Flatten", 23, "")
        op = Op(self, "Flatten", schema)
        return op(*self._prepare_inputs(schema, input), axis=axis)

    V_Identity = TypeVar(
        "V_Identity",
        Optional[Sequence[BOOL]],
        Optional[Sequence[COMPLEX128]],
        Optional[Sequence[COMPLEX64]],
        Optional[Sequence[DOUBLE]],
        Optional[Sequence[FLOAT]],
        Optional[Sequence[FLOAT16]],
        Optional[Sequence[INT16]],
        Optional[Sequence[INT32]],
        Optional[Sequence[INT64]],
        Optional[Sequence[INT8]],
        Optional[Sequence[STRING]],
        Optional[Sequence[UINT16]],
        Optional[Sequence[UINT32]],
        Optional[Sequence[UINT64]],
        Optional[Sequence[UINT8]],
        Optional[BOOL],
        Optional[COMPLEX128],
        Optional[COMPLEX64],
        Optional[DOUBLE],
        Optional[FLOAT],
        Optional[FLOAT16],
        Optional[INT16],
        Optional[INT32],
        Optional[INT64],
        Optional[INT8],
        Optional[STRING],
        Optional[UINT16],
        Optional[UINT32],
        Optional[UINT64],
        Optional[UINT8],
        Sequence[BOOL],
        Sequence[COMPLEX128],
        Sequence[COMPLEX64],
        Sequence[DOUBLE],
        Sequence[FLOAT],
        Sequence[FLOAT16],
        Sequence[INT16],
        Sequence[INT32],
        Sequence[INT64],
        Sequence[INT8],
        Sequence[STRING],
        Sequence[UINT16],
        Sequence[UINT32],
        Sequence[UINT64],
        Sequence[UINT8],
        BFLOAT16,
        BOOL,
        COMPLEX128,
        COMPLEX64,
        DOUBLE,
        FLOAT,
        FLOAT16,
        FLOAT4E2M1,
        FLOAT8E4M3FN,
        FLOAT8E4M3FNUZ,
        FLOAT8E5M2,
        FLOAT8E5M2FNUZ,
        INT16,
        INT32,
        INT4,
        INT64,
        INT8,
        STRING,
        UINT16,
        UINT32,
        UINT4,
        UINT64,
        UINT8,
    )

    def Identity(self, input: V_Identity) -> V_Identity:
        r"""[🌐 Identity(23)](https://onnx.ai/onnx/operators/onnx__Identity.html#identity-23 "Online Documentation")

        Identity operator

        Args:
            input: (differentiable) Input tensor
        """

        schema = get_schema("Identity", 23, "")
        op = Op(self, "Identity", schema)
        return op(*self._prepare_inputs(schema, input))

    B_If: TypeAlias = BOOL

    V_If: TypeAlias = Union[
        None,
        Sequence[BFLOAT16],
        Sequence[BOOL],
        Sequence[COMPLEX128],
        Sequence[COMPLEX64],
        Sequence[DOUBLE],
        Sequence[FLOAT],
        Sequence[FLOAT16],
        Sequence[INT16],
        Sequence[INT32],
        Sequence[INT64],
        Sequence[INT8],
        Sequence[STRING],
        Sequence[UINT16],
        Sequence[UINT32],
        Sequence[UINT64],
        Sequence[UINT8],
        BFLOAT16,
        BOOL,
        COMPLEX128,
        COMPLEX64,
        DOUBLE,
        FLOAT,
        FLOAT16,
        FLOAT4E2M1,
        FLOAT8E4M3FN,
        FLOAT8E4M3FNUZ,
        FLOAT8E5M2,
        FLOAT8E5M2FNUZ,
        INT16,
        INT32,
        INT4,
        INT64,
        INT8,
        STRING,
        UINT16,
        UINT32,
        UINT4,
        UINT64,
        UINT8,
        Sequence[FLOAT4E2M1],
        Sequence[FLOAT8E4M3FN],
        Sequence[FLOAT8E4M3FNUZ],
        Sequence[FLOAT8E5M2],
        Sequence[FLOAT8E5M2FNUZ],
        Sequence[INT4],
        Sequence[UINT4],
    ]

    def If(self, cond: B_If, *, else_branch: GraphProto, then_branch: GraphProto) -> V_If:
        r"""[🌐 If(23)](https://onnx.ai/onnx/operators/onnx__If.html#if-23 "Online Documentation")

        If conditional

        Args:
            cond: Condition for the if. The tensor must contain a single element.

            else_branch: Graph to run if condition is false. Has N outputs: values you
                wish to be live-out to the enclosing scope. The number of outputs must
                match the number of outputs in the then_branch.

            then_branch: Graph to run if condition is true. Has N outputs: values you
                wish to be live-out to the enclosing scope. The number of outputs must
                match the number of outputs in the else_branch.
        """

        schema = get_schema("If", 23, "")
        op = Op(self, "If", schema)
        return op(
            *self._prepare_inputs(schema, cond),
            else_branch=else_branch,
            then_branch=then_branch,
        )

    I_Loop: TypeAlias = INT64

    B_Loop: TypeAlias = BOOL

    V_Loop = TypeVar(
        "V_Loop",
        Optional[Sequence[BFLOAT16]],
        Optional[Sequence[BOOL]],
        Optional[Sequence[COMPLEX128]],
        Optional[Sequence[COMPLEX64]],
        Optional[Sequence[DOUBLE]],
        Optional[Sequence[FLOAT]],
        Optional[Sequence[FLOAT16]],
        Optional[Sequence[INT16]],
        Optional[Sequence[INT32]],
        Optional[Sequence[INT64]],
        Optional[Sequence[INT8]],
        Optional[Sequence[STRING]],
        Optional[Sequence[UINT16]],
        Optional[Sequence[UINT32]],
        Optional[Sequence[UINT64]],
        Optional[Sequence[UINT8]],
        Optional[BFLOAT16],
        Optional[BOOL],
        Optional[COMPLEX128],
        Optional[COMPLEX64],
        Optional[DOUBLE],
        Optional[FLOAT],
        Optional[FLOAT16],
        Optional[FLOAT4E2M1],
        Optional[FLOAT8E4M3FN],
        Optional[FLOAT8E4M3FNUZ],
        Optional[FLOAT8E5M2],
        Optional[FLOAT8E5M2FNUZ],
        Optional[INT16],
        Optional[INT32],
        Optional[INT4],
        Optional[INT64],
        Optional[INT8],
        Optional[STRING],
        Optional[UINT16],
        Optional[UINT32],
        Optional[UINT4],
        Optional[UINT64],
        Optional[UINT8],
        Sequence[BFLOAT16],
        Sequence[BOOL],
        Sequence[COMPLEX128],
        Sequence[COMPLEX64],
        Sequence[DOUBLE],
        Sequence[FLOAT],
        Sequence[FLOAT16],
        Sequence[FLOAT4E2M1],
        Sequence[FLOAT8E4M3FN],
        Sequence[FLOAT8E4M3FNUZ],
        Sequence[FLOAT8E5M2],
        Sequence[FLOAT8E5M2FNUZ],
        Sequence[INT16],
        Sequence[INT32],
        Sequence[INT4],
        Sequence[INT64],
        Sequence[INT8],
        Sequence[STRING],
        Sequence[UINT16],
        Sequence[UINT32],
        Sequence[UINT4],
        Sequence[UINT64],
        Sequence[UINT8],
        BFLOAT16,
        BOOL,
        COMPLEX128,
        COMPLEX64,
        DOUBLE,
        FLOAT,
        FLOAT16,
        FLOAT4E2M1,
        FLOAT8E4M3FN,
        FLOAT8E4M3FNUZ,
        FLOAT8E5M2,
        FLOAT8E5M2FNUZ,
        INT16,
        INT32,
        INT4,
        INT64,
        INT8,
        STRING,
        UINT16,
        UINT32,
        UINT4,
        UINT64,
        UINT8,
    )

