Repository: Quantco/spox (language: python)

Issue:
Signature of `spox.opset.ai.onnx.v18.split` is wrong
Opset 18 added an explicit attribute for the number of (variadic) outputs in Split. Unfortunately, the way this is generated in Spox isn’t particularly robust and currently the signature contains both the new attribute and the old workaround for specifying this (only the latter of which works, but IIRC both are required).

https://github.com/Quantco/spox/blob/5e83a185a5e2868d560cb888bd9831689ee3fc01/src/spox/opset/ai/onnx/v18.py#L2713-L2720

Compare to opset 17, which used just the workaround:

https://github.com/Quantco/spox/blob/5e83a185a5e2868d560cb888bd9831689ee3fc01/src/spox/opset/ai/onnx/v17.py#L14302-L14308

Bug workaround: use split from v17 and let the adapter handle this.
A breaking change will be required to fix this, but the current form is a bug.


Discussion / hints:

Fix patch:
diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 0bf0286a..e9e6dad9 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -7,6 +7,18 @@
 Change log
 ==========
 
+0.9.0 (2023-06-xx)
+------------------
+
+**New features**
+
+- The opset ``ai.onnx@19`` (ONNX 1.14) is now shipped with Spox.
+
+**Bug fixes**
+
+- The constructor for ``ai.onnx@18::Split`` is no longer generated incorrectly. No extraneous attribute is generated anymore, and the ``num_outputs`` attribute is marked as required (so that Spox can infer the number of outputs).
+
+
 0.8.1 (2023-05-xx)
 ------------------
 
diff --git a/docs/index.rst b/docs/index.rst
index 94b88295..22859edf 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -32,6 +32,7 @@ Spox is a Python framework for constructing `ONNX <https://github.com/onnx/onnx/
    API Reference <api/modules>
    Operator constructors for the ai.onnx domain version 17 <api/spox.opset.ai.onnx.v17>
    Operator constructors for the ai.onnx domain version 18 <api/spox.opset.ai.onnx.v18>
+   Operator constructors for the ai.onnx domain version 19 <api/spox.opset.ai.onnx.v19>
    Operator constructors for the ai.onnx.ml domain version 3 <api/spox.opset.ai.onnx.ml.v3>
 
 
diff --git a/environment.yml b/environment.yml
index e6f44124..0fd437d7 100644
--- a/environment.yml
+++ b/environment.yml
@@ -5,7 +5,7 @@ channels:
 dependencies:
   # Runtime
   - numpy
-  - onnx>=1.13.0
+  - onnx>=1.14.0
   - python>=3.8
 
   # Development
@@ -17,7 +17,7 @@ dependencies:
   - nbsphinx
   - numpydoc
   - onnxruntime>=1.13.1
-  - pandoc<=2.19.2  # ONNX docstrings in opset generation
+  - pandoc  # ONNX docstrings in opset generation
   - pip
   - pre-commit
   - pytest-cov
diff --git a/src/spox/opset/ai/onnx/ml/v3.py b/src/spox/opset/ai/onnx/ml/v3.py
index 2cef3e34..5ef6d835 100644
--- a/src/spox/opset/ai/onnx/ml/v3.py
+++ b/src/spox/opset/ai/onnx/ml/v3.py
@@ -820,11 +820,11 @@ def category_mapper(
     default_int64
         Attribute.
         An integer to use when an input string value is not found in the map.One
-        and only one of the 'default_*' attributes must be defined.
+        and only one of the 'default\_\*' attributes must be defined.
     default_string
         Attribute.
         A string to use when an input integer value is not found in the map.One
-        and only one of the 'default_*' attributes must be defined.
+        and only one of the 'default\_\*' attributes must be defined.
 
     Returns
     =======
@@ -979,8 +979,8 @@ def imputer(
     replaced_value_int64 should be defined, which one depends on whether
     floats or integers are being processed. The imputed_value attribute
     length can be 1 element, or it can have one element per input feature.In
-    other words, if the input tensor has the shape [*,F], then the length of
-    the attribute array may be 1 or F. If it is 1, then it is broadcast
+    other words, if the input tensor has the shape [\*,F], then the length
+    of the attribute array may be 1 or F. If it is 1, then it is broadcast
     along the last dimension and applied to each feature.
 
     Parameters
@@ -1082,7 +1082,7 @@ def label_encoder(
         A list of ints.
     keys_strings
         Attribute.
-        A list of strings. One and only one of 'keys_*'s should be set.
+        A list of strings. One and only one of 'keys\_\*'s should be set.
     values_floats
         Attribute.
         A list of floats.
@@ -1091,7 +1091,7 @@ def label_encoder(
         A list of ints.
     values_strings
         Attribute.
-        A list of strings. One and only one of 'value_*'s should be set.
+        A list of strings. One and only one of 'value\_\*'s should be set.
 
     Returns
     =======
@@ -1330,12 +1330,12 @@ def one_hot_encoder(
         Data to be encoded.
     cats_int64s
         Attribute.
-        List of categories, ints.One and only one of the 'cats_*' attributes
+        List of categories, ints.One and only one of the 'cats\_\*' attributes
         must be defined.
     cats_strings
         Attribute.
-        List of categories, strings.One and only one of the 'cats_*' attributes
-        must be defined.
+        List of categories, strings.One and only one of the 'cats\_\*'
+        attributes must be defined.
     zeros
         Attribute.
         If true and category is not present, will return all zeros; if false and
@@ -1392,11 +1392,11 @@ def svmclassifier(
     classlabels_ints
         Attribute.
         Class labels if using integer labels.One and only one of the
-        'classlabels_*' attributes must be defined.
+        'classlabels\_\*' attributes must be defined.
     classlabels_strings
         Attribute.
         Class labels if using string labels.One and only one of the
-        'classlabels_*' attributes must be defined.
+        'classlabels\_\*' attributes must be defined.
     coefficients
         Attribute.
 
@@ -1625,7 +1625,7 @@ def tree_ensemble_classifier(
     Tree Ensemble classifier. Returns the top class for each of N inputs.
     The attributes named 'nodes_X' form a sequence of tuples, associated by
     index into the sequences, which must all be of equal length. These
-    tuples define the nodes. Similarly, all fields prefixed with 'class_'
+    tuples define the nodes. Similarly, all fields prefixed with 'class\_'
     are tuples of votes at the leaves. A leaf may have multiple votes, where
     each vote is weighted by the associated class_weights index. One and
     only one of classlabels_strings or classlabels_int64s will be defined.
@@ -1664,11 +1664,11 @@ def tree_ensemble_classifier(
     classlabels_int64s
         Attribute.
         Class labels if using integer labels.One and only one of the
-        'classlabels_*' attributes must be defined.
+        'classlabels\_\*' attributes must be defined.
     classlabels_strings
         Attribute.
         Class labels if using string labels.One and only one of the
-        'classlabels_*' attributes must be defined.
+        'classlabels\_\*' attributes must be defined.
     nodes_falsenodeids
         Attribute.
         Child node if expression is false.
@@ -1944,12 +1944,12 @@ def zip_map(
         The input values
     classlabels_int64s
         Attribute.
-        The keys when using int keys.One and only one of the 'classlabels_*'
+        The keys when using int keys.One and only one of the 'classlabels\_\*'
         attributes must be defined.
     classlabels_strings
         Attribute.
-        The keys when using string keys.One and only one of the 'classlabels_*'
-        attributes must be defined.
+        The keys when using string keys.One and only one of the
+        'classlabels\_\*' attributes must be defined.
 
     Returns
     =======
diff --git a/src/spox/opset/ai/onnx/v17.py b/src/spox/opset/ai/onnx/v17.py
index 23add9fe..896e3dfc 100644
--- a/src/spox/opset/ai/onnx/v17.py
+++ b/src/spox/opset/ai/onnx/v17.py
@@ -4409,13 +4409,13 @@ def average_pool(
 
     ::
 
-       output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
+       output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1)
 
     or
 
     ::
 
-       output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
+       output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1)
 
     if ceil_mode is enabled
 
@@ -4428,14 +4428,14 @@ def average_pool(
 
     ::
 
-       VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i])
+       VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])
        SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
 
     And pad shape will be following if ``SAME_UPPER`` or ``SAME_LOWER``:
 
     ::
 
-       pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i]
+       pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i]
 
     The output of each pooling window is divided by the number of elements
     (exclude pad when attribute count_include_pad is zero).
@@ -4539,8 +4539,8 @@ def batch_normalization(
     (training_mode=True). There are multiple cases for the number of
     outputs, which we list below:
 
-    Output case #1: Y, running_mean, running_var (training_mode=True) Output
-    case #2: Y (training_mode=False)
+    -  Output case #1: Y, running_mean, running_var (training_mode=True)
+    -  Output case #2: Y (training_mode=False)
 
     When training_mode=False, extra outputs are invalid. The outputs are
     updated as follows when training_mode=True:
@@ -4552,14 +4552,16 @@ def batch_normalization(
 
        Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B
 
-       where:
+    where:
+
+    ::
 
        current_mean = ReduceMean(X, axis=all_except_channel_index)
        current_var =  ReduceVar(X, axis=all_except_channel_index)
 
-       Notice that ReduceVar refers to the population variance, and it equals to
-       sum(sqrd(x_i - x_avg)) / N
-       where N is the population size (this formula does not use sample size N - 1).
+    Notice that ``ReduceVar`` refers to the population variance, and it
+    equals to ``sum(sqrd(x_i - x_avg)) / N`` where ``N`` is the population
+    size (this formula does not use sample size ``N - 1``).
 
     The computation of ReduceMean and ReduceVar uses float to avoid overflow
     for float16 inputs.
@@ -4835,8 +4837,8 @@ def cast(
     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 result 100. There are some string literals reserved for special
-    floating-point values; "+INF" (and "INF"), "-INF", and "NaN" are
+    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
@@ -4962,7 +4964,8 @@ def ceil(
     r"""
     Ceil takes one input data (Tensor<T>) and produces one output data
     (Tensor<T>) where the ceil is, y = ceil(x), is applied to the tensor
-    elementwise.
+    elementwise. If x is integral, +0, -0, NaN, or infinite, x itself is
+    returned.
 
     Parameters
     ==========
@@ -5249,7 +5252,7 @@ def constant(
 ) -> Var:
     r"""
     This operator produces a constant tensor. Exactly one of the provided
-    attributes, either value, sparse_value, or value_\* must be specified.
+    attributes, either value, sparse_value, or value\_\* must be specified.
 
     Parameters
     ==========
@@ -5921,7 +5924,7 @@ def dft(
         Attribute.
         If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) +
         1] are returned because the real-to-complex Fourier transform satisfies
-        the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. Note if
+        the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]\*. Note if
         the input or window tensors are complex, then onesided output is not
         possible. Enabling onesided with real inputs performs a Real-valued fast
         Fourier transform (RFFT). When invoked with real or complex valued
@@ -5933,12 +5936,12 @@ def dft(
         Type T1.
         The Fourier Transform of the input vector.If onesided is 0, the
         following shape is expected:
-        [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][2]. If axis=0 and
+        [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][2]. If axis=1 and
         onesided is 1, the following shape is expected:
         [batch_idx][floor(signal_dim1/2)+1][signal_dim2]...[signal_dimN][2]. If
-        axis=1 and onesided is 1, the following shape is expected:
+        axis=2 and onesided is 1, the following shape is expected:
         [batch_idx][signal_dim1][floor(signal_dim2/2)+1]...[signal_dimN][2]. If
-        axis=N-1 and onesided is 1, the following shape is expected:
+        axis=N and onesided is 1, the following shape is expected:
         [batch_idx][signal_dim1][signal_dim2]...[floor(signal_dimN/2)+1][2]. The
         signal_dim at the specified axis is equal to the dft_length.
 
@@ -5979,29 +5982,23 @@ def depth_to_space(
     in the following order: depth, column, and then row. The output y is
     computed from the input x as below:
 
-    b, c, h, w = x.shape
-
-    tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h,
-    w])
-
-    tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2])
+    ::
 
-    y = np.reshape(tmp, [b, c // (blocksize**2), h \* blocksize, w \*
-    blocksize])
+       b, c, h, w = x.shape
+       tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w])
+       tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2])
+       y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize])
 
     In the CRD mode, elements along the depth dimension from the input
     tensor are rearranged in the following order: column, row, and the
     depth. The output y is computed from the input x as below:
 
-    b, c, h, w = x.shape
-
-    tmp = np.reshape(x, [b, c // (blocksize \*\* 2), blocksize, blocksize,
-    h, w])
-
-    tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3])
+    ::
 
-    y = np.reshape(tmp, [b, c // (blocksize \*\* 2), h \* blocksize, w \*
-    blocksize])
+       b, c, h, w = x.shape
+       tmp = np.reshape(x, [b, c // (blocksize ** 2), blocksize, blocksize, h, w])
+       tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3])
+       y = np.reshape(tmp, [b, c // (blocksize ** 2), h * blocksize, w * blocksize])
 
     Parameters
     ==========
@@ -6052,12 +6049,12 @@ def dequantize_linear(
     r"""
     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 same shape, and can be either a scalar for
-    per-tensor / per layer quantization, or a 1-D tensor for per-axis
-    quantization. 'x_zero_point' and 'x' must have same type. 'x' and 'y'
-    must have same shape. In the case of dequantizing int32, there's no zero
-    point (zero point is supposed to be 0).
+    dequantization formula is ``y = (x - x_zero_point) * x_scale``.
+    ``x_scale`` and ``x_zero_point`` must have same shape, and can be either
+    a scalar for per-tensor / per layer quantization, or a 1-D tensor for
+    per-axis quantization. ``x_zero_point`` and ``x`` must have same type.
+    ``x`` and ``y`` must have same shape. In the case of dequantizing int32,
+    there's no zero point (zero point is supposed to be 0).
 
     Parameters
     ==========
@@ -6286,9 +6283,11 @@ def dynamic_quantize_linear(
 
     ::
 
-        y_scale = (max(x) - min(x))/(qmax - qmin)
-        * where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8
-        * data range is adjusted to include 0.
+       y_scale = (max(x) - min(x))/(qmax - qmin)
+
+    -  where qmax and qmin are max and min values for quantization range
+       .i.e [0, 255] in case of uint8
+    -  data range is adjusted to include 0.
 
     Zero point is calculated as:
 
@@ -6296,17 +6295,22 @@ def dynamic_quantize_linear(
 
        intermediate_zero_point = qmin - min(x)/y_scale
        y_zero_point = cast(round(saturate(itermediate_zero_point)))
-       * where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8
-       * for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported.
-       * rounding to nearest ties to even.
+
+    -  where qmax and qmin are max and min values for quantization range
+       .i.e [0, 255] in case of uint8
+    -  for saturation, it saturates to [0, 255] if it's uint8, or [-127,
+       127] if it's int8. Right now only uint8 is supported.
+    -  rounding to nearest ties to even.
 
     Data quantization formula is:
 
     ::
 
        y = saturate (round (x / y_scale) + y_zero_point)
-       * for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported.
-       * rounding to nearest ties to even.
+
+    -  for saturation, it saturates to [0, 255] if it's uint8, or [-127,
+       127] if it's int8. Right now only uint8 is supported.
+    -  rounding to nearest ties to even.
 
     Parameters
     ==========
@@ -6353,7 +6357,9 @@ def einsum(
     An einsum of the form ``term1, term2 -> output-term`` produces an output
     tensor using the following equation
 
-    ``output[output-term] = reduce-sum( input1[term1] * input2[term] )``
+    ::
+
+       output[output-term] = reduce-sum( input1[term1] * input2[term] )
 
     where the reduce-sum performs a summation over all the indices occurring
     in the input terms (term1, term2) that do not occur in the output-term.
@@ -6683,7 +6689,7 @@ def flatten(
     r"""
     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).
+    d\_(axis-1), d_axis X d\_(axis+1) ... X dn).
 
     Parameters
     ==========
@@ -6730,7 +6736,8 @@ def floor(
     r"""
     Floor takes one input data (Tensor<T>) and produces one output data
     (Tensor<T>) where the floor is, y = floor(x), is applied to the tensor
-    elementwise.
+    elementwise. If x is integral, +0, -0, NaN, or infinite, x itself is
+    returned.
 
     Parameters
     ==========
@@ -6782,80 +6789,53 @@ def gru(
 
     Notations:
 
-    ``X`` - input tensor
-
-    ``z`` - update gate
-
-    ``r`` - reset gate
-
-    ``h`` - hidden gate
-
-    ``t`` - time step (t-1 means previous time step)
-
-    ``W[zrh]`` - W parameter weight matrix for update, reset, and hidden
-    gates
-
-    ``R[zrh]`` - R recurrence weight matrix for update, reset, and hidden
-    gates
-
-    ``Wb[zrh]`` - W bias vectors for update, reset, and hidden gates
-
-    ``Rb[zrh]`` - R bias vectors for update, reset, and hidden gates
-
-    ``WB[zrh]`` - W parameter weight matrix for backward update, reset, and
-    hidden gates
-
-    ``RB[zrh]`` - R recurrence weight matrix for backward update, reset, and
-    hidden gates
-
-    ``WBb[zrh]`` - W bias vectors for backward update, reset, and hidden
-    gates
-
-    ``RBb[zrh]`` - R bias vectors for backward update, reset, and hidden
-    gates
-
-    ``H`` - Hidden state
-
-    ``num_directions`` - 2 if direction == bidirectional else 1
+    -  ``X`` - input tensor
+    -  ``z`` - update gate
+    -  ``r`` - reset gate
+    -  ``h`` - hidden gate
+    -  ``t`` - time step (t-1 means previous time step)
+    -  ``W[zrh]`` - W parameter weight matrix for update, reset, and hidden
+       gates
+    -  ``R[zrh]`` - R recurrence weight matrix for update, reset, and hidden
+       gates
+    -  ``Wb[zrh]`` - W bias vectors for update, reset, and hidden gates
+    -  ``Rb[zrh]`` - R bias vectors for update, reset, and hidden gates
+    -  ``WB[zrh]`` - W parameter weight matrix for backward update, reset,
+       and hidden gates
+    -  ``RB[zrh]`` - R recurrence weight matrix for backward update, reset,
+       and hidden gates
+    -  ``WBb[zrh]`` - W bias vectors for backward update, reset, and hidden
+       gates
+    -  ``RBb[zrh]`` - R bias vectors for backward update, reset, and hidden
+       gates
+    -  ``H`` - Hidden state
+    -  ``num_directions`` - 2 if direction == bidirectional else 1
 
     Activation functions:
 
-    Relu(x) - max(0, x)
-
-    Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
-
-    Sigmoid(x) - 1/(1 + e^{-x})
-
-    (NOTE: Below are optional)
-
-    Affine(x) - alpha*x + beta
+    -  Relu(x) - max(0, x)
+    -  Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
+    -  Sigmoid(x) - 1/(1 + e^{-x})
 
-    LeakyRelu(x) - x if x >= 0 else alpha \* x
+    NOTE: Below are optional
 
-    ThresholdedRelu(x) - x if x >= alpha else 0
-
-    ScaledTanh(x) - alpha\ *Tanh(beta*\ x)
-
-    HardSigmoid(x) - min(max(alpha*x + beta, 0), 1)
-
-    Elu(x) - x if x >= 0 else alpha*(e^x - 1)
-
-    Softsign(x) - x/(1 + \|x|)
-
-    Softplus(x) - log(1 + e^x)
+    -  Affine(x) - alpha \* x + beta
+    -  LeakyRelu(x) - x if x >= 0 else alpha \* x
+    -  ThresholdedRelu(x) - x if x >= alpha else 0
+    -  ScaledTanh(x) - alpha \* Tanh(beta \* x)
+    -  HardSigmoid(x) - min(max(alpha \* x + beta, 0), 1)
+    -  Elu(x) - x if x >= 0 else alpha \* (e^x - 1)
+    -  Softsign(x) - x/(1 + \|x\|)
+    -  Softplus(x) - log(1 + e^x)
 
     Equations (Default: f=Sigmoid, g=Tanh):
 
     -  zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz)
-
     -  rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr)
-
     -  ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when
        linear_before_reset = 0
-
     -  ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when
        linear_before_reset != 0
-
     -  Ht = (1 - zt) (.) ht + zt (.) Ht-1 This operator has **optional**
        inputs/outputs. See `the
        doc <https://github.com/onnx/onnx/blob/main/docs/IR.md>`__ for more
@@ -6992,54 +6972,50 @@ def gather(
     one as axis=0) indexed by ``indices``, and concatenates them in an
     output tensor of rank q + (r - 1).
 
-    axis = 0 :
-
-    Let k = indices[i_{0}, ..., i_{q-1}] Then output[i_{0}, ..., i_{q-1},
-    j_{0}, ..., j_{r-2}] = input[k , j_{0}, ..., j_{r-2}]
+    If ``axis = 0``, let ``k = indices[i_{0}, ..., i_{q-1}]`` then
+    ``output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[k , j_{0}, ..., j_{r-2}]``:
 
     ::
 
-         data = [
-             [1.0, 1.2],
-             [2.3, 3.4],
-             [4.5, 5.7],
-         ]
-         indices = [
-             [0, 1],
-             [1, 2],
-         ]
-         output = [
-             [
-                 [1.0, 1.2],
-                 [2.3, 3.4],
-             ],
-             [
-                 [2.3, 3.4],
-                 [4.5, 5.7],
-             ],
-         ]
-
-    axis = 1 :
-
-    Let k = indices[i_{0}, ..., i_{q-1}] Then output[j_{0}, i_{0}, ...,
-    i_{q-1}, j_{1}, ..., j_{r-2}] = input[j_{0}, k, j_{1}, ..., j_{r-2}]
+       data = [
+           [1.0, 1.2],
+           [2.3, 3.4],
+           [4.5, 5.7],
+       ]
+       indices = [
+           [0, 1],
+           [1, 2],
+       ]
+       output = [
+           [
+               [1.0, 1.2],
+               [2.3, 3.4],
+           ],
+           [
+               [2.3, 3.4],
+               [4.5, 5.7],
+           ],
+       ]
+
+    If ``axis = 1``, let ``k = indices[i_{0}, ..., i_{q-1}]`` then
+    ``output[j_{0}, i_{0}, ..., i_{q-1}, j_{1}, ..., j_{r-2}] = input[j_{0}, k, j_{1}, ..., j_{r-2}]``:
 
     ::
 
-         data = [
-             [1.0, 1.2, 1.9],
-             [2.3, 3.4, 3.9],
-             [4.5, 5.7, 5.9],
-         ]
-         indices = [
-             [0, 2],
-         ]
-         axis = 1,
-         output = [
-                 [[1.0, 1.9]],
-                 [[2.3, 3.9]],
-                 [[4.5, 5.9]],
-         ]
+       data = [
+           [1.0, 1.2, 1.9],
+           [2.3, 3.4, 3.9],
+           [4.5, 5.7, 5.9],
+       ]
+       indices = [
+           [0, 2],
+       ]
+       axis = 1,
+       output = [
+               [[1.0, 1.9]],
+               [[2.3, 3.9]],
+               [[4.5, 5.9]],
+       ]
 
     Parameters
     ==========
@@ -7102,9 +7078,9 @@ def gather_elements(
 
     ::
 
-         out[i][j][k] = input[index[i][j][k]][j][k] if axis = 0,
-         out[i][j][k] = input[i][index[i][j][k]][k] if axis = 1,
-         out[i][j][k] = input[i][j][index[i][j][k]] if axis = 2,
+       out[i][j][k] = input[index[i][j][k]][j][k] if axis = 0,
+       out[i][j][k] = input[i][index[i][j][k]][k] if axis = 1,
+       out[i][j][k] = input[i][j][index[i][j][k]] if axis = 2,
 
     This operator is also the inverse of ScatterElements. It is similar to
     Torch's gather operation.
@@ -7113,38 +7089,38 @@ def gather_elements(
 
     ::
 
-         data = [
-             [1, 2],
-             [3, 4],
-         ]
-         indices = [
-             [0, 0],
-             [1, 0],
-         ]
-         axis = 1
-         output = [
-             [1, 1],
-             [4, 3],
-         ]
+       data = [
+           [1, 2],
+           [3, 4],
+       ]
+       indices = [
+           [0, 0],
+           [1, 0],
+       ]
+       axis = 1
+       output = [
+           [1, 1],
+           [4, 3],
+       ]
 
     Example 2:
 
     ::
 
-         data = [
-             [1, 2, 3],
-             [4, 5, 6],
-             [7, 8, 9],
-         ]
-         indices = [
-             [1, 2, 0],
-             [2, 0, 0],
-         ]
-         axis = 0
-         output = [
-             [4, 8, 3],
-             [7, 2, 3],
-         ]
+       data = [
+           [1, 2, 3],
+           [4, 5, 6],
+           [7, 8, 9],
+       ]
+       indices = [
+           [1, 2, 0],
+           [2, 0, 0],
+       ]
+       axis = 0
+       output = [
+           [4, 8, 3],
+           [7, 2, 3],
+       ]
 
     Parameters
     ==========
@@ -7355,9 +7331,8 @@ def gemm(
     General Matrix multiplication:
     https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
 
-    A' = transpose(A) if transA else A
-
-    B' = transpose(B) if transB else B
+    -  A' = transpose(A) if transA else A
+    -  B' = transpose(B) if transB else B
 
     Compute Y = alpha \* A' \* B' + beta \* C, where input tensor A has
     shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input
@@ -8237,17 +8212,14 @@ def lrn(
     Local Response Normalization proposed in the `AlexNet
     paper <https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf>`__.
     It normalizes over local input regions. The local region is defined
-    across the channels. For an element X[n, c, d1, ..., dk] in a tensor of
-    shape (N x C x D1 x D2, ..., Dk), its region is {X[n, i, d1, ..., dk] \|
-    max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1)
-    / 2))}.
+    across the channels. For an element ``X[n, c, d1, ..., dk]`` in a tensor
+    of shape ``(N x C x D1 x D2, ..., Dk)``, its region is
+    ``{X[n, i, d1, ..., dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}``.
 
-    square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2), where
-    max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1)
-    / 2)).
+    ``square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2)``, where
+    ``max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))``.
 