    def Loop(
        self,
        M: Optional[I_Loop],
        cond: Optional[B_Loop],
        *v_initial: V_Loop,
        body: GraphProto,
    ) -> V_Loop:
        r"""[🌐 Loop(23)](https://onnx.ai/onnx/operators/onnx__Loop.html#loop-23 "Online Documentation")


        Generic Looping construct. This loop has multiple termination conditions:

        1) Trip count. Iteration count specified at runtime. Set by
           specifying the input M. Optional. Set to empty string to omit.
           Note that a static trip count (specified at graph construction time) can be
           specified by passing in a constant node for input M.
        2) Loop termination condition. This is an input to the op that determines
           whether to run the first iteration and also a loop-carried dependency for
           the body graph. The body graph must yield a value for the condition variable,
           whether this input is provided or not.

        This table summarizes the operating modes of this operator with equivalent
        C-style code:

        Operator inputs defined as (max_trip_count, condition_var).

        * input ("", ""):
                for (int i=0; ; ++i) {
                  cond = ... // Note this value is ignored, but is required in the body
                }

        * input ("", cond) // Note this is analogous to a while loop
                bool cond = ...;
                for (int i=0; cond; ++i) {
                  cond = ...;
                }

        * input ("", 1) // Note this is analogous to a do-while loop
                bool cond = true
                for (int i=0; cond; ++i) {
                  cond = ...;
                }

        * input (trip_count, "") // Note this is analogous to a for loop
                int trip_count = ...
                for (int i=0; i < trip_count; ++i) {
                  cond = ...; // ignored
                }

        * input (trip_count, cond)
                int trip_count = ...;
                bool cond = ...;
                for (int i=0; i < trip_count && cond; ++i) {
                  cond = ...;
                }


        *Sample usage - cond as well as trip count*

            graph predict-net {
              %a = Constant[value = <Scalar Tensor [3]>]()
              %b = Constant[value = <Scalar Tensor [6]>]()
              %keepgoing = Constant[value = <Scalar Tensor [1]>]()
              %max_trip_count = Constant[value = <Scalar Tensor [10]>]()
              %keepgoing_out, %b_out, %user_defined_vals = Loop[body = <graph body-net>](%max_trip_count, %keepgoing, %b)
              return
            }

            graph body-net (
              %i[INT32, scalar]           // iteration number
              %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used
              %b_in[INT32, scalar]        // incoming value of loop-carried-dependency b
            ) {
              %my_local = Add(%a, %b_in)
              %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b
              %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition
              %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated
              return %keepgoing_out, %b_out, %user_defined_val
            }

        *Sample equivalent C code*

            {
              /* User-defined code (enclosing scope) */
              int a = 3, b = 6;
              bool keepgoing = true; // Analogous to input cond
              /* End user-defined code */

              /* Implicitly-defined code */
              const int max_trip_count = 10; // Analogous to input M
              int user_defined_vals[]; // Imagine this is resizable
              /* End implicitly-defined code */
              /* initialize loop-carried variables and scan-output variables */
              bool keepgoing_out = keepgoing
              int b_out = b

              for (int i=0; i < max_trip_count && keepgoing_out; ++i) {
                /* Implicitly-defined code: bind actual parameter values
                   to formal parameter variables of loop-body */
                bool keepgoing_in = keepgoing_out;
                bool b_in = b_out;

                /* User-defined code (loop body) */
                int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine
                b_out = a - b_in;
                keepgoing_out = my_local > b_out;
                user_defined_val = b_in + b_in; // b_in and b_out are different variables
                /* End user-defined code */

                /* Implicitly defined-code */
                user_defined_vals[i] = user_defined_val // accumulate scan-output values
              }
              // int t = my_local; // Can't do this. my_local is not accessible here.

              // The values below are bound to the output variables of the loop and therefore accessible
              // b_out; user_defined_vals; keepgoing_out;
            }

        There are several things of note in this code snippet:

        1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can
           be referenced in the inputs of the loop.
        2) Any values computed in the loop body that needs to be used in a subsequent
           iteration or after the loop are modelled using a pair of variables in the loop-body,
           consisting of an input variable (eg., b_in) and an output variable (eg., b_out).
           These are referred to as loop-carried dependences. The loop operation node
           supplies the input value of the input variable for the first iteration, and
           returns the output value of the output variable produced by the final
           iteration.
        3) Scan_output variables are used to implicitly concatenate values computed across
           all the iterations. In the above example, the value of user_defined_val computed
           over all iterations are concatenated and returned as the value of user_defined_vals
           after the loop.
        4) Values created in the body cannot be accessed in the enclosing scope,
           except using the mechanism described above.

        Note that the semantics of this op support "diagonal" or "wavefront" execution.
        (See Step 3 here for an example:
        https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/).
        Frontends should emit multi-layer RNNs as a series of While operators (with
        time being the inner looping dimension), with each successive layer consuming
        the scan_outputs from the previous layer, possibly going through several
        point-wise operators (e.g. dropout, residual connections, linear layer).

        The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order.


        Args:
            M: (optional) A maximum trip-count for the loop specified at runtime.
                Optional. Pass empty string to skip.

            cond: (optional) A boolean termination condition. Optional. Pass empty
                string to skip.

            v_initial: (variadic, heterogeneous) The initial values of any loop-carried
                dependencies (values that change across loop iterations)

            body: The graph run each iteration. It has 2+N inputs: (iteration_num,
                condition, loop carried dependencies...). It has 1+N+K outputs:
                (condition, loop carried dependencies..., scan_outputs...). Each
                scan_output is created by concatenating the value of the specified
                output value at the end of each iteration of the loop. It is an error if
                the dimensions or data type of these scan_outputs change across loop
                iterations.
        """

        schema = get_schema("Loop", 23, "")
        op = Op(self, "Loop", schema)
        return op(*self._prepare_inputs(schema, M, cond, *v_initial), body=body)

    T_Pad = TypeVar(
        "T_Pad",
        BFLOAT16,
        BOOL,
        COMPLEX128,
        COMPLEX64,
        DOUBLE,
        FLOAT,
        FLOAT16,
        FLOAT4E2M1,
        FLOAT8E4M3FN,
        FLOAT8E4M3FNUZ,
        FLOAT8E5M2,
        FLOAT8E5M2FNUZ,
        INT16,
        INT32,
        INT4,
        INT64,
        INT8,
        STRING,
        UINT16,
        UINT32,
        UINT4,
        UINT64,
        UINT8,
    )

    Tind_Pad = TypeVar("Tind_Pad", INT32, INT64)

    def Pad(
        self,
        data: T_Pad,
        pads: INT64,
        constant_value: Optional[T_Pad] = None,
        axes: Optional[Tind_Pad] = None,
        *,
        mode: str = "constant",
    ) -> T_Pad:
        r"""[🌐 Pad(23)](https://onnx.ai/onnx/operators/onnx__Pad.html#pad-23 "Online Documentation")


        Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`,
        a padded tensor (`output`) is generated.

        The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`):

        1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False)

        2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis

        3) `edge` - pads with the edge values of array

        4) `wrap` - wrap-around padding as if the data tensor forms a torus


        Example 1 (`constant` mode):

        Insert 0 pads to the beginning of the second dimension.

        ::

            data = [
                [1.0, 1.2],
                [2.3, 3.4],
                [4.5, 5.7],
            ]

            pads = [0, 2, 0, 0]

            mode = 'constant'

            constant_value = 0.0

            output = [
                [0.0, 0.0, 1.0, 1.2],
                [0.0, 0.0, 2.3, 3.4],
                [0.0, 0.0, 4.5, 5.7],
            ]



        Example 2 (`reflect` mode):

        ::

            data = [
                [1.0, 1.2],
                [2.3, 3.4],
                [4.5, 5.7],
            ]

            pads = [0, 2, 0, 0]

            mode = 'reflect'

            output = [
                [1.0, 1.2, 1.0, 1.2],
                [2.3, 3.4, 2.3, 3.4],
                [4.5, 5.7, 4.5, 5.7],
            ]



        Example 3 (`edge` mode):

        ::

            data = [
                [1.0, 1.2],
                [2.3, 3.4],
                [4.5, 5.7],
            ]

            pads = [0, 2, 0, 0]

            mode = 'edge'

            output = [
                [1.0, 1.0, 1.0, 1.2],
                [2.3, 2.3, 2.3, 3.4],
                [4.5, 4.5, 4.5, 5.7],
            ]



        Example 4 (`wrap` mode):

        ::

            data = [
                [1.0, 1.2],
                [2.3, 3.4],
                [4.5, 5.7],
            ]

            pads = [2, 1, 1, 1]

            mode = 'wrap'

            output = [
                [3.4, 2.3, 3.4, 2.3],
                [5.7, 4.5, 5.7, 4.5],
                [1.2, 1.0, 1.2, 1.0],
                [3.4, 2.3, 3.4, 2.3],
                [5.7, 4.5, 5.7, 4.5],
                [1.2, 1.0, 1.2, 1.0],
            ]




        Args:
            data: (differentiable) Input tensor.

            pads: (non-differentiable) Tensor of integers indicating the number of
                padding elements to add or remove (if negative) at the beginning and end
                of each axis. For 2D input tensor, it is the number of pixels. `pads`
                should be a 1D tensor of shape [2 * num_axes] where `num_axes` refers to
                the number of elements in the `axes` input or the input rank if `axes`
                are not provided explicitly. `pads` format should be: [x1_begin,
                x2_begin, ..., x1_end, x2_end,...], where xi_begin is the number of pad
                values added at the beginning of axis `axes[i]` and xi_end, the number
                of pad values added at the end of axis `axes[i]`.

            constant_value: (optional, non-differentiable) (Optional) A scalar value to
                be used if the mode chosen is `constant` (by default it is 0, empty
                string or False).

            axes: (optional, non-differentiable) 1-D tensor of axes that `pads` apply
                to. Negative value means counting dimensions from the back. Accepted
                range is [-r, r-1] where r = rank(data). Behavior is undefined if an
                axis is repeated. If not provided, all axes are assumed (`[0, 1, ...,
                input_rank-1]`).

            mode: Supported modes: `constant`(default), `reflect`, `edge`, `wrap`
        """

        schema = get_schema("Pad", 23, "")
        op = Op(self, "Pad", schema)
        return op(*self._prepare_inputs(schema, data, pads, constant_value, axes), mode=mode)

    T1_QuantizeLinear = TypeVar("T1_QuantizeLinear", BFLOAT16, FLOAT, FLOAT16, INT32)

    T2_QuantizeLinear = TypeVar("T2_QuantizeLinear", BFLOAT16, FLOAT, FLOAT16, INT32)

    T3_QuantizeLinear = TypeVar(
        "T3_QuantizeLinear",
        FLOAT4E2M1,
        FLOAT8E4M3FN,
        FLOAT8E4M3FNUZ,
        FLOAT8E5M2,
        FLOAT8E5M2FNUZ,
        INT16,
        INT4,
        INT8,
        UINT16,
        UINT4,
        UINT8,
    )

    def QuantizeLinear(
        self,
        x: T1_QuantizeLinear,
        y_scale: T2_QuantizeLinear,
        y_zero_point: Optional[T3_QuantizeLinear] = None,
        *,
        axis: int = 1,
        block_size: int = 0,
        output_dtype: int = 0,
        precision: int = 0,
        saturate: int = 1,
    ) -> T3_QuantizeLinear:
        r"""[🌐 QuantizeLinear(23)](https://onnx.ai/onnx/operators/onnx__QuantizeLinear.html#quantizelinear-23 "Online Documentation")


        The linear quantization operator consumes a high-precision tensor, a scale, and a zero point to compute the
        low-precision/quantized tensor. The scale factor and zero point must have the same shape, determining the quantization
        granularity. The quantization formula is `y = saturate((x / y_scale) + y_zero_point)`.