-    Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size \*
-    square_sum[n, c, d1, ..., dk] ) ^ beta
+    ``Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size * square_sum[n, c, d1, ..., dk] ) ^ beta``
 
     Parameters
     ==========
@@ -8325,88 +8297,59 @@ def lstm(
 
     Notations:
 
-    ``X`` - input tensor
-
-    ``i`` - input gate
-
-    ``o`` - output gate
-
-    ``f`` - forget gate
-
-    ``c`` - cell gate
-
-    ``t`` - time step (t-1 means previous time step)
-
-    ``W[iofc]`` - W parameter weight matrix for input, output, forget, and
-    cell gates
-
-    ``R[iofc]`` - R recurrence weight matrix for input, output, forget, and
-    cell gates
-
-    ``Wb[iofc]`` - W bias vectors for input, output, forget, and cell gates
-
-    ``Rb[iofc]`` - R bias vectors for input, output, forget, and cell gates
-
-    ``P[iof]`` - P peephole weight vector for input, output, and forget
-    gates
-
-    ``WB[iofc]`` - W parameter weight matrix for backward input, output,
-    forget, and cell gates
-
-    ``RB[iofc]`` - R recurrence weight matrix for backward input, output,
-    forget, and cell gates
-
-    ``WBb[iofc]`` - W bias vectors for backward input, output, forget, and
-    cell gates
-
-    ``RBb[iofc]`` - R bias vectors for backward input, output, forget, and
-    cell gates
-
-    ``PB[iof]`` - P peephole weight vector for backward input, output, and
-    forget gates
-
-    ``H`` - Hidden state
-
-    ``num_directions`` - 2 if direction == bidirectional else 1
+    -  ``X`` - input tensor
+    -  ``i`` - input gate
+    -  ``o`` - output gate
+    -  ``f`` - forget gate
+    -  ``c`` - cell gate
+    -  ``t`` - time step (t-1 means previous time step)
+    -  ``W[iofc]`` - W parameter weight matrix for input, output, forget,
+       and cell gates
+    -  ``R[iofc]`` - R recurrence weight matrix for input, output, forget,
+       and cell gates
+    -  ``Wb[iofc]`` - W bias vectors for input, output, forget, and cell
+       gates
+    -  ``Rb[iofc]`` - R bias vectors for input, output, forget, and cell
+       gates
+    -  ``P[iof]`` - P peephole weight vector for input, output, and forget
+       gates
+    -  ``WB[iofc]`` - W parameter weight matrix for backward input, output,
+       forget, and cell gates
+    -  ``RB[iofc]`` - R recurrence weight matrix for backward input, output,
+       forget, and cell gates
+    -  ``WBb[iofc]`` - W bias vectors for backward input, output, forget,
+       and cell gates
+    -  ``RBb[iofc]`` - R bias vectors for backward input, output, forget,
+       and cell gates
+    -  ``PB[iof]`` - P peephole weight vector for backward input, output,
+       and forget gates
+    -  ``H`` - Hidden state
+    -  ``num_directions`` - 2 if direction == bidirectional else 1
 
     Activation functions:
 
-    Relu(x) - max(0, x)
-
-    Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
-
-    Sigmoid(x) - 1/(1 + e^{-x})
-
-    (NOTE: Below are optional)
-
-    Affine(x) - alpha*x + beta
-
-    LeakyRelu(x) - x if x >= 0 else alpha \* x
+    -  Relu(x) - max(0, x)
+    -  Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
+    -  Sigmoid(x) - 1/(1 + e^{-x})
 
-    ThresholdedRelu(x) - x if x >= alpha else 0
+    NOTE: Below are optional
 
-    ScaledTanh(x) - alpha\ *Tanh(beta*\ x)
-
-    HardSigmoid(x) - min(max(alpha*x + beta, 0), 1)
-
-    Elu(x) - x if x >= 0 else alpha*(e^x - 1)
-
-    Softsign(x) - x/(1 + \|x|)
-
-    Softplus(x) - log(1 + e^x)
+    -  Affine(x) - alpha*x + beta
+    -  LeakyRelu(x) - x if x >= 0 else alpha \* x
+    -  ThresholdedRelu(x) - x if x >= alpha else 0
+    -  ScaledTanh(x) - alpha\ *Tanh(beta*\ x)
+    -  HardSigmoid(x) - min(max(alpha*x + beta, 0), 1)
+    -  Elu(x) - x if x >= 0 else alpha*(e^x - 1)
+    -  Softsign(x) - x/(1 + \|x\|)
+    -  Softplus(x) - log(1 + e^x)
 
     Equations (Default: f=Sigmoid, g=Tanh, h=Tanh):
 
     -  it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi)
-
     -  ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf)
-
     -  ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)
-
     -  Ct = ft (.) Ct-1 + it (.) ct
-
     -  ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo)
-
     -  Ht = ot (.) h(Ct) This operator has **optional** inputs/outputs. See
        `the doc <https://github.com/onnx/onnx/blob/main/docs/IR.md>`__ for
        more details about the representation of optional arguments. An empty
@@ -8651,10 +8594,6 @@ def leaky_relu(
     ``f(x) = alpha * x for x < 0``, ``f(x) = x for x >= 0``, is applied to
     the data tensor elementwise.
 
-    **History**
-
-    -  Version 16 adds bfloat16 to the types allowed.
-
     Parameters
     ==========
     X
@@ -8881,39 +8820,23 @@ def loop(
     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 = ...; }
 
-       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 = ...;
-           }
+    -  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*
 
@@ -9366,11 +9289,8 @@ def max_pool(
 
        output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1)
 
-    if ceil_mode is enabled
-
-    ::
-
-       * pad_shape[i] is sum of pads along axis i
+    if ceil_mode is enabled ``pad_shape[i]`` is the sum of pads along axis
+    ``i``.
 
     ``auto_pad`` is a DEPRECATED attribute. If you are using them currently,
     the output spatial shape will be following:
@@ -9865,16 +9785,17 @@ def mod(
     Performs element-wise binary modulus (with Numpy-style broadcasting
     support). The sign of the remainder is the same as that of the Divisor.
 
-    ::
-
-       Mod operator can also behave like C fmod() or numpy.fmod. In this case, the sign of the remainder however, will be the same as the Dividend
-       (in contrast to integer mod). To force a behavior like numpy.fmod() an 'fmod' Attribute is provided.
-       This attribute is set to 0 by default causing the behavior to be like integer mod.
-       Setting this attribute to 1 causes the remainder to be calculated similar to that of numpy.fmod().
+    Mod operator can also behave like C fmod() or numpy.fmod. In this case,
+    the sign of the remainder however, will be the same as the Dividend (in
+    contrast to integer mod). To force a behavior like numpy.fmod() an
+    'fmod' Attribute is provided. This attribute is set to 0 by default
+    causing the behavior to be like integer mod. Setting this attribute to 1
+    causes the remainder to be calculated similar to that of numpy.fmod().
 
-       If the input type is floating point, then `fmod` attribute must be set to 1.
+    If the input type is floating point, then ``fmod`` attribute must be set
+    to 1.
 
-       In case of dividend being zero, the results will be platform dependent.
+    In case of dividend being zero, the results will be platform dependent.
 
     This operator supports **multidirectional (i.e., Numpy-style)
     broadcasting**; for more details please check `the
@@ -10108,7 +10029,7 @@ def negative_log_likelihood_loss(
        sum(loss) / sum(weight[target[n][d_1][d_2]...[d_k]]]), for all samples.
 
     If "reduction" attribute is set to "sum", the output is a scalar:
-    sum(loss).
+    ``sum(loss)``.
 
     See also https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss.
 
@@ -10119,7 +10040,7 @@ def negative_log_likelihood_loss(
        // negative log likelihood loss, "none" reduction
        N, C, d1 = 2, 3, 2
        input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
-                [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
+                 [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
        target = [[2, 1], [0, 2]]
 
        loss = np.zeros((N, d1))
@@ -10637,15 +10558,10 @@ def prelu(
     PRelu takes input data (Tensor<T>) and slope tensor as input, and
     produces one output data (Tensor<T>) where the function
     ``f(x) = slope * x for x < 0``, ``f(x) = x for x >= 0``., is applied to
-    the data tensor elementwise.
-
-    **History**
-
-    -  Version 16 adds bfloat16 to the types allowed. This operator supports
-       **unidirectional broadcasting** (tensor slope should be
-       unidirectional broadcastable to input tensor X); for more details
-       please check `the
-       doc <https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md>`__.
+    the data tensor elementwise. This operator supports **unidirectional
+    broadcasting** (tensor slope should be unidirectional broadcastable to
+    input tensor X); for more details please check `the
+    doc <https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md>`__.
 
     Parameters
     ==========
@@ -11094,7 +11010,7 @@ def quantize_linear(
     per-axis quantization. The quantization formula is y = saturate ((x /
     y_scale) + y_zero_point). For saturation, it saturates to [0, 255] if
     it's uint8, or [-128, 127] if it's int8. For (x / y_scale), it's
-    rounding to nearest ties to even. Refer to
+    rounding to the nearest even. Refer to
     https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and
     'y' must have same type.
 
@@ -11167,57 +11083,36 @@ def rnn(
 
     Notations:
 
-    ``X`` - input tensor
-
-    ``i`` - input gate
-
-    ``t`` - time step (t-1 means previous time step)
-
-    ``Wi`` - W parameter weight matrix for input gate
-
-    ``Ri`` - R recurrence weight matrix for input gate
-
-    ``Wbi`` - W parameter bias vector for input gate
-
-    ``Rbi`` - R parameter bias vector for input gate
-
-    ``WBi`` - W parameter weight matrix for backward input gate
-
-    ``RBi`` - R recurrence weight matrix for backward input gate
-
-    ``WBbi`` - WR bias vectors for backward input gate
-
-    ``RBbi`` - RR bias vectors for backward input gate
-
-    ``H`` - Hidden state
-
-    ``num_directions`` - 2 if direction == bidirectional else 1
+    -  ``X`` - input tensor
+    -  ``i`` - input gate
+    -  ``t`` - time step (t-1 means previous time step)
+    -  ``Wi`` - W parameter weight matrix for input gate
+    -  ``Ri`` - R recurrence weight matrix for input gate
+    -  ``Wbi`` - W parameter bias vector for input gate
+    -  ``Rbi`` - R parameter bias vector for input gate
+    -  ``WBi`` - W parameter weight matrix for backward input gate
+    -  ``RBi`` - R recurrence weight matrix for backward input gate
+    -  ``WBbi`` - WR bias vectors for backward input gate
+    -  ``RBbi`` - RR bias vectors for backward input gate
+    -  ``H`` - Hidden state
+    -  ``num_directions`` - 2 if direction == bidirectional else 1
 
     Activation functions:
 
-    Relu(x) - max(0, x)
-
-    Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
-
-    Sigmoid(x) - 1/(1 + e^{-x})
-
-    (NOTE: Below are optional)
-
-    Affine(x) - alpha*x + beta
+    -  Relu(x) - max(0, x)
+    -  Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
+    -  Sigmoid(x) - 1/(1 + e^{-x})
 
-    LeakyRelu(x) - x if x >= 0 else alpha \* x
+    NOTE: Below are optional
 
-    ThresholdedRelu(x) - x if x >= alpha else 0
-
-    ScaledTanh(x) - alpha\ *Tanh(beta*\ x)
-
-    HardSigmoid(x) - min(max(alpha*x + beta, 0), 1)
-
-    Elu(x) - x if x >= 0 else alpha*(e^x - 1)
-
-    Softsign(x) - x/(1 + \|x|)
-
-    Softplus(x) - log(1 + e^x)
+    -  Affine(x) - alpha*x + beta
+    -  LeakyRelu(x) - x if x >= 0 else alpha \* x
+    -  ThresholdedRelu(x) - x if x >= alpha else 0
+    -  ScaledTanh(x) - alpha\ *Tanh(beta*\ x)
+    -  HardSigmoid(x) - min(max(alpha*x + beta, 0), 1)
+    -  Elu(x) - x if x >= 0 else alpha*(e^x - 1)
+    -  Softsign(x) - x/(1 + \|x\|)
+    -  Softplus(x) - log(1 + e^x)
 
     Equations (Default: f=Tanh):
 
@@ -11606,24 +11501,33 @@ def range(
     ``start`` and extends by increments of ``delta`` up to ``limit``
     (exclusive).
 
-    The number of elements in the output of range is computed as below-
+    The number of elements in the output of range is computed as below:
+
+    ::
+
+       number_of_elements = max( ceil( (limit - start) / delta ) , 0 )
+
+    The pseudocode determining the contents of the output is shown below:
 
-    ``number_of_elements = max( ceil( (limit - start) / delta ) , 0 )``
+    ::
 
-    The pseudocode determining the contents of the output is shown below-
+       for(int i=0; i<number_of_elements; ++i) {
+         output[i] =  start + (i * delta);
+       }
 
-    ``for(int i=0; i<number_of_elements; ++i)``
+    Example 1
 
-    ``{``
+    ::
 
-    ``output[i] =  start + (i * delta);``
+       Inputs: start = 3, limit = 9, delta = 3
+       Output: [3, 6]
 
-    ``}``
+    Example 2
 
-    ``Example 1`` Inputs: start = 3, limit = 9, delta = 3 Output: [3, 6]
+    ::
 
-    ``Example 2`` Inputs: start = 10, limit = 4, delta = -2 Output: [10, 8,
-    6]
+       Inputs: start = 10, limit = 4, delta = -2
+       Output: [10, 8, 6]
 
     Parameters
     ==========
@@ -11703,10 +11607,10 @@ def reduce_l1(
     keepdims: int = 1,
 ) -> Var:
     r"""
-    Computes the L1 norm of the input tensor's element along the provided
+    Computes the L1 norm of the input tensor's elements along the provided
     axes. The resulting tensor has the same rank as the input if keepdims
     equals 1. If keepdims equals 0, then the resulting tensor has the
-    reduced dimension pruned.
+    reduced dimension pruned. Input tensors of rank zero are valid.
 
     The above behavior is similar to numpy, with the exception that numpy
     defaults keepdims to False instead of True.
@@ -11757,10 +11661,10 @@ def reduce_l2(
     keepdims: int = 1,
 ) -> Var:
     r"""
-    Computes the L2 norm of the input tensor's element along the provided
+    Computes the L2 norm of the input tensor's elements along the provided
     axes. The resulting tensor has the same rank as the input if keepdims
     equals 1. If keepdims equals 0, then the resulting tensor has the
-    reduced dimension pruned.
+    reduced dimension pruned. Input tensors of rank zero are valid.
 