        Saturation is done according to:
        - uint16: [0, 65535]
        - int16: [-32768, 32767]
        - uint8: [0, 255]
        - int8: [-128, 127]
        - uint4: [0, 15]
        - int4: [-8, 7]

        For `(x / y_scale)`, it rounds to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details.

        `y_zero_point` and `y` must have the same type. `y_zero_point` is usually not used for quantization to float8 and 4bit types, but the quantization
        formula remains the same for consistency, and the type of the attribute `y_zero_point` still determines the quantization type.
        `x` and `y_scale` are allowed to have different types. The type of `y_scale` determines the precision of the division operation between `x` and
        `y_scale`, unless the `precision` attribute is specified.

        There are three supported quantization granularities, determined by the shape of `y_scale`.
        In all cases, `y_zero_point` must have the same shape as `y_scale`.
        - Per-tensor (per-layer) quantization: `y_scale` is a scalar.
        - Per-axis quantization: The scale must be a 1-D tensor, with the length of the quantization axis. For an input shape
         `(D0, ..., Di, ..., Dn)` and `axis=i`, `y_scale` is a 1-D tensor of length `Di`.
        - Blocked quantization: The scale's shape is identical to the input's shape, except for one dimension, in which
          blocking is performed. Given `x` shape `(D0, ..., Di, ..., Dn)`, `axis=i`, and block size `B`: `y_scale` shape is
          `(D0, ..., ceil(Di/B), ..., Dn)`.


        Args:
            x: N-D full precision Input tensor to be quantized.

            y_scale: Scale for doing quantization to get `y`. For per-tensor/layer
                quantization the scale is a scalar, for per-axis quantization it is a
                1-D Tensor and for blocked quantization it has the same shape as the
                input, except for one dimension in which blocking is performed.

            y_zero_point: (optional) Zero point for doing quantization to get `y`. Shape
                must match `y_scale`.Default is uint8 with zero point of 0 if it's not
                specified.

            axis: (Optional) The axis of the dequantizing dimension of the input tensor.
                Used only for per-axis and blocked quantization. Negative value means
                counting dimensions from the back. Accepted range is `[-r, r-1]` where
                `r = rank(input)`. When the rank of the input is 1, per-tensor
                quantization is applied, rendering the axis unnecessary in this
                scenario.

            block_size: (Optional) The size of the quantization block (number of times
                every scale is replicated). Used only for blocked quantization. The
                block size is a positive integer. Given `x` shape `(D0, ..., Di, ...,
                Dn)`, `y_scale` shape `(S0, ... Si, ...Sn)` and `axis=i`, the accepted
                range is `[ceil(Di/Si), ceil(Di/(Si-1))-1]`

            output_dtype: (Optional) The output data type. If not supplied, the output
                data type is inferred from `y_zero_point` data type (`T3`). If neither
                `output_dtype` nor `y_zero_point` are supplied, output data type is
                uint8. If both `output_dtype` and `y_zero_point` are specified,
                `output_dtype` must be `T3`.

            precision: (Optional) The precision of the division operation between `x`
                and `y_scale`. If not provided, it will be the same as the type of
                `y_scale`.

            saturate: The parameter defines how the conversion behaves if an input value
                is out of range of the destination type. It only applies for float 8
                quantization (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz).
                It is true by default. All cases are fully described in two tables
                inserted in the operator description.
        """

        schema = get_schema("QuantizeLinear", 23, "")
        op = Op(self, "QuantizeLinear", schema)
        return op(
            *self._prepare_inputs(schema, x, y_scale, y_zero_point),
            axis=axis,
            block_size=block_size,
            output_dtype=output_dtype,
            precision=precision,
            saturate=saturate,
        )

    T_RMSNormalization = TypeVar("T_RMSNormalization", BFLOAT16, DOUBLE, FLOAT, FLOAT16)

    V_RMSNormalization = TypeVar("V_RMSNormalization", BFLOAT16, DOUBLE, FLOAT, FLOAT16)

    def RMSNormalization(
        self,
        X: T_RMSNormalization,
        scale: V_RMSNormalization,
        *,
        axis: int = -1,
        epsilon: float = 9.999999747378752e-06,
        stash_type: int = 1,
    ) -> V_RMSNormalization:
        r"""[🌐 RMSNormalization(23)](https://onnx.ai/onnx/operators/onnx__RMSNormalization.html#rmsnormalization-23 "Online Documentation")


              This is RMS normalization defined in ONNX as function as described in the paper https://arxiv.org/pdf/1910.07467.
              The overall computation can be split into two stages. The root mean squared norm is taken over the last D dimensions,
              where D is the dimension of normalized_shape. For example, if normalized_shape is (3, 5) (a 2-dimensional shape),
              the rms norm is computed over the last 2 dimensions of the input. The computation required by standardization can be
              described by the following equations.
              ```
              XSquared = Mul(X, X)
              XSquaredMean = ReduceMean<axes=normalized_axes>(XSquared)
              MeanSquareEpsilon = Add(XSquaredMean, epsilon)
              RMS = Sqrt(MeanSquareEpsilon)
              Normalized = Div(X, RMS)
              ```
              where `normalized_axes` is `[axis, ..., rank of X - 1]`. The variables `RMS` stand for root mean square,
              Depending on `stash_type` attribute, the actual computation
              must happen in different floating-point precision.
              For example, if `stash_type` is 1, this operator casts
              all input variables to 32-bit float, perform the computation, and
              finally cast `Normalized` back to the original type of `X`.
              The second stage then scales the outcome of the first stage using:
              ```
              Y= Mul(Normalized, Scale)
              ```
              Let `d[i]` indicate the i-th dimension of `X`.
              If `X`'s shape is `[d[0], ..., d[axis-1], d[axis], ..., d[rank-1]]`,
              the shape of `RMS` is `[d[0], ..., d[axis-1], 1, ..., 1]`.
              `Y` and `X` have the same shape. This operator supports unidirectional broadcasting
              (`Scale` should be unidirectional broadcastable to tensor `X`);
              for more details please check `Broadcasting in ONNX <https://github.com/onnx/onnx/blob/master/docs/Broadcasting.md>`_.