     The above behavior is similar to numpy, with the exception that numpy
     defaults keepdims to False instead of True.
@@ -11811,10 +11715,10 @@ def reduce_log_sum(
     keepdims: int = 1,
 ) -> Var:
     r"""
-    Computes the log sum of the input tensor's element along the provided
+    Computes the log sum of the input tensor's elements along the provided
     axes. The resulting tensor has the same rank as the input if keepdims
     equals 1. If keepdims equals 0, then the resulting tensor has the
-    reduced dimension pruned.
+    reduced dimension pruned. Input tensors of rank zero are valid.
 
     The above behavior is similar to numpy, with the exception that numpy
     defaults keepdims to False instead of True.
@@ -11865,10 +11769,10 @@ def reduce_log_sum_exp(
     keepdims: int = 1,
 ) -> Var:
     r"""
-    Computes the log sum exponent of the input tensor's element along the
+    Computes the log sum exponent of the input tensor's elements along the
     provided axes. The resulting tensor has the same rank as the input if
     keepdims equals 1. If keepdims equals 0, then the resulting tensor has
-    the reduced dimension pruned.
+    the reduced dimension pruned. Input tensors of rank zero are valid.
 
     The above behavior is similar to numpy, with the exception that numpy
     defaults keepdims to False instead of True.
@@ -11919,10 +11823,10 @@ def reduce_max(
     keepdims: int = 1,
 ) -> Var:
     r"""
-    Computes the max of the input tensor's element along the provided axes.
+    Computes the max of the input tensor's elements along the provided axes.
     The resulting tensor has the same rank as the input if keepdims equals
     1. If keepdims equals 0, then the resulting tensor has the reduced
-    dimension pruned.
+    dimension pruned. Input tensors of rank zero are valid.
 
     The above behavior is similar to numpy, with the exception that numpy
     defaults keepdims to False instead of True.
@@ -11973,10 +11877,10 @@ def reduce_mean(
     keepdims: int = 1,
 ) -> Var:
     r"""
-    Computes the mean of the input tensor's element along the provided axes.
-    The resulting tensor has the same rank as the input if keepdims equals
-    1. If keepdims equals 0, then the resulting tensor has the reduced
-    dimension pruned.
+    Computes the mean of the input tensor's elements along the provided
+    axes. The resulting tensor has the same rank as the input if keepdims
+    equals 1. If keepdims equals 0, then the resulting tensor has the
+    reduced dimension pruned. Input tensors of rank zero are valid.
 
     The above behavior is similar to numpy, with the exception that numpy
     defaults keepdims to False instead of True.
@@ -12027,10 +11931,10 @@ def reduce_min(
     keepdims: int = 1,
 ) -> Var:
     r"""
-    Computes the min of the input tensor's element along the provided axes.
+    Computes the min of the input tensor's elements along the provided axes.
     The resulting tensor has the same rank as the input if keepdims equals
     1. If keepdims equals 0, then the resulting tensor has the reduced
-    dimension pruned.
+    dimension pruned. Input tensors of rank zero are valid.
 
     The above behavior is similar to numpy, with the exception that numpy
     defaults keepdims to False instead of True.
@@ -12081,10 +11985,10 @@ def reduce_prod(
     keepdims: int = 1,
 ) -> Var:
     r"""
-    Computes the product of the input tensor's element along the provided
+    Computes the product of the input tensor's elements along the provided
     axes. The resulting tensor has the same rank as the input if keepdims
     equals 1. If keepdims equals 0, then the resulting tensor has the
-    reduced dimension pruned.
+    reduced dimension pruned. Input tensors of rank zero are valid.
 
     The above behavior is similar to numpy, with the exception that numpy
     defaults keepdims to False instead of True.
@@ -12136,10 +12040,10 @@ def reduce_sum(
     noop_with_empty_axes: int = 0,
 ) -> Var:
     r"""
-    Computes the sum of the input tensor's element along the provided axes.
+    Computes the sum of the input tensor's elements along the provided axes.
     The resulting tensor has the same rank as the input if keepdims equals
     1. If keepdims equals 0, then the resulting tensor has the reduced
-    dimension pruned.
+    dimension pruned. Input tensors of rank zero are valid.
 
     The above behavior is similar to numpy, with the exception that numpy
     defaults keepdims to False instead of True.
@@ -12199,10 +12103,10 @@ def reduce_sum_square(
     keepdims: int = 1,
 ) -> Var:
     r"""
-    Computes the sum square of the input tensor's element along the provided
-    axes. The resulting tensor has the same rank as the input if keepdims
-    equals 1. If keepdims equals 0, then the resulting tensor has the
-    reduced dimension pruned.
+    Computes the sum square of the input tensor's elements along the
+    provided axes. The resulting tensor has the same rank as the input if
+    keepdims equals 1. If keepdims equals 0, then the resulting tensor has
+    the reduced dimension pruned. Input tensors of rank zero are valid.
 
     The above behavior is similar to numpy, with the exception that numpy
     defaults keepdims to False instead of True.
@@ -12664,8 +12568,9 @@ def round(
     r"""
     Round takes one input Tensor and rounds the values, element-wise,
     meaning it finds the nearest integer for each value. In case of halfs,
-    the rule is to round them to the nearest even integer. The output tensor
-    has the same shape and type as the input.
+    the rule is to round them to the nearest even integer. If input x is
+    integral, +0, -0, NaN, or infinite, x itself is returned. The output
+    tensor has the same shape and type as the input.
 
     Examples:
 
@@ -12740,7 +12645,7 @@ def stft(
         Attribute.
         If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) +
         1] are returned because the real-to-complex Fourier transform satisfies
-        the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. Note if
+        the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]\*. Note if
         the input or window tensors are complex, then onesided output is not
         possible. Enabling onesided with real inputs performs a Real-valued fast
         Fourier transform (RFFT).When invoked with real or complex valued input,
@@ -13665,14 +13570,32 @@ def shape(
     specifying an end value of r, and specifying any start value < -r is
     equivalent to specifying a start value of 0.
 
-    For example: Input tensor with shape: [2, 3, 4] No attributes specified.
-    Output: [2, 3, 4]
+    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] start: -1 Output: [4]
+    ::
 
-    Input tensor with shape: [2, 3, 4] end: -1 Output: [2, 3]
+       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]
+    ::
+
+       Input tensor with shape: [2, 3, 4]
+       start: 1
+       end: 2
+       Output: [3]
 
     Parameters
     ==========
@@ -13979,10 +13902,35 @@ def slice(
     recommended to pass in ``INT_MAX`` when slicing forward and 'INT_MIN'
     when slicing backward.
 
-    Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts =
-    [1, 0] ends = [2, 3] steps = [1, 2] result = [ [5, 7], ] Example 2: data
-    = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000]
-    result = [ [2, 3, 4], ]
+    Example 1:
+
+    ::
+
+       data = [
+           [1, 2, 3, 4],
+           [5, 6, 7, 8],
+       ]
+       axes = [0, 1]
+       starts = [1, 0]
+       ends = [2, 3]
+       steps = [1, 2]
+       result = [
+           [5, 7],
+       ]
+
+    Example 2:
+
+    ::
+
+       data = [
+           [1, 2, 3, 4],
+           [5, 6, 7, 8],
+       ]
+       starts = [0, 1]
+       ends = [-1, 1000]
+       result = [
+           [2, 3, 4],
+       ]
 
     Parameters
     ==========
@@ -14100,27 +14048,46 @@ def softmax_cross_entropy_loss(
     L[i,][j_1][j_2]...[j_k] denotes a scalar element in L. After L is
     available, this operator can optionally do a reduction operator.
 
-    shape(scores): (N, C) where C is the number of classes, or (N, C, D1,
-    D2,..., Dk), with K >= 1 in case of K-dimensional loss. shape(labels):
-    (N) where each value is 0 <= labels[i] <= C-1, or (N, D1, D2,..., Dk),
-    with K >= 1 in case of K-dimensional loss.
+    -  shape(scores): (N, C) where C is the number of classes, or (N, C, D1,
+       D2,..., Dk), with K >= 1 in case of K-dimensional loss.
+    -  shape(labels): (N) where each value is 0 <= labels[i] <= C-1, or (N,
+       D1, D2,..., Dk), with K >= 1 in case of K-dimensional loss.
 
     The loss for one sample, l_i, can caculated as follows:
-    l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk], where i is the index of
-    classes. or l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk] \* weights[c],
-    if 'weights' is provided.
+
+    ::
+
+       l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk], where i is the index of classes.
+
+    or
+
+    ::
+
+       l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk] * weights[c], if 'weights' is provided.
 
     loss is zero for the case when label-value equals ignore_index.
-    l[i][d1][d2]...[dk] = 0, when labels[n][d1][d2]...[dk] = ignore_index
 
-    where: p = Softmax(scores) y = Log(p) c = labels[i][d1][d2]...[dk]
+    ::
+
+       l[i][d1][d2]...[dk]  = 0, when labels[n][d1][d2]...[dk] = ignore_index
+
+    where:
+
+    ::
+
+       p = Softmax(scores)
+       y = Log(p)
+       c = labels[i][d1][d2]...[dk]
 
-    Finally, L is optionally reduced: If reduction = 'none', the output is L
-    with shape (N, D1, D2, ..., Dk). If reduction = 'sum', the output is
-    scalar: Sum(L). If reduction = 'mean', the output is scalar:
-    ReduceMean(L), or if weight is provided: ReduceSum(L) / ReduceSum(W),
-    where tensor W is of shape (N, D1, D2, ..., Dk) and W[n][d1][d2]...[dk]
-    = weights[labels[i][d1][d2]...[dk]].
+    Finally, L is optionally reduced:
+
+    -  If reduction = 'none', the output is L with shape (N, D1, D2, ...,
+       Dk).
+    -  If reduction = 'sum', the output is scalar: Sum(L).
+    -  If reduction = 'mean', the output is scalar: ReduceMean(L), or if
+       weight is provided: ``ReduceSum(L) / ReduceSum(W)``, where tensor W
+       is of shape ``(N, D1, D2, ..., Dk)`` and
+       ``W[n][d1][d2]...[dk] = weights[labels[i][d1][d2]...[dk]]``.
 
     Parameters
     ==========
@@ -14224,7 +14191,7 @@ def softsign(
     input: Var,
 ) -> Var:
     r"""
-    Calculates the softsign (x/(1+|x|)) of the given input tensor
+    Calculates the softsign (x/(1+|x\|)) of the given input tensor
     element-wise.
 
     Parameters
@@ -14237,7 +14204,7 @@ def softsign(
     =======
     output : Var
         Type T.
-        The softsign (x/(1+|x|)) values of the input tensor computed
+        The softsign (x/(1+|x\|)) values of the input tensor computed
         element-wise
 
     Notes
@@ -14362,15 +14329,18 @@ def split_to_sequence(
 ) -> Var:
     r"""
     Split a tensor into a sequence of tensors, along the specified 'axis'.
-    Lengths of the parts can be specified using argument 'split'. 'split'
-    must contain only positive numbers. 'split' is either a scalar (tensor
-    of empty shape), or a 1-D tensor. If 'split' is a scalar, then 'input'
-    will be split into equally sized chunks(if possible). Last chunk will be
-    smaller if the 'input' size along the given axis 'axis' is not divisible
-    by 'split'. Otherwise, the tensor is split into 'size(split)' chunks,
-    with lengths of the parts on 'axis' specified in 'split'. In this
-    scenario, the sum of entries in 'split' must be equal to the dimension
-    size of input tensor on 'axis'.
+    Lengths of the parts can be specified using the optional argument
+    'split'. If the argument
+    ``split' is not specified, a default scalar value of 1 is used as the value of``\ split'.
+    'split' must contain only positive numbers. 'split' is either a scalar
+    (tensor of empty shape), or a 1-D tensor. If 'split' is a scalar, then
+    'input' will be split into chunks all of size 'split' if possible. The
+    last chunk alone may be smaller than 'split' if the 'input' size along
+    the given axis 'axis' is not divisible by 'split'. If 'split' is a
+    1-dimensional tensor, the input tensor is split into 'size(split)'
+    chunks, with lengths of the parts on 'axis' specified in 'split'. In
+    this scenario, the sum of entries in 'split' must be equal to the
+    dimension size of input tensor on 'axis'.
 
     Parameters
     ==========
@@ -14802,7 +14772,7 @@ def tf_idf_vectorizer(
     ngram_indexes
         Attribute.
         list of int64s (type: AttributeProto::INTS). This list is parallel to
-        the specified 'pool_*' attribute. The i-th element in ngram_indexes
+        the specified 'pool\_\*' attribute. The i-th element in ngram_indexes
         indicate the coordinate of the i-th n-gram in the output tensor.
     pool_int64s
         Attribute.
@@ -14954,16 +14924,24 @@ def top_k(
     r"""
     Retrieve the top-K largest or smallest elements along a specified axis.
     Given an input tensor of shape [a_1, a_2, ..., a_n, r] and integer
-    argument k, return two outputs: -Value tensor of shape [a_1, a_2, ...,
-    a_{axis-1}, k, a_{axis+1}, ... a_n] which contains the values of the top
-    k elements along the specified axis -Index tensor of shape [a_1, a_2,
-    ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which contains the indices of
-    the top k elements (original indices from the input tensor).
+    argument k, return two outputs:
+
+    -  Value tensor of shape [a_1, a_2, ..., a\_{axis-1}, k, a\_{axis+1},
+       ... a_n] which contains the values of the top k elements along the
+       specified axis
 
-    If "largest" is 1 (the default value) then the k largest elements are
-    returned. If "sorted" is 1 (the default value) then the resulting k
-    elements will be sorted. If "sorted" is 0, order of returned 'Values'
-    and 'Indices' are undefined.
+    -  Index tensor of shape [a_1, a_2, ..., a\_{axis-1}, k, a\_{axis+1},
+       ... a_n] which contains the indices of the top k elements (original
+       indices from the input tensor).
+
+    -  If "largest" is 1 (the default value) then the k largest elements are
+       returned.
+
+    -  If "sorted" is 1 (the default value) then the resulting k elements
+       will be sorted.
+
+    -  If "sorted" is 0, order of returned 'Values' and 'Indices' are
+       undefined.
 
     Given two equivalent values, this operator uses the indices along the
     axis as a tiebreaker. That is, the element with the lower index will
@@ -14994,11 +14972,11 @@ def top_k(
     =======
     Values : Var
         Type T.
-        Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n]
+        Tensor of shape [a_1, a_2, ..., a\_{axis-1}, k, a\_{axis+1}, ... a_n]
         containing top K values from the input tensor
     Indices : Var
         Type I.
-        Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n]
+        Tensor of shape [a_1, a_2, ..., a\_{axis-1}, k, a\_{axis+1}, ... a_n]
         containing the corresponding input tensor indices for the top K values.
 
     Notes
@@ -15077,7 +15055,7 @@ def trilu(
     whether the upper or lower part is retained. If set to true, the upper
     triangular matrix is retained. Lower triangular matrix is retained
     otherwise. Default value for the "upper" attribute is true. Trilu takes
-    one input tensor of shape [*, N, M], where \* is zero or more batch
+    one input tensor of shape [\*, N, M], where \* is zero or more batch
     dimensions. The upper triangular part consists of the elements on and
     above the given diagonal (k). The lower triangular part consists of
     elements on and below the diagonal. All other elements in the matrix are
@@ -15087,7 +15065,7 @@ def trilu(
     above it. A negative k value retains the main diagonal and \|k\|
     diagonals below it. If upper is set to false, a positive k retains the
     lower triangular matrix including the main diagonal and k diagonals
-    above it. A negative k value excludes the main diagonal and (|k|-1)
+    above it. A negative k value excludes the main diagonal and (\|k\|-1)
     diagonals below it.
 
     Parameters
@@ -15155,43 +15133,101 @@ def unique(
 
     https://docs.scipy.org/doc/numpy/reference/generated/numpy.unique.html
 
-    Example 1: input_X = [2, 1, 1, 3, 4, 3] attribute_sorted = 0
-    attribute_axis = None output_Y = [2, 1, 3, 4] output_indices = [0, 1, 3,
-    4] output_inverse_indices = [0, 1, 1, 2, 3, 2] output_counts = [1, 2, 2,
-    1]
+    Example 1:
 
-    Example 2: input_X = [[1, 3], [2, 3]] attribute_sorted = 1
-    attribute_axis = None output_Y = [1, 2, 3] output_indices = [0, 2, 1]
-    output_inverse_indices = [0, 2, 1, 2] output_counts = [1, 1, 2]
+    ::
 
-    Example 3: input_X = [[1, 0, 0], [1, 0, 0], [2, 3, 4]] attribute_sorted
-    = 1 attribute_axis = 0 output_Y = [[1, 0, 0], [2, 3, 4]] output_indices
-    = [0, 2] output_inverse_indices = [0, 0, 1] output_counts = [2, 1]
+       input_X = [2, 1, 1, 3, 4, 3]
+       attribute_sorted = 0
+       attribute_axis = None
+       output_Y = [2, 1, 3, 4]
+       output_indices = [0, 1, 3, 4]
+       output_inverse_indices = [0, 1, 1, 2, 3, 2]
+       output_counts = [1, 2, 2, 1]
 
-    Example 4: input_x = [[[1., 1.], [0., 1.], [2., 1.], [0., 1.]], [[1.,
-    1.], [0., 1.], [2., 1.], [0., 1.]]] attribute_sorted = 1 attribute_axis
-    = 1
+    Example 2:
 
-    intermediate data are presented below for better understanding:
+    ::
 
-    there are 4 subtensors sliced along axis 1 of input_x (shape = (2, 4,
-    2)): A: [[1, 1], [1, 1]], [[0, 1], [0, 1]], [[2, 1], [2, 1]], [[0, 1],
-    [0, 1]].
+       input_X = [[1, 3], [2, 3]]
+       attribute_sorted = 1
+       attribute_axis = None
+       output_Y = [1, 2, 3]
+       output_indices = [0, 2, 1]
+       output_inverse_indices = [0, 2, 1, 2]
+       output_counts = [1, 1, 2]
 
-    there are 3 unique subtensors: [[1, 1], [1, 1]], [[0, 1], [0, 1]], [[2,
-    1], [2, 1]].
+    Example 3:
 
-    sorted unique subtensors: B: [[0, 1], [0, 1]], [[1, 1], [1, 1]], [[2,
-    1], [2, 1]].
+    ::
+
+       input_X = [[1, 0, 0], [1, 0, 0], [2, 3, 4]]
+       attribute_sorted = 1
+       attribute_axis = 0
+       output_Y = [[1, 0, 0], [2, 3, 4]]
+       output_indices = [0, 2]
+       output_inverse_indices = [0, 0, 1]
+       output_counts = [2, 1]
 
-    output_Y is constructed from B: [[[0. 1.], [1. 1.], [2. 1.]], [[0. 1.],
-    [1. 1.], [2. 1.]]]
+    Example 4:
 
-    output_indices is to map from B to A: [1, 0, 2]
+    ::
 
-    output_inverse_indices is to map from A to B: [1, 0, 2, 0]
+       input_x = [[[1., 1.], [0., 1.], [2., 1.], [0., 1.]],
+                   [[1., 1.], [0., 1.], [2., 1.], [0., 1.]]]
+       attribute_sorted = 1
+       attribute_axis = 1
 
-    output_counts = [2 1 1]
+    intermediate data are presented below for better understanding: there
+    are 4 subtensors sliced along axis 1 of input_x (shape = (2, 4, 2)):
+
+    ::
+
+       A: [[1, 1], [1, 1]],
+          [[0, 1], [0, 1]],
+          [[2, 1], [2, 1]],
+          [[0, 1], [0, 1]].
+
+    there are 3 unique subtensors:
+
+    ::
+
+       [[1, 1], [1, 1]],
+       [[0, 1], [0, 1]],
+       [[2, 1], [2, 1]].
+
+    sorted unique subtensors:
+
+    ::
+
+       B: [[0, 1], [0, 1]],
+          [[1, 1], [1, 1]],
+          [[2, 1], [2, 1]].
+
+    output_Y is constructed from B:
+
+    ::
+
+       [[[0. 1.], [1. 1.], [2. 1.]],
+        [[0. 1.], [1. 1.], [2. 1.]]]
+
+    output_indices is to map from B to A:
+
+    ::
+
+       [1, 0, 2]
+
+    output_inverse_indices is to map from A to B:
+
+    ::
+
+       [1, 0, 2, 0]
+
+    output_counts:
+
+    ::
+
+       [2, 1, 1]
 
     Parameters
     ==========
@@ -15261,7 +15297,7 @@ def unsqueeze(
     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
+    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].
 
@@ -15320,11 +15356,6 @@ def where(
     broadcasting**; for more details please check `the
     doc <https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md>`__.
 
-    **History**
-
-    -  Version 16 adds bfloat16 to the types allowed (for the second and
-       third parameter).
-
     Parameters
     ==========
     condition
diff --git a/src/spox/opset/ai/onnx/v18.py b/src/spox/opset/ai/onnx/v18.py
index 55d91519..0cd9e3ce 100644
--- a/src/spox/opset/ai/onnx/v18.py
+++ b/src/spox/opset/ai/onnx/v18.py
@@ -1341,11 +1341,8 @@ def lp_pool(
 
        output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1)
 
-    if ceil_mode is enabled
-
-    ::
-
-       * pad_shape[i] is sum of pads along axis i
+    if ceil_mode is enabled ``pad_shape[i]`` is the sum of pads along axis
+    ``i``.
 
     ``auto_pad`` is a DEPRECATED attribute. If you are using them currently,
     the output spatial shape will be following:
@@ -1579,39 +1576,69 @@ def pad(
 
     3) ``edge`` - pads with the edge values of array
 
-    Example 1 (``constant`` mode): Insert 0 pads to the beginning of the
-    second dimension.
+    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
 
-    data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ]
+       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],
+       ]
 
-    pads = [0, 2, 0, 0]
+    Example 2 (``reflect`` mode):
 
-    mode = 'constant'
+    ::
 
-    constant_value = 0.0
+       data = [
+           [1.0, 1.2],
+           [2.3, 3.4],
+           [4.5, 5.7],
+       ]
 
-    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], ]
+       pads = [0, 2, 0, 0]
 
-    Example 2 (``reflect`` mode): data = [ [1.0, 1.2], [2.3, 3.4], [4.5,
-    5.7], ]
+       mode = 'reflect'
 
-    pads = [0, 2, 0, 0]
+       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],
+       ]
 
-    mode = 'reflect'
+    Example 3 (``edge`` mode):
 
-    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],
-    ]
+       data = [
+           [1.0, 1.2],
+           [2.3, 3.4],
+           [4.5, 5.7],
+       ]
 
-    pads = [0, 2, 0, 0]
+       pads = [0, 2, 0, 0]
 
-    mode = 'edge'
+       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], ]
+       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],
+       ]
 
     Parameters
     ==========
@@ -1678,10 +1705,10 @@ def reduce_l1(
     noop_with_empty_axes: int = 0,
 ) -> Var:
     r"""
-    Computes the L1 norm of the input tensor's element along the provided
+    Computes the L1 norm of the input tensor's elements along the provided
     axes. The resulting tensor has the same rank as the input if keepdims
     equals 1. If keepdims equals 0, then the resulting tensor has the
-    reduced dimension pruned.
+    reduced dimension pruned. Input tensors of rank zero are valid.
 
     The above behavior is similar to numpy, with the exception that numpy
     defaults keepdims to False instead of True.
@@ -1742,10 +1769,10 @@ def reduce_l2(
     noop_with_empty_axes: int = 0,
 ) -> Var:
     r"""
-    Computes the L2 norm of the input tensor's element along the provided
+    Computes the L2 norm of the input tensor's elements along the provided
     axes. The resulting tensor has the same rank as the input if keepdims
     equals 1. If keepdims equals 0, then the resulting tensor has the
-    reduced dimension pruned.
+    reduced dimension pruned. Input tensors of rank zero are valid.
 
     The above behavior is similar to numpy, with the exception that numpy
     defaults keepdims to False instead of True.
@@ -1806,10 +1833,10 @@ def reduce_log_sum(
     noop_with_empty_axes: int = 0,
 ) -> Var:
     r"""
-    Computes the log sum of the input tensor's element along the provided
+    Computes the log sum of the input tensor's elements along the provided
     axes. The resulting tensor has the same rank as the input if keepdims
     equals 1. If keepdims equals 0, then the resulting tensor has the
-    reduced dimension pruned.
+    reduced dimension pruned. Input tensors of rank zero are valid.
 
     The above behavior is similar to numpy, with the exception that numpy
     defaults keepdims to False instead of True.
@@ -1870,10 +1897,10 @@ def reduce_log_sum_exp(
     noop_with_empty_axes: int = 0,
 ) -> Var:
     r"""
-    Computes the log sum exponent of the input tensor's element along the
+    Computes the log sum exponent of the input tensor's elements along the
     provided axes. The resulting tensor has the same rank as the input if
     keepdims equals 1. If keepdims equals 0, then the resulting tensor has
-    the reduced dimension pruned.
+    the reduced dimension pruned. Input tensors of rank zero are valid.
 
     The above behavior is similar to numpy, with the exception that numpy
     defaults keepdims to False instead of True.
@@ -1934,10 +1961,10 @@ def reduce_max(
     noop_with_empty_axes: int = 0,
 ) -> Var:
     r"""
-    Computes the max of the input tensor's element along the provided axes.
+    Computes the max of the input tensor's elements along the provided axes.
     The resulting tensor has the same rank as the input if keepdims equals
     1. If keepdims equals 0, then the resulting tensor has the reduced
-    dimension pruned.
+    dimension pruned. Input tensors of rank zero are valid.
 
     The above behavior is similar to numpy, with the exception that numpy
     defaults keepdims to False instead of True.
@@ -1998,10 +2025,10 @@ def reduce_mean(
     noop_with_empty_axes: int = 0,
 ) -> Var:
     r"""
-    Computes the mean of the input tensor's element along the provided axes.
-    The resulting tensor has the same rank as the input if keepdims equals
-    1. If keepdims equals 0, then the resulting tensor has the reduced
-    dimension pruned.
+    Computes the mean of the input tensor's elements along the provided
+    axes. The resulting tensor has the same rank as the input if keepdims
+    equals 1. If keepdims equals 0, then the resulting tensor has the
+    reduced dimension pruned. Input tensors of rank zero are valid.
 
     The above behavior is similar to numpy, with the exception that numpy
     defaults keepdims to False instead of True.
@@ -2062,10 +2089,10 @@ def reduce_min(
     noop_with_empty_axes: int = 0,
 ) -> Var:
     r"""
-    Computes the min of the input tensor's element along the provided axes.
+    Computes the min of the input tensor's elements along the provided axes.
     The resulting tensor has the same rank as the input if keepdims equals
     1. If keepdims equals 0, then the resulting tensor has the reduced
-    dimension pruned.
+    dimension pruned. Input tensors of rank zero are valid.
 