        Args:
            X: The input tensor to be normalized. In general, the shape is (D1, D2, ...
                , Dn) for n-dimensional data, where the root mean squared norm is taken
                over the last D dimensions, D is determined by the axis attribute.

            scale: Scale tensor. Scale tensor shape should be broadcastable to the
                normalized shape.

            axis: The first normalization dimension. If rank(X) is r, axis' allowed
                range is [-r, r). Negative value means counting dimensions from the
                back.

            epsilon: The epsilon value to use to avoid division by zero.

            stash_type: The floating-point precision used in stage one of the
                computation.
        """

        schema = get_schema("RMSNormalization", 23, "")
        op = Op(self, "RMSNormalization", schema)
        return op(
            *self._prepare_inputs(schema, X, scale),
            axis=axis,
            epsilon=epsilon,
            stash_type=stash_type,
        )

    T_Reshape = TypeVar(
        "T_Reshape",
        BFLOAT16,
        BOOL,
        COMPLEX128,
        COMPLEX64,
        DOUBLE,
        FLOAT,
        FLOAT16,
        FLOAT4E2M1,
        FLOAT8E4M3FN,
        FLOAT8E4M3FNUZ,
        FLOAT8E5M2,
        FLOAT8E5M2FNUZ,
        INT16,
        INT32,
        INT4,
        INT64,
        INT8,
        STRING,
        UINT16,
        UINT32,
        UINT4,
        UINT64,
        UINT8,
    )

    def Reshape(self, data: T_Reshape, shape: INT64, *, allowzero: int = 0) -> T_Reshape:
        r"""[🌐 Reshape(23)](https://onnx.ai/onnx/operators/onnx__Reshape.html#reshape-23 "Online Documentation")


        Reshape the input tensor similar to numpy.reshape.
        First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor.
        At most one dimension of the new shape can be -1. In this case, the value is
        inferred from the size of the tensor and the remaining dimensions. A dimension
        could also be 0, in which case the actual dimension value is unchanged (i.e. taken
        from the input tensor). If 'allowzero' is set, and the new shape includes 0, the
        dimension will be set explicitly to zero (i.e. not taken from input tensor).
        Shape (second input) could be an empty shape, which means converting to a scalar.
        The input tensor's shape and the output tensor's shape are required to have the same number of elements.

        If the attribute 'allowzero' is set, it is invalid for the specified shape to
        contain both a zero value and -1, as the value of the dimension corresponding
        to -1 cannot be determined uniquely.


        Args:
            data: (differentiable) An input tensor.

            shape: (non-differentiable) Specified shape for output.

            allowzero: (Optional) By default, when any value in the 'shape' input is
                equal to zero the corresponding dimension value is copied from the input
                tensor dynamically. allowzero=1 indicates that if any value in the
                'shape' input is set to zero, the zero value is honored, similar to
                NumPy.
        """

        schema = get_schema("Reshape", 23, "")
        op = Op(self, "Reshape", schema)
        return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)

    T_RotaryEmbedding = TypeVar("T_RotaryEmbedding", BFLOAT16, FLOAT, FLOAT16)

    M_RotaryEmbedding: TypeAlias = INT64

    def RotaryEmbedding(
        self,
        X: T_RotaryEmbedding,
        cos_cache: T_RotaryEmbedding,
        sin_cache: T_RotaryEmbedding,
        position_ids: Optional[M_RotaryEmbedding] = None,
        *,
        interleaved: int = 0,
        num_heads: Optional[int] = None,
        rotary_embedding_dim: int = 0,
    ) -> T_RotaryEmbedding:
        r"""[🌐 RotaryEmbedding(23)](https://onnx.ai/onnx/operators/onnx__RotaryEmbedding.html#rotaryembedding-23 "Online Documentation")


        RotaryEmbedding is the implementation of rotary positional embeddings (RoPE) based on the paper https://arxiv.org/pdf/2104.09864.
        The key advantage of RoPE is that it allows the model to understand both the absolute position of a token and the relative distances
        between tokens. This is achieved through a rotational mechanism where the extent of rotation is computed based on the token's absolute position (position_ids).

        The rotational mechanism is defined by sine and cosine functions that are used to represent the rotation angles.
        For each token in the sequence, its positional embedding is computed by rotating its embedding vector. This is done by splitting the
        embedding vector either into two halves or interleaving every alternate token and applying the rotation matrix to each half of the embedding vector.
        The rotation matrix is parameterized by the token's position in the sequence. The rotated halves of the embedding vector are concatenated
        to form the final positional embedding for each token. The rotated positional embeddings are used in the self-attention mechanism.
        The rotation ensures that the model captures both absolute and relative positional information.

        Rotary embeddings are defined using the following algorithm:

        ::

            def compute_rotary_embedding(
                input,
                position_ids,
                sin_cache,
                cos_cache,
                interleaved=0,
                rotary_embedding_dim=0,
                num_heads=0,
            ):
                # First ensure input to be processed has shape [batch_size, seq_len, num_heads, head_size]
                if len(input.shape) == 4:
                    input = np.transpose(input, (0, 2, 1, 3))
                batch_size = input.shape[0]
                sequence_length = input.shape[1]
                if len(input.shape) == 3:
                    hidden_size = input.shape[2]
                    assert num_heads != 0
                    head_size = int(hidden_size / num_heads)
                    new_shape = [batch_size, sequence_length, num_heads, head_size]
                    input = np.reshape(input, new_shape)
                assert len(input.shape) == 4
                head_size = input.shape[3]

                # Fully or partially perform rotation on input based on rotary_embedding_dim attribute
                if rotary_embedding_dim == 0:
                    # If rotary_embedding_dim not provided, perform full rotation by using head_size
                    rotary_embedding_dim = head_size
                x_rotate = input[:, :, :, :rotary_embedding_dim]
                x_not_rotate = input[:, :, :, rotary_embedding_dim:]
                rotary_embedding_dim_half = int(rotary_embedding_dim / 2)

                # Retrieve sin and cos caches using position ids
                if position_ids is not None:
                    cos = cos_cache[position_ids]  # Shape: [batch_size, sequence_length, head_size/2]
                    sin = sin_cache[position_ids]  # Shape: [batch_size, sequence_length, head_size/2]
                else:
                    cos = cos_cache
                    sin = sin_cache
                cos = cos[:, :, :rotary_embedding_dim_half]  # Shape: [batch_size, sequence_length, rotary_embedding_dim/2]
                sin = sin[:, :, :rotary_embedding_dim_half]  # Shape: [batch_size, sequence_length, rotary_embedding_dim/2]
                cos = np.expand_dims(cos, axis=2)  # Shape: [batch_size, sequence_length, 1, rotary_embedding_dim/2]
                sin = np.expand_dims(sin, axis=2)  # Shape: [batch_size, sequence_length, 1, rotary_embedding_dim/2]