     The above behavior is similar to numpy, with the exception that numpy
     defaults keepdims to False instead of True.
@@ -2126,10 +2153,10 @@ def reduce_prod(
     noop_with_empty_axes: int = 0,
 ) -> Var:
     r"""
-    Computes the product of the input tensor's element along the provided
+    Computes the product of the input tensor's elements along the provided
     axes. The resulting tensor has the same rank as the input if keepdims
     equals 1. If keepdims equals 0, then the resulting tensor has the
-    reduced dimension pruned.
+    reduced dimension pruned. Input tensors of rank zero are valid.
 
     The above behavior is similar to numpy, with the exception that numpy
     defaults keepdims to False instead of True.
@@ -2190,10 +2217,10 @@ def reduce_sum_square(
     noop_with_empty_axes: int = 0,
 ) -> Var:
     r"""
-    Computes the sum square of the input tensor's element along the provided
-    axes. The resulting tensor has the same rank as the input if keepdims
-    equals 1. If keepdims equals 0, then the resulting tensor has the
-    reduced dimension pruned.
+    Computes the sum square of the input tensor's elements along the
+    provided axes. The resulting tensor has the same rank as the input if
+    keepdims equals 1. If keepdims equals 0, then the resulting tensor has
+    the reduced dimension pruned. Input tensors of rank zero are valid.
 
     The above behavior is similar to numpy, with the exception that numpy
     defaults keepdims to False instead of True.
@@ -2470,18 +2497,18 @@ def scatter_elements(
 
     ::
 
-         output[indices[i][j]][j] = updates[i][j] if axis = 0,
-         output[i][indices[i][j]] = updates[i][j] if axis = 1,
+       output[indices[i][j]][j] = updates[i][j] if axis = 0,
+       output[i][indices[i][j]] = updates[i][j] if axis = 1,
 
     When ``reduction`` is set to some reduction function ``f``, the update
     corresponding to the [i][j] entry is performed as below:
 
     ::
 
-         output[indices[i][j]][j] += f(output[indices[i][j]][j], updates[i][j]) if axis = 0,
-         output[i][indices[i][j]] += f(output[i][indices[i][j]], updates[i][j]) if axis = 1,
+       output[indices[i][j]][j] += f(output[indices[i][j]][j], updates[i][j]) if axis = 0,
+       output[i][indices[i][j]] += f(output[i][indices[i][j]], updates[i][j]) if axis = 1,
 
-    where the ``f`` is +/*/max/min as specified.
+    where the ``f`` is ``+``, ``*``, ``max`` or ``min`` as specified.
 
     This operator is the inverse of GatherElements. It is similar to Torch's
     Scatter operation.
@@ -2492,34 +2519,34 @@ def scatter_elements(
 
     ::
 
-         data = [
-             [0.0, 0.0, 0.0],
-             [0.0, 0.0, 0.0],
-             [0.0, 0.0, 0.0],
-         ]
-         indices = [
-             [1, 0, 2],
-             [0, 2, 1],
-         ]
-         updates = [
-             [1.0, 1.1, 1.2],
-             [2.0, 2.1, 2.2],
-         ]
-         output = [
-             [2.0, 1.1, 0.0]
-             [1.0, 0.0, 2.2]
-             [0.0, 2.1, 1.2]
-         ]
+       data = [
+           [0.0, 0.0, 0.0],
+           [0.0, 0.0, 0.0],
+           [0.0, 0.0, 0.0],
+       ]
+       indices = [
+           [1, 0, 2],
+           [0, 2, 1],
+       ]
+       updates = [
+           [1.0, 1.1, 1.2],
+           [2.0, 2.1, 2.2],
+       ]
+       output = [
+           [2.0, 1.1, 0.0]
+           [1.0, 0.0, 2.2]
+           [0.0, 2.1, 1.2]
+       ]
 
     Example 2:
 
     ::
 
-         data = [[1.0, 2.0, 3.0, 4.0, 5.0]]
-         indices = [[1, 3]]
-         updates = [[1.1, 2.1]]
-         axis = 1
-         output = [[1.0, 1.1, 3.0, 2.1, 5.0]]
+       data = [[1.0, 2.0, 3.0, 4.0, 5.0]]
+       indices = [[1, 3]]
+       updates = [[1.1, 2.1]]
+       axis = 1
+       output = [[1.0, 1.1, 3.0, 2.1, 5.0]]
 
     Parameters
     ==========
@@ -2636,7 +2663,7 @@ def scatter_nd(
        for idx in np.ndindex(update_indices):
            output[indices[idx]] = f(output[indices[idx]], updates[idx])
 
-    where the ``f`` is +/*/max/min as specified.
+    where the ``f`` is ``+``, ``*``, ``max`` or ``min`` as specified.
 
     This operator is the inverse of GatherND.
 
@@ -2646,26 +2673,26 @@ def scatter_nd(
 
     ::
 
-         data    = [1, 2, 3, 4, 5, 6, 7, 8]
-         indices = [[4], [3], [1], [7]]
-         updates = [9, 10, 11, 12]
-         output  = [1, 11, 3, 10, 9, 6, 7, 12]
+       data    = [1, 2, 3, 4, 5, 6, 7, 8]
+       indices = [[4], [3], [1], [7]]
+       updates = [9, 10, 11, 12]
+       output  = [1, 11, 3, 10, 9, 6, 7, 12]
 
     Example 2:
 
     ::
 
-         data    = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
-                    [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
-                    [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
-                    [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
-         indices = [[0], [2]]
-         updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
-                    [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]]
-         output  = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
-                    [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
-                    [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
-                    [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
+       data    = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+                   [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+                   [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+                   [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
+       indices = [[0], [2]]
+       updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+                   [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]]
+       output  = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+                   [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+                   [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+                   [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
 
     Parameters
     ==========
@@ -2714,7 +2741,6 @@ def split(
     input: Var,
     split: Optional[Var] = None,
     *,
-    outputs_count: int,
     axis: int = 0,
     num_outputs: Optional[int] = None,
 ) -> Sequence[Var]:
@@ -2744,9 +2770,6 @@ def split(
         Attribute.
         Number of outputs to split parts of the tensor into. If the tensor is
         not evenly splittable the last chunk will be smaller.
-    outputs_count
-        Specifies the number of variadic outputs of this operator.
-        Non-standard parameter created by the opset generator, as inference (a solution) it was not implemented or is impossible.
 
     Returns
     =======
@@ -2770,7 +2793,7 @@ def split(
             input=input,
             split=split,
         ),
-        out_variadic=outputs_count,
+        out_variadic=num_outputs,
     ).outputs.outputs
 
 
diff --git a/src/spox/opset/ai/onnx/v19.py b/src/spox/opset/ai/onnx/v19.py
new file mode 100644
index 00000000..6695deb6
--- /dev/null
+++ b/src/spox/opset/ai/onnx/v19.py
@@ -0,0 +1,2995 @@
+# flake8: noqa
+import typing  # noqa: F401
+import warnings  # noqa: F401
+from dataclasses import dataclass
+from typing import (  # noqa: F401
+    Any,
+    Callable,
+    Dict,
+    Iterable,
+    List,
+    Optional,
+    Sequence,
+    Tuple,
+    Union,
+)
+from typing import cast as typing_cast  # noqa: F401
+
+import numpy as np  # noqa: F401
+import numpy.typing as npt  # noqa: F401
+
+from spox._attributes import (
+    AttrDtype,
+    AttrFloat32,
+    AttrFloat32s,
+    AttrGraph,
+    AttrInt64,
+    AttrInt64s,
+    AttrString,
+    AttrStrings,
+    AttrTensor,
+    AttrType,
+)
+from spox._fields import BaseAttributes, BaseInputs, BaseOutputs  # noqa: F401
+from spox._graph import Graph, subgraph  # noqa: F401
+from spox._internal_op import intro  # noqa: F401
+from spox._node import OpType  # noqa: F401
+from spox._standard import InferenceError, StandardNode  # noqa: F401
+from spox._type_system import Sequence as SpoxSequence  # noqa: F401
+from spox._type_system import Tensor, Type
+from spox._value_prop import PropValueType  # noqa: F401
+from spox._var import Var, result_type  # noqa: F401
+from spox.opset.ai.onnx.v18 import (
+    _DFT,
+    _GRU,
+    _LRN,
+    _LSTM,
+    _RNN,
+    _STFT,
+    _Abs,
+    _Acos,
+    _Acosh,
+    _Add,
+    _And,
+    _ArgMax,
+    _ArgMin,
+    _Asin,
+    _Asinh,
+    _Atan,
+    _Atanh,
+    _BatchNormalization,
+    _Bernoulli,
+    _BitShift,
+    _BitwiseAnd,
+    _BitwiseNot,
+    _BitwiseOr,
+    _BitwiseXor,
+    _BlackmanWindow,
+    _Ceil,
+    _Celu,
+    _CenterCropPad,
+    _Clip,
+    _Col2Im,
+    _Compress,
+    _Concat,
+    _ConcatFromSequence,
+    _ConstantOfShape,
+    _Conv,
+    _ConvInteger,
+    _ConvTranspose,
+    _Cos,
+    _Cosh,
+    _CumSum,
+    _DepthToSpace,
+    _Det,
+    _Div,
+    _Dropout,
+    _DynamicQuantizeLinear,
+    _Einsum,
+    _Elu,
+    _Erf,
+    _Exp,
+    _Expand,
+    _EyeLike,
+    _Flatten,
+    _Floor,
+    _Gather,
+    _GatherElements,
+    _GatherND,
+    _Gemm,
+    _GlobalAveragePool,
+    _GlobalLpPool,
+    _GlobalMaxPool,
+    _Greater,
+    _GreaterOrEqual,
+    _GridSample,
+    _GroupNormalization,
+    _HammingWindow,
+    _HannWindow,
+    _Hardmax,
+    _HardSigmoid,
+    _HardSwish,
+    _InstanceNormalization,
+    _IsInf,
+    _IsNaN,
+    _LayerNormalization,
+    _LeakyRelu,
+    _Less,
+    _LessOrEqual,
+    _Log,
+    _LogSoftmax,
+    _LpNormalization,
+    _LpPool,
+    _MatMul,
+    _MatMulInteger,
+    _Max,
+    _MaxPool,
+    _MaxRoiPool,
+    _MaxUnpool,
+    _Mean,
+    _MeanVarianceNormalization,
+    _MelWeightMatrix,
+    _Min,
+    _Mish,
+    _Mod,
+    _Mul,
+    _Multinomial,
+    _Neg,
+    _NegativeLogLikelihoodLoss,
+    _NonMaxSuppression,
+    _NonZero,
+    _Not,
+    _OneHot,
+    _Optional,
+    _OptionalGetElement,
+    _OptionalHasElement,
+    _Or,
+    _Pow,
+    _PRelu,
+    _QLinearConv,
+    _QLinearMatMul,
+    _RandomNormal,
+    _RandomNormalLike,
+    _RandomUniform,
+    _RandomUniformLike,
+    _Range,
+    _Reciprocal,
+    _ReduceL1,
+    _ReduceL2,
+    _ReduceLogSum,
+    _ReduceLogSumExp,
+    _ReduceMax,
+    _ReduceMean,
+    _ReduceMin,
+    _ReduceProd,
+    _ReduceSum,
+    _ReduceSumSquare,
+    _Relu,
+    _ReverseSequence,
+    _RoiAlign,
+    _Round,
+    _ScatterElements,
+    _ScatterND,
+    _Selu,
+    _SequenceAt,
+    _SequenceConstruct,
+    _SequenceEmpty,
+    _SequenceErase,
+    _SequenceInsert,
+    _SequenceLength,
+    _SequenceMap,
+    _Shrink,
+    _Sigmoid,
+    _Sign,
+    _Sin,
+    _Sinh,
+    _Slice,
+    _Softmax,
+    _SoftmaxCrossEntropyLoss,
+    _Softplus,
+    _Softsign,
+    _SpaceToDepth,
+    _Split,
+    _SplitToSequence,
+    _Sqrt,
+    _Squeeze,
+    _StringNormalizer,
+    _Sub,
+    _Sum,
+    _Tan,
+    _Tanh,
+    _TfIdfVectorizer,
+    _ThresholdedRelu,
+    _Tile,
+    _TopK,
+    _Transpose,
+    _Trilu,
+    _Unique,
+    _Unsqueeze,
+    _Where,
+    _Xor,
+    abs,
+    acos,
+    acosh,
+    add,
+    and_,
+    arg_max,
+    arg_min,
+    asin,
+    asinh,
+    atan,
+    atanh,
+    batch_normalization,
+    bernoulli,
+    bit_shift,
+    bitwise_and,
+    bitwise_not,
+    bitwise_or,
+    bitwise_xor,
+    blackman_window,
+    ceil,
+    celu,
+    center_crop_pad,
+    clip,
+    col2_im,
+    compress,
+    concat,
+    concat_from_sequence,
+    constant_of_shape,
+    conv,
+    conv_integer,
+    conv_transpose,
+    cos,
+    cosh,
+    cumsum,
+    depth_to_space,
+    det,
+    dft,
+    div,
+    dropout,
+    dynamic_quantize_linear,
+    einsum,
+    elu,
+    erf,
+    exp,
+    expand,
+    eye_like,
+    flatten,
+    floor,
+    gather,
+    gather_elements,
+    gather_nd,
+    gemm,
+    global_average_pool,
+    global_lp_pool,
+    global_max_pool,
+    greater,
+    greater_or_equal,
+    grid_sample,
+    group_normalization,
+    gru,
+    hamming_window,
+    hann_window,
+    hard_sigmoid,
+    hard_swish,
+    hardmax,
+    instance_normalization,
+    isinf,
+    isnan,
+    layer_normalization,
+    leaky_relu,
+    less,
+    less_or_equal,
+    log,
+    log_softmax,
+    lp_normalization,
+    lp_pool,
+    lrn,
+    lstm,
+    matmul,
+    matmul_integer,
+    max,
+    max_pool,
+    max_roi_pool,
+    max_unpool,
+    mean,
+    mean_variance_normalization,
+    mel_weight_matrix,
+    min,
+    mish,
+    mod,
+    mul,
+    multinomial,
+    neg,
+    negative_log_likelihood_loss,
+    non_max_suppression,
+    non_zero,
+    not_,
+    one_hot,
+    optional,
+    optional_get_element,
+    optional_has_element,
+    or_,
+    pow,
+    prelu,
+    qlinear_conv,
+    qlinear_matmul,
+    random_normal,
+    random_normal_like,
+    random_uniform,
+    random_uniform_like,
+    range,
+    reciprocal,
+    reduce_l1,
+    reduce_l2,
+    reduce_log_sum,
+    reduce_log_sum_exp,
+    reduce_max,
+    reduce_mean,
+    reduce_min,
+    reduce_prod,
+    reduce_sum,
+    reduce_sum_square,
+    relu,
+    reverse_sequence,
+    rnn,
+    roi_align,
+    round,
+    scatter_elements,
+    scatter_nd,
+    selu,
+    sequence_at,
+    sequence_construct,
+    sequence_empty,
+    sequence_erase,
+    sequence_insert,
+    sequence_length,
+    sequence_map,
+    shrink,
+    sigmoid,
+    sign,
+    sin,
+    sinh,
+    slice,
+    softmax,
+    softmax_cross_entropy_loss,
+    softplus,
+    softsign,
+    space_to_depth,
+    split,
+    split_to_sequence,
+    sqrt,
+    squeeze,
+    stft,
+    string_normalizer,
+    sub,
+    sum,
+    tan,
+    tanh,
+    tf_idf_vectorizer,
+    thresholded_relu,
+    tile,
+    top_k,
+    transpose,
+    trilu,
+    unique,
+    unsqueeze,
+    where,
+    xor,
+)
+
+
+class _AveragePool(StandardNode):
+    @dataclass
+    class Attributes(BaseAttributes):
+        auto_pad: AttrString
+        ceil_mode: AttrInt64
+        count_include_pad: AttrInt64
+        dilations: Optional[AttrInt64s]
+        kernel_shape: AttrInt64s
+        pads: Optional[AttrInt64s]
+        strides: Optional[AttrInt64s]
+
+    @dataclass
+    class Inputs(BaseInputs):
+        X: Var
+
+    @dataclass
+    class Outputs(BaseOutputs):
+        Y: Var
+
+    op_type = OpType("AveragePool", "", 19)
+
+    attrs: Attributes
+    inputs: Inputs
+    outputs: Outputs
+
+
+class _Cast(StandardNode):
+    @dataclass
+    class Attributes(BaseAttributes):
+        saturate: AttrInt64
+        to: AttrDtype
+
+    @dataclass
+    class Inputs(BaseInputs):
+        input: Var
+
+    @dataclass
+    class Outputs(BaseOutputs):
+        output: Var
+
+    op_type = OpType("Cast", "", 19)
+
+    attrs: Attributes
+    inputs: Inputs
+    outputs: Outputs
+
+
+class _CastLike(StandardNode):
+    @dataclass
+    class Attributes(BaseAttributes):
+        saturate: AttrInt64
+
+    @dataclass
+    class Inputs(BaseInputs):
+        input: Var
+        target_type: Var
+
+    @dataclass
+    class Outputs(BaseOutputs):
+        output: Var
+
+    op_type = OpType("CastLike", "", 19)
+
+    attrs: Attributes
+    inputs: Inputs
+    outputs: Outputs
+
+
+class _Constant(StandardNode):
+    @dataclass
+    class Attributes(BaseAttributes):
+        value: Optional[AttrTensor]
+        value_float: Optional[AttrFloat32]
+        value_floats: Optional[AttrFloat32s]
+        value_int: Optional[AttrInt64]
+        value_ints: Optional[AttrInt64s]
+        value_string: Optional[AttrString]
+        value_strings: Optional[AttrStrings]
+
+    Inputs = BaseInputs
+
+    @dataclass
+    class Outputs(BaseOutputs):
+        output: Var
+
+    def propagate_values(self) -> Dict[str, PropValueType]:
+        ((key, raw),) = (
+            (k, v.value) for k, v in self.attrs.get_fields().items() if v is not None
+        )
+        if key == "value":
+            value = raw
+        elif key == "value_float":
+            value = np.array(raw, dtype=np.float32)
+        elif key == "value_int":
+            value = np.array(raw, dtype=np.int64)
+        elif key == "value_string":
+            value = np.array(raw, dtype=np.str_)
+        elif key == "value_floats":
+            value = np.array(list(raw), dtype=np.float32).reshape(-1)
+        elif key == "value_ints":
+            value = np.array(list(raw), dtype=np.int64).reshape(-1)
+        elif key == "value_strings":
+            value = np.array(list(raw), dtype=np.str_).reshape(-1)
+        elif key == "sparse_value":
+            return {}
+        else:
+            raise RuntimeError(
+                f"Could not extract the set Constant value attribute, got: {key}"
+            )
+        return {"output": value}
+
+    op_type = OpType("Constant", "", 19)
+
+    attrs: Attributes
+    inputs: BaseInputs
+    outputs: Outputs
+
+
+class _DeformConv(StandardNode):
+    @dataclass
+    class Attributes(BaseAttributes):
+        dilations: Optional[AttrInt64s]
+        group: AttrInt64
+        kernel_shape: Optional[AttrInt64s]
+        offset_group: AttrInt64
+        pads: Optional[AttrInt64s]
+        strides: Optional[AttrInt64s]
+
+    @dataclass
+    class Inputs(BaseInputs):
+        X: Var
+        W: Var
+        offset: Var
+        B: Optional[Var]
+        mask: Optional[Var]
+
+    @dataclass
+    class Outputs(BaseOutputs):
+        Y: Var
+
+    op_type = OpType("DeformConv", "", 19)
+
+    attrs: Attributes
+    inputs: Inputs
+    outputs: Outputs
+
+
+class _DequantizeLinear(StandardNode):
+    @dataclass
+    class Attributes(BaseAttributes):
+        axis: AttrInt64
+
+    @dataclass
+    class Inputs(BaseInputs):
+        x: Var
+        x_scale: Var
+        x_zero_point: Optional[Var]
+
+    @dataclass
+    class Outputs(BaseOutputs):
+        y: Var
+
+    op_type = OpType("DequantizeLinear", "", 19)
+
+    attrs: Attributes
+    inputs: Inputs
+    outputs: Outputs
+
+
+class _Equal(StandardNode):
+    @dataclass
+    class Attributes(BaseAttributes):
+        pass
+
+    @dataclass
+    class Inputs(BaseInputs):
+        A: Var
+        B: Var
+
+    @dataclass
+    class Outputs(BaseOutputs):
+        C: Var
+
+    op_type = OpType("Equal", "", 19)
+
+    attrs: Attributes
+    inputs: Inputs
+    outputs: Outputs
+
+
+class _Identity(StandardNode):
+    @dataclass
+    class Attributes(BaseAttributes):
+        pass
+
+    @dataclass
+    class Inputs(BaseInputs):
+        input: Var
+
+    @dataclass
+    class Outputs(BaseOutputs):
+        output: Var
+
+    op_type = OpType("Identity", "", 19)
+
+    attrs: Attributes
+    inputs: Inputs
+    outputs: Outputs
+
+
+class _If(StandardNode):
+    @dataclass
+    class Attributes(BaseAttributes):
+        else_branch: AttrGraph
+        then_branch: AttrGraph
+
+    @dataclass
+    class Inputs(BaseInputs):
+        cond: Var
+
+    @dataclass
+    class Outputs(BaseOutputs):
+        outputs: Sequence[Var]
+
+    op_type = OpType("If", "", 19)
+
+    attrs: Attributes
+    inputs: Inputs
+    outputs: Outputs
+
+
+class _Loop(StandardNode):
+    @dataclass
+    class Attributes(BaseAttributes):
+        body: AttrGraph
+
+    @dataclass
+    class Inputs(BaseInputs):
+        M: Optional[Var]
+        cond: Optional[Var]
+        v_initial: Sequence[Var]
+
+    @dataclass
+    class Outputs(BaseOutputs):
+        v_final_and_scan_outputs: Sequence[Var]
+
+    op_type = OpType("Loop", "", 19)
+
+    attrs: Attributes
+    inputs: Inputs
+    outputs: Outputs
+
+
+class _Pad(StandardNode):
+    @dataclass
+    class Attributes(BaseAttributes):
+        mode: AttrString
+
+    @dataclass
+    class Inputs(BaseInputs):
+        data: Var
+        pads: Var
+        constant_value: Optional[Var]
+        axes: Optional[Var]
+
+    @dataclass
+    class Outputs(BaseOutputs):
+        output: Var
+
+    op_type = OpType("Pad", "", 19)
+
+    attrs: Attributes
+    inputs: Inputs
+    outputs: Outputs
+
+
+class _QuantizeLinear(StandardNode):
+    @dataclass
+    class Attributes(BaseAttributes):
+        axis: AttrInt64
+        saturate: AttrInt64
+
+    @dataclass
+    class Inputs(BaseInputs):
+        x: Var
+        y_scale: Var
+        y_zero_point: Optional[Var]
+
+    @dataclass
+    class Outputs(BaseOutputs):
+        y: Var
+
+    op_type = OpType("QuantizeLinear", "", 19)
+
+    attrs: Attributes
+    inputs: Inputs
+    outputs: Outputs
+
+
+class _Reshape(StandardNode):
+    @dataclass
+    class Attributes(BaseAttributes):
+        allowzero: AttrInt64
+
+    @dataclass
+    class Inputs(BaseInputs):
+        data: Var
+        shape: Var
+
+    @dataclass
+    class Outputs(BaseOutputs):
+        reshaped: Var
+
+    op_type = OpType("Reshape", "", 19)
+
+    attrs: Attributes
+    inputs: Inputs
+    outputs: Outputs
+
+
+class _Resize(StandardNode):
+    @dataclass
+    class Attributes(BaseAttributes):
+        antialias: AttrInt64
+        axes: Optional[AttrInt64s]
+        coordinate_transformation_mode: AttrString
+        cubic_coeff_a: AttrFloat32
+        exclude_outside: AttrInt64
+        extrapolation_value: AttrFloat32
+        keep_aspect_ratio_policy: AttrString
+        mode: AttrString
+        nearest_mode: AttrString
+
+    @dataclass
+    class Inputs(BaseInputs):
+        X: Var
+        roi: Optional[Var]
+        scales: Optional[Var]
+        sizes: Optional[Var]
+
+    @dataclass
+    class Outputs(BaseOutputs):
+        Y: Var
+
+    op_type = OpType("Resize", "", 19)
+
+    attrs: Attributes
+    inputs: Inputs
+    outputs: Outputs
+
+
+class _Scan(StandardNode):
+    @dataclass
+    class Attributes(BaseAttributes):
+        body: AttrGraph
+        num_scan_inputs: AttrInt64
+        scan_input_axes: Optional[AttrInt64s]
+        scan_input_directions: Optional[AttrInt64s]
+        scan_output_axes: Optional[AttrInt64s]
+        scan_output_directions: Optional[AttrInt64s]
+
+    @dataclass
+    class Inputs(BaseInputs):
+        initial_state_and_scan_inputs: Sequence[Var]
+
+    @dataclass
+    class Outputs(BaseOutputs):
+        final_state_and_scan_outputs: Sequence[Var]
+
+    op_type = OpType("Scan", "", 19)
+
+    attrs: Attributes
+    inputs: Inputs
+    outputs: Outputs
+
+
+class _Shape(StandardNode):
+    @dataclass
+    class Attributes(BaseAttributes):
+        end: Optional[AttrInt64]
+        start: AttrInt64
+
+    @dataclass
+    class Inputs(BaseInputs):
+        data: Var
+
+    @dataclass
+    class Outputs(BaseOutputs):
+        shape: Var
+
+    op_type = OpType("Shape", "", 19)
+
+    attrs: Attributes
+    inputs: Inputs
+    outputs: Outputs
+
+
+class _Size(StandardNode):
+    @dataclass
+    class Attributes(BaseAttributes):