                # Either divide the input in halves or interleave (based on interleaved attribute)
                if interleaved:
                    x1 = x_rotate[:, :, :, 0::2]
                    x2 = x_rotate[:, :, :, 1::2]
                else:
                    x1, x2 = np.split(x_rotate, 2, axis=-1)

                # Calculate real and imaginary values
                real = cos * x1 - sin * x2
                imag = sin * x1 + cos * x2

                # Inserted rotated embeddings back to the original input
                if interleaved:
                    # x_rotate[:, :, :, 0::2] = real
                    # x_rotate[:, :, :, 1::2] = imag
                    real = np.expand_dims(real, axis=-1)
                    imag = np.expand_dims(imag, axis=-1)
                    x_rotate_concat = np.concatenate((real, imag), axis=-1)
                    x_rotate = np.reshape(x_rotate_concat, x_rotate.shape)
                else:
                    x_rotate = np.concatenate((real, imag), axis=-1)
                output = np.concatenate((x_rotate, x_not_rotate), axis=-1)
                if len(original_input_shape) == 3:
                    output = np.reshape(output, input.shape)
                else:
                    output = np.transpose(output, (0, 2, 1, 3))
                return output




        Args:
            X: The input tensor representing the token embeddings. 4D tensor with shape
                `(batch_size, num_heads, sequence_length, head_size)` or 3D tensor with
                shape `(batch_size, sequence_length, hidden_size)`. For cases with a 4D
                input tensor, `head_size` has to be even. For cases with a 3D input
                tensor, `num_heads` attribute must be provided and `hidden_size` must be
                an even multiple of `num_heads` where `hidden_size = num_heads *
                head_size`

            cos_cache: The cosine values for the rotation. 2D tensor with shape
                `(max_position_id_plus_1, head_size / 2)` for full rotation or
                `(max_position_id_plus_1, rotary_embedding_dim / 2)` for partial
                rotation when `position_ids` are provided. 3D tensor with shape
                `(batch_size, sequence_length, head_size / 2)` for full rotation or
                `(batch_size, sequence_length, rotary_embedding_dim / 2)` for partial
                rotation when `position_ids` are not provided. `max_position_id_plus_1`
                is a parameter to the model.

            sin_cache: The sine values for the rotation. 2D tensor with shape
                `(max_position_id_plus_1, head_size / 2)` for full rotation or
                `(max_position_id_plus_1, rotary_embedding_dim / 2)` for partial
                rotation when `position_ids` are provided. 3D tensor with shape
                `(batch_size, sequence_length, head_size / 2)` for full rotation or
                `(batch_size, sequence_length, rotary_embedding_dim / 2)` for partial
                rotation when `position_ids` are not provided. `max_position_id_plus_1`
                is a parameter to the model.

            position_ids: (optional) The position indices for the tokens. 2D tensor with
                shape `(batch_size, sequence_length)`

            interleaved: Rotate using interleaved pattern. Default value is 0 (False).

            num_heads: Number of attention heads. Must be provided when input is a 3D
                tensor.

            rotary_embedding_dim: Rotary embedding dimension used to apply partial
                rotary embeddings.
        """

        schema = get_schema("RotaryEmbedding", 23, "")
        op = Op(self, "RotaryEmbedding", schema)
        return op(
            *self._prepare_inputs(schema, X, cos_cache, sin_cache, position_ids),
            interleaved=interleaved,
            num_heads=num_heads,
            rotary_embedding_dim=rotary_embedding_dim,
        )

    V_Scan = TypeVar(
        "V_Scan",
        BFLOAT16,
        BOOL,
        COMPLEX128,
        COMPLEX64,
        DOUBLE,
        FLOAT,
        FLOAT16,
        FLOAT4E2M1,
        FLOAT8E4M3FN,
        FLOAT8E4M3FNUZ,
        FLOAT8E5M2,
        FLOAT8E5M2FNUZ,
        INT16,
        INT32,
        INT4,
        INT64,
        INT8,
        STRING,
        UINT16,
        UINT32,
        UINT4,
        UINT64,
        UINT8,
    )

    def Scan(
        self,
        *initial_state_and_scan_inputs: V_Scan,
        body: GraphProto,
        num_scan_inputs: int,
        scan_input_axes: Optional[Sequence[int]] = None,
        scan_input_directions: Optional[Sequence[int]] = None,
        scan_output_axes: Optional[Sequence[int]] = None,
        scan_output_directions: Optional[Sequence[int]] = None,
    ) -> V_Scan:
        r"""[🌐 Scan(23)](https://onnx.ai/onnx/operators/onnx__Scan.html#scan-23 "Online Documentation")


        Scan can be used to iterate over one or more scan_input tensors,
        constructing zero or more scan_output tensors. It combines ideas from general recurrences,
        functional programming constructs such as scan, fold, map, and zip, and is intended to enable
        generalizations of RNN-like constructs for sequence-to-sequence processing.
        Other tensors (referred to as state_variables here) can be used to carry a state
        when iterating from one element to another (similar to hidden-state in RNNs, also referred
        to as loop-carried dependences in the context of loops).
        Many common usages involve a single scan_input tensor (where functionality
        similar to scan, fold and map can be obtained). When more than one scan_input is used,
        a behavior similar to zip is obtained.

        The attribute body must be a graph, specifying the computation to be performed in
        every iteration. It takes as input the current values of the state_variables and
        the current iterated element of the scan_inputs. It must return the (updated) values
        of the state_variables and zero or more scan_output_element tensors. The values of the
        scan_output_element tensors are concatenated over all the iterations to produce the
        scan_output values of the scan construct (similar to the concatenated intermediate
        hidden-state values of RNN-like constructs). All the output tensors (state_variables as
        well as scan_output_element tensors) are required to have the same shape in each iteration
        of the loop (a restriction imposed to enable efficient memory allocation).

        Note that the iterated element passed to the body subgraph does not have a sequence
        axis. It will have a rank one less than the rank of the corresponding scan_input.

        The scan operation returns the final values of the state_variables as well as the
        scan_outputs.

        The optional attribute scan_input_directions specifies the direction (forward or backward)
        for each scan input. If this attribute is omitted, all sequences are scanned in the forward
        direction. A bidirectional scan may be performed by specifying the same tensor input twice
        in the scan_inputs, once with a forward direction, and once with a backward direction.

        The scan_output of the operation is produced by concatenating the scan_output_element
        values produced by the body in each iteration.  The optional attribute scan_output_directions
        specifies the direction in which scan_output is constructed (by appending or prepending the
        scan_output_element to scan_output in each iteration) for each scan_output. If this attribute
        is omitted, the scan_output_element is appended to the scan_output in each iteration.

        The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input.
        If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the
        batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1.
        Note that scanning a non-zero axis may be less efficient than scanning axis zero.

        The optional attribute scan_output_axes specifies the axis along which the scan_outputs
        are accumulated for each scan_output. For example, if axis 1 is the time axis (to be
        scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis
        value of 1.

        Note that because of the ONNX restriction that only the last parameter of an operator can
        be variadic, the initial-states and scan-inputs are listed together as one input parameter.
        Similarly, the final-states and scan-outputs are listed together as one output parameter.
        The attribute num_scan_inputs indicates the number M of scan-inputs.

        The behavior of

            Scan <
                num_scan_inputs = m,
                body = loop-body,
                scan_input_axes = [axis_1, ..., axis_m]
            > (init_1, ..., init_n, scan_1, ..., scan_m)

        is equivalent to the following pseudo-code:

            // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i
            // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j.
            sequence_length = scan_1.shape[axis_1];

            // initialize state-variables
            st_1 = init_1; ... st_n = init_n;
            // initialize scan-output variables: [] denotes an empty tensor
            scan_out_1 = []; ...; scan_out_k = [];
            // identify number of iterations:

            // execute loop
            for (int t = 0; t < sequence_length; ++t) {
                // generate the scan-input elements: the notation T<axis=k>[t] indicates the sub-tensor
                // of rank one less than T obtained by indexing T at position t along axis k.
                si_1 = scan_1<axis=axis_1>[t];
                ... ;
                si_m = scan_m<axis=axis_m>[t];
                // execute loop-body
                st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m)
                // accumulate the scan-output elements
                scan_out_1 = Concat<axis=0>(scan_out_1, so_1); ... ; scan_out_k = Concat<axis=0>(scan_out_k, so_k);
            }

            return st_1, ..., st_n, scan_out_1, ..., scan_out_k;

        *Sample usage: Encoding RNN using a Scan*

        The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi,
        recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can
        be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes
        %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these
        values are computed in the outer graph, they need to be passed in as extra state_variables.

            graph rnn-encoding {
              %H_0 = ...
              %X = ...
              %Y_h, %Y = Scan[body = <graph rnn-cell-1>, num_scan_inputs=1](%H_0, %X)
              return %Y, %Y_h
            }

            graph rnn-cell-1 (
              %H_tminus1[FLOAT, tensor]
              %X_t[FLOAT, tensor]
            ) {
              %Wi = ...
              %Ri = ...
              %Wbi = ...
              %Rbi = ...
              %t1 = X_t * (Wi^T)
              %t2 = H_tminus1*(Ri^T)
              %t3 = Add(%t1, %t2)
              %t4 = Add(%t3, %Wbi)
              %t5 = Add(%t4, %Rbi)
              %Ht = Tanh(%t5)
              %Accumulate = Identity(%Ht)
              return %Ht, %Accumulate
            }



        Args:
            initial_state_and_scan_inputs: (variadic, heterogeneous) Initial values of
                the loop's N state variables followed by M scan_inputs

            body: The graph run each iteration. It has N+M inputs: (loop state
                variables..., scan_input_elts...). It has N+K outputs: (loop state
                variables..., scan_output_elts...). Each scan_output is created by
                concatenating the value of the specified scan_output_elt value at the
                end of each iteration of the loop. It is an error if the dimensions of
                these values change across loop iterations.

            num_scan_inputs: An attribute specifying the number of scan_inputs M.

            scan_input_axes: An optional list of M flags. The i-th element of the list
                specifies the axis to be scanned (the sequence axis) for the i-th
                scan_input. If omitted, 0 will be used as the scan axis for every
                scan_input. Negative value for an axis means counting dimensions from
                the back. Accepted range is [-r, r-1] where r = rank(input).

            scan_input_directions: An optional list of M flags. The i-th element of the
                list specifies the direction to be scanned for the i-th scan_input
                tensor: 0 indicates forward direction and 1 indicates reverse direction.
                If omitted, all scan_input tensors will be scanned in the forward
                direction.

            scan_output_axes: An optional list of K flags. The i-th element of the list
                specifies the axis for the i-th scan_output. The scan outputs are
                accumulated along the specified axis. If omitted, 0 will be used as the
                scan axis for every scan_output. Negative value for an axis means
                counting dimensions from the back. Accepted range is [-r, r-1].

            scan_output_directions: An optional list of K flags, one for each
                scan_output. The i-th element of the list specifies whether the i-th
                scan_output should be constructed by appending or prepending a new value
                in each iteration: 0 indicates appending and 1 indicates prepending. If
                omitted, all scan_output tensors will be produced by appending a value
                in each iteration.
        """

        schema = get_schema("Scan", 23, "")
        op = Op(self, "Scan", schema)
        return op(
            *self._prepare_inputs(schema, *initial_state_and_scan_inputs),
            body=body,
            num_scan_inputs=num_scan_inputs,
            scan_input_axes=scan_input_axes,
            scan_input_directions=scan_input_directions,
            scan_output_axes=scan_output_axes,
            scan_output_directions=scan_output_directions,
        )

    T_Shape = TypeVar(
        "T_Shape",
        BFLOAT16,
        BOOL,
        COMPLEX128,
        COMPLEX64,
        DOUBLE,
        FLOAT,
        FLOAT16,
        FLOAT4E2M1,
        FLOAT8E4M3FN,
        FLOAT8E4M3FNUZ,
        FLOAT8E5M2,
        FLOAT8E5M2FNUZ,
        INT16,
        INT32,
        INT4,
        INT64,
        INT8,
        STRING,
        UINT16,
        UINT32,
        UINT4,
        UINT64,
        UINT8,
    )

    T1_Shape: TypeAlias = INT64

    def Shape(self, data: T_Shape, *, end: Optional[int] = None, start: int = 0) -> T1_Shape:
        r"""[🌐 Shape(23)](https://onnx.ai/onnx/operators/onnx__Shape.html#shape-23 "Online Documentation")


        Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor.
        Optional attributes start and end can be used to compute a slice of the input tensor's shape.
        If start axis is omitted, the slice starts from axis 0.
        The end axis, if specified, is exclusive (and the returned value will not include the size of that axis).
        If the end axis is omitted, the axes upto the last one will be included.
        Negative axes indicate counting back from the last axis.
        Note that axes will be clamped to the range [0, r], where r is the
        rank of the input tensor if they are out-of-range (after adding r in the case of
        negative axis). Thus, specifying any end value > r is equivalent to specifying an end
        value of r, and specifying any start value < -r is equivalent to specifying a start
        value of 0. If start > end, the result will be an empty shape.

        Examples:

        ::

            Input tensor with shape: [2, 3, 4]
            No attributes specified.
            Output: [2, 3, 4]



        ::

            Input tensor with shape: [2, 3, 4]
            start: -1
            Output: [4]



        ::

            Input tensor with shape: [2, 3, 4]
            end: -1
            Output: [2, 3]



        ::

            Input tensor with shape: [2, 3, 4]
            start: 1
            end: 2
            Output: [3]




        Args:
            data: (non-differentiable) An input tensor.

            end: (Optional) Ending axis for slicing the shape. Negative value means
                counting dimensions from the back. If omitted, sizes of all axes upto
                (including) the last one will be included.

            start: (Optional) Starting axis for slicing the shape. Default value is
                0.Negative value means counting dimensions from the back.
        """

        schema = get_schema("Shape", 23, "")
        op = Op(self, "Shape", schema)
        return op(*self._prepare_inputs(schema, data), end=end, start=start)

    T_Size = TypeVar(
        "T_Size",
        BFLOAT16,
        BOOL,
        COMPLEX128,
        COMPLEX64,
        DOUBLE,
        FLOAT,
        FLOAT16,
        FLOAT4E2M1,
        FLOAT8E4M3FN,
        FLOAT8E4M3FNUZ,
        FLOAT8E5M2,
        FLOAT8E5M2FNUZ,
        INT16,
        INT32,
        INT4,
        INT64,
        INT8,
        STRING,
        UINT16,
        UINT32,
        UINT4,
        UINT64,
        UINT8,
    )

    T1_Size: TypeAlias = INT64

    def Size(self, data: T_Size) -> T1_Size:
        r"""[🌐 Size(23)](https://onnx.ai/onnx/operators/onnx__Size.html#size-23 "Online Documentation")


        Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor.


        Args:
            data: (non-differentiable) An input tensor.
        """

        schema = get_schema("Size", 23, "")
        op = Op(self, "Size", schema)
        return op(*self._prepare_inputs(schema, data))

    T_Squeeze = TypeVar(
        "T_Squeeze",
        BFLOAT16,
        BOOL,
        COMPLEX128,
        COMPLEX64,
        DOUBLE,
        FLOAT,
        FLOAT16,
        FLOAT4E2M1,
        FLOAT8E4M3FN,
        FLOAT8E4M3FNUZ,
        FLOAT8E5M2,
        FLOAT8E5M2FNUZ,
        INT16,
        INT32,
        INT4,
        INT64,
        INT8,
        STRING,
        UINT16,
        UINT32,
        UINT4,
        UINT64,
        UINT8,
    )

    def Squeeze(self, data: T_Squeeze, axes: Optional[INT64] = None) -> T_Squeeze:
        r"""[🌐 Squeeze(23)](https://onnx.ai/onnx/operators/onnx__Squeeze.html#squeeze-23 "Online Documentation")


        Remove single-dimensional entries from the shape of a tensor.
        Takes an input `axes` with a list of axes to squeeze.
        If `axes` is not provided, all the single dimensions will be removed from
        the shape. If an axis is selected with shape entry not equal to one, an error is raised.


        Args:
            data: (differentiable) Tensors with at least max(dims) dimensions.

            axes: (optional, non-differentiable) 1D tensor of integers indicating the
                dimensions to squeeze. Negative value means counting dimensions from the
                back. Accepted range is [-r, r-1] where r = rank(data).
        """

        schema = get_schema("Squeeze", 23, "")
        op = Op(self, "Squeeze", schema)
        return op(*self._prepare_inputs(schema, data, axes))

    T_Transpose = TypeVar(
        "T_Transpose",
        BFLOAT16,
        BOOL,
        COMPLEX128,
        COMPLEX64,
        DOUBLE,
        FLOAT,
        FLOAT16,
        FLOAT4E2M1,
        FLOAT8E4M3FN,
        FLOAT8E4M3FNUZ,
        FLOAT8E5M2,
        FLOAT8E5M2FNUZ,
        INT16,
        INT32,
        INT4,
        INT64,
        INT8,
        STRING,
        UINT16,
        UINT32,
        UINT4,
        UINT64,
        UINT8,
    )

    def Transpose(
        self, data: T_Transpose, *, perm: Optional[Sequence[int]] = None
    ) -> T_Transpose:
        r"""[🌐 Transpose(23)](https://onnx.ai/onnx/operators/onnx__Transpose.html#transpose-23 "Online Documentation")


        Transpose the input tensor similar to numpy.transpose. For example, when
        perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape
        will be (2, 1, 3).


        Args:
            data: (differentiable) An input tensor.

            perm: A list of integers. By default, reverse the dimensions, otherwise
                permute the axes according to the values given. Its length must be equal
                to the rank of the input.
        """

        schema = get_schema("Transpose", 23, "")
        op = Op(self, "Transpose", schema)
        return op(*self._prepare_inputs(schema, data), perm=perm)

    T_Unsqueeze = TypeVar(
        "T_Unsqueeze",
        BFLOAT16,
        BOOL,
        COMPLEX128,
        COMPLEX64,
        DOUBLE,
        FLOAT,
        FLOAT16,
        FLOAT4E2M1,
        FLOAT8E4M3FN,
        FLOAT8E4M3FNUZ,
        FLOAT8E5M2,
        FLOAT8E5M2FNUZ,
        INT16,
        INT32,
        INT4,
        INT64,
        INT8,
        STRING,
        UINT16,
        UINT32,
        UINT4,
        UINT64,
        UINT8,
    )

    def Unsqueeze(self, data: T_Unsqueeze, axes: INT64) -> T_Unsqueeze:
        r"""[🌐 Unsqueeze(23)](https://onnx.ai/onnx/operators/onnx__Unsqueeze.html#unsqueeze-23 "Online Documentation")


        Insert single-dimensional entries to the shape of an input tensor (`data`).
        Takes one required input `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`).

        For example, given an input tensor (`data`) of shape [3, 4, 5], then
        Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1].

        The input `axes` should not contain any duplicate entries. It is an error if it contains duplicates.
        The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`.
        Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1].
        The order of values in `axes` does not matter and can come in any order.


        Args:
            data: (differentiable) Original tensor

            axes: (non-differentiable) 1D tensor of integers indicating the dimensions
                to be inserted. Negative value means counting dimensions from the back.
                Accepted range is [-r, r-1] where r = rank(expanded).
        """

        schema = get_schema("Unsqueeze", 23, "")
        op = Op(self, "Unsqueeze", schema)
        return op(*self._prepare_inputs(schema, data, axes))