+        pass
+
+    @dataclass
+    class Inputs(BaseInputs):
+        data: Var
+
+    @dataclass
+    class Outputs(BaseOutputs):
+        size: Var
+
+    op_type = OpType("Size", "", 19)
+
+    attrs: Attributes
+    inputs: Inputs
+    outputs: Outputs
+
+
+def average_pool(
+    X: Var,
+    *,
+    auto_pad: str = "NOTSET",
+    ceil_mode: int = 0,
+    count_include_pad: int = 0,
+    dilations: Optional[Iterable[int]] = None,
+    kernel_shape: Iterable[int],
+    pads: Optional[Iterable[int]] = None,
+    strides: Optional[Iterable[int]] = None,
+) -> Var:
+    r"""
+    AveragePool consumes an input tensor X and applies average pooling
+    across the tensor according to kernel sizes, stride sizes, and pad
+    lengths. average pooling consisting of computing the average on all
+    values of a subset of the input tensor according to the kernel size and
+    downsampling the data into the output tensor Y for further processing.
+    The output spatial shape will be following:
+
+    ::
+
+       output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1)
+
+    or
+
+    ::
+
+       output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1)
+
+    if ceil_mode is enabled ``pad_shape[i]`` is the sum of pads along axis
+    ``i``.
+
+    ``auto_pad`` is a DEPRECATED attribute. If you are using them currently,
+    the output spatial shape will be following:
+
+    ::
+
+       VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])
+       SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
+
+    And pad shape will be following if ``SAME_UPPER`` or ``SAME_LOWER``:
+
+    ::
+
+       pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i]
+
+    The output of each pooling window is divided by the number of elements
+    (exclude pad when attribute count_include_pad is zero).
+
+    Parameters
+    ==========
+    X
+        Type T.
+        Input data tensor from the previous operator; dimensions for image case
+        are (N x C x H x W), where N is the batch size, C is the number of
+        channels, and H and W are the height and the width of the data. For non
+        image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn),
+        where N is the batch size. Optionally, if dimension denotation is in
+        effect, the operation expects the input data tensor to arrive with the
+        dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE,
+        DATA_FEATURE ...].
+    auto_pad
+        Attribute.
+        auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where
+        default value is NOTSET, which means explicit padding is used.
+        SAME_UPPER or SAME_LOWER mean pad the input so that
+        ``output_shape[i] = ceil(input_shape[i] / strides[i])`` for each axis
+        ``i``. The padding is split between the two sides equally or almost
+        equally (depending on whether it is even or odd). In case the padding is
+        an odd number, the extra padding is added at the end for SAME_UPPER and
+        at the beginning for SAME_LOWER.
+    ceil_mode
+        Attribute.
+        Whether to use ceil or floor (default) to compute the output shape.
+    count_include_pad
+        Attribute.
+        Whether include pad pixels when calculating values for the edges.
+        Default is 0, doesn't count include pad.
+    dilations
+        Attribute.
+        Dilation value along each spatial axis of filter. If not present, the
+        dilation defaults to 1 along each spatial axis.
+    kernel_shape
+        Attribute.
+        The size of the kernel along each axis.
+    pads
+        Attribute.
+        Padding for the beginning and ending along each spatial axis, it can
+        take any value greater than or equal to 0. The value represent the
+        number of pixels added to the beginning and end part of the
+        corresponding axis. ``pads`` format should be as follow [x1_begin,
+        x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels
+        added at the beginning of axis ``i`` and xi_end, the number of pixels
+        added at the end of axis ``i``. This attribute cannot be used
+        simultaneously with auto_pad attribute. If not present, the padding
+        defaults to 0 along start and end of each spatial axis.
+    strides
+        Attribute.
+        Stride along each spatial axis. If not present, the stride defaults to 1
+        along each spatial axis.
+
+    Returns
+    =======
+    Y : Var
+        Type T.
+        Output data tensor from average or max pooling across the input tensor.
+        Dimensions will vary based on various kernel, stride, and pad sizes.
+        Floor value of the dimension is used
+
+    Notes
+    =====
+    Signature: ``ai.onnx@19::AveragePool``.
+
+    Type constraints:
+     - T: `tensor(double)`, `tensor(float)`, `tensor(float16)`
+    """
+    return _AveragePool(
+        _AveragePool.Attributes(
+            auto_pad=AttrString(auto_pad),
+            ceil_mode=AttrInt64(ceil_mode),
+            count_include_pad=AttrInt64(count_include_pad),
+            dilations=AttrInt64s.maybe(dilations),
+            kernel_shape=AttrInt64s(kernel_shape),
+            pads=AttrInt64s.maybe(pads),
+            strides=AttrInt64s.maybe(strides),
+        ),
+        _AveragePool.Inputs(
+            X=X,
+        ),
+    ).outputs.Y
+
+
+def cast(
+    input: Var,
+    *,
+    saturate: int = 1,
+    to: npt.DTypeLike,
+) -> Var:
+    r"""
+    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
+    [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
+    +/- Inf        NaN    NaN      +/- Inf NaN
+    [x] > FLT_MAX  NaN    NaN      Inf     NaN
+    [x] < -FLT_MAX NaN    NaN      -Inf    NaN
+    else           RNE    RNE      RNE     RNE
+    ============== ====== ======== ======= ========
+
+    Parameters
+    ==========
+    input
+        Type T1.
+        Input tensor to be cast.
+    saturate
+        Attribute.
+        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
+        Attribute.
+        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
+
+    Returns
+    =======
+    output : Var
+        Type T2.
+        Output tensor with the same shape as input with type specified by the
+        'to' argument
+
+    Notes
+    =====
+    Signature: ``ai.onnx@19::Cast``.
+
+    Type constraints:
+     - T1: `tensor(bfloat16)`, `tensor(bool)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(float8e4m3fn)`, `tensor(float8e4m3fnuz)`, `tensor(float8e5m2)`, `tensor(float8e5m2fnuz)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)`
+     - T2: `tensor(bfloat16)`, `tensor(bool)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(float8e4m3fn)`, `tensor(float8e4m3fnuz)`, `tensor(float8e5m2)`, `tensor(float8e5m2fnuz)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)`
+    """
+    return _Cast(
+        _Cast.Attributes(
+            saturate=AttrInt64(saturate),
+            to=AttrDtype(to),
+        ),
+        _Cast.Inputs(
+            input=input,
+        ),
+    ).outputs.output
+
+
+def cast_like(
+    input: Var,
+    target_type: Var,
+    *,
+    saturate: int = 1,
+) -> Var:
+    r"""
+    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.
+
+    Parameters
+    ==========
+    input
+        Type T1.
+        Input tensor to be cast.
+    target_type
+        Type T2.
+        The (first) input tensor will be cast to produce a tensor of the same
+        type as this (second input) tensor.
+    saturate
+        Attribute.
+        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.
+
+    Returns
+    =======
+    output : Var
+        Type T2.
+        Output tensor produced by casting the first input tensor to have the
+        same type as the second input tensor.
+
+    Notes
+    =====
+    Signature: ``ai.onnx@19::CastLike``.
+
+    Type constraints:
+     - T1: `tensor(bfloat16)`, `tensor(bool)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(float8e4m3fn)`, `tensor(float8e4m3fnuz)`, `tensor(float8e5m2)`, `tensor(float8e5m2fnuz)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)`
+     - T2: `tensor(bfloat16)`, `tensor(bool)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(float8e4m3fn)`, `tensor(float8e4m3fnuz)`, `tensor(float8e5m2)`, `tensor(float8e5m2fnuz)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)`
+    """
+    return _CastLike(
+        _CastLike.Attributes(
+            saturate=AttrInt64(saturate),
+        ),
+        _CastLike.Inputs(
+            input=input,
+            target_type=target_type,
+        ),
+    ).outputs.output
+
+
+def constant(
+    *,
+    value: Optional[np.ndarray] = None,
+    value_float: Optional[float] = None,
+    value_floats: Optional[Iterable[float]] = None,
+    value_int: Optional[int] = None,
+    value_ints: Optional[Iterable[int]] = None,
+    value_string: Optional[str] = None,
+    value_strings: Optional[Iterable[str]] = None,
+) -> Var:
+    r"""
+    This operator produces a constant tensor. Exactly one of the provided
+    attributes, either value, sparse_value, or value\_\* must be specified.
+
+    Parameters
+    ==========
+    sparse_value
+        Attribute.
+        The value for the elements of the output tensor in sparse format.
+    value
+        Attribute.
+        The value for the elements of the output tensor.
+    value_float
+        Attribute.
+        The value for the sole element for the scalar, float32, output tensor.
+    value_floats
+        Attribute.
+        The values for the elements for the 1D, float32, output tensor.
+    value_int
+        Attribute.
+        The value for the sole element for the scalar, int64, output tensor.
+    value_ints
+        Attribute.
+        The values for the elements for the 1D, int64, output tensor.
+    value_string
+        Attribute.
+        The value for the sole element for the scalar, UTF-8 string, output
+        tensor.
+    value_strings
+        Attribute.
+        The values for the elements for the 1D, UTF-8 string, output tensor.
+
+    Returns
+    =======
+    output : Var
+        Type T.
+        Output tensor containing the same value of the provided tensor.
+
+    Notes
+    =====
+    Signature: ``ai.onnx@19::Constant``.
+
+    Type constraints:
+     - T: `tensor(bfloat16)`, `tensor(bool)`, `tensor(complex128)`, `tensor(complex64)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(float8e4m3fn)`, `tensor(float8e4m3fnuz)`, `tensor(float8e5m2)`, `tensor(float8e5m2fnuz)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)`
+    """
+    return _Constant(
+        _Constant.Attributes(
+            value=AttrTensor.maybe(value),
+            value_float=AttrFloat32.maybe(value_float),
+            value_floats=AttrFloat32s.maybe(value_floats),
+            value_int=AttrInt64.maybe(value_int),
+            value_ints=AttrInt64s.maybe(value_ints),
+            value_string=AttrString.maybe(value_string),
+            value_strings=AttrStrings.maybe(value_strings),
+        ),
+        _Constant.Inputs(),
+    ).outputs.output
+
+
+def deform_conv(
+    X: Var,
+    W: Var,
+    offset: Var,
+    B: Optional[Var] = None,
+    mask: Optional[Var] = None,
+    *,
+    dilations: Optional[Iterable[int]] = None,
+    group: int = 1,
+    kernel_shape: Optional[Iterable[int]] = None,
+    offset_group: int = 1,
+    pads: Optional[Iterable[int]] = None,
+    strides: Optional[Iterable[int]] = None,
+) -> Var:
+    r"""
+    Performs deformable convolution as described in
+    https://arxiv.org/abs/1703.06211 and https://arxiv.org/abs/1811.11168.
+    This operator specification supports the general N-D case. Note that
+    most common use cases have 2D or 3D data.
+
+    Parameters
+    ==========
+    X
+        Type T.
+        Input data tensor. For 2D image data, it has shape (N, C, H, W) where N
+        is the batch size, C is the number of input channels, and H and W are
+        the height and width. In general, the shape is (N, C, D1, D2, ... , Dn)
+        for n-dimensional data, where D1 to Dn are the spatial dimension sizes.
+        Most common use cases have n = 2 or 3.
+    W
+        Type T.
+        Weight tensor that will be used in the convolutions. It has shape (oC,
+        C/group, kH, kW), where oC is the number of output channels and kH and
+        kW are the kernel height and width. For more than 2 dimensions, it has
+        shape (oC, C/group, k1, k2, ... , kn).
+    offset
+        Type T.
+        Offset tensor denoting the offset for the sampling locations in the
+        convolution kernel. It has shape (N, offset_group \* kH \* kW \* 2, oH,
+        oW) for 2D data or (N, offset_group \* k1 \* k2 \* ... \* kn \* n, o1,
+        o2, ... , on) for nD data. Use linear interpolationfor fractional offset
+        values. Sampling locations outside of the padded input tensor gives
+        zero.
+    B
+        Type T.
+        Optional 1D bias of length oC to be added to the convolution. Default is
+        a tensor of zeros.
+    mask
+        Type T.
+        The mask tensor to be applied to each position in the convolution
+        kernel. It has shape (N, offset_group \* kH \* kW, oH, oW) for 2D data
+        or (N, offset_group \* k1 \* k2 \* ... \* kn \* n, o1, o2, ... , on) for
+        nD data. Default is a tensor of ones.
+    dilations
+        Attribute.
+        Dilation value along each spatial axis of the kernel. Default is 1 along
+        each axis.
+    group
+        Attribute.
+        Number of groups the input and output channels, C and oC, are divided
+        into. C and oC must both be divisible by group. Default is 1.
+    kernel_shape
+        Attribute.
+        Shape of the convolution kernel. If not present, it is inferred from the
+        shape of input W.
+    offset_group
+        Attribute.
+        Number of groups of offset. C must be divisible by offset_group. Default
+        is 1.
+    pads
+        Attribute.
+        Padding for the beginning and end along each spatial axis. The values
+        represent the number of pixels added to the beginning and end of the
+        corresponding axis and can take any nonnegative value. The format should
+        be as follows: [x1_begin, x2_begin, ..., x1_end, x2_end, ...], where
+        xi_begin is the number of pixels added at the beginning of axis ``i``
+        and xi_end is the number of pixels added at the end of axis ``i``.
+        Default is 0 along each axis.
+    strides
+        Attribute.
+        Stride along each spatial axis. Default is 1 along each axis.
+
+    Returns
+    =======
+    Y : Var
+        Type T.
+        Output data tensor that contains the result of convolution. It has shape
+        (N, oC, oH, oW) for 2D data or (N, oC, o1, o2, ..., on) for nD data
+
+    Notes
+    =====
+    Signature: ``ai.onnx@19::DeformConv``.
+
+    Type constraints:
+     - T: `tensor(double)`, `tensor(float)`, `tensor(float16)`
+    """
+    return _DeformConv(
+        _DeformConv.Attributes(
+            dilations=AttrInt64s.maybe(dilations),
+            group=AttrInt64(group),
+            kernel_shape=AttrInt64s.maybe(kernel_shape),
+            offset_group=AttrInt64(offset_group),
+            pads=AttrInt64s.maybe(pads),
+            strides=AttrInt64s.maybe(strides),
+        ),
+        _DeformConv.Inputs(
+            X=X,
+            W=W,
+            offset=offset,
+            B=B,
+            mask=mask,
+        ),
+    ).outputs.Y
+
+
+def dequantize_linear(
+    x: Var,
+    x_scale: Var,
+    x_zero_point: Optional[Var] = None,
+    *,
+    axis: int = 1,
+) -> Var:
+    r"""
+    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 same shape, and can be either
+    a scalar for per-tensor / per layer quantization, or a 1-D tensor for
+    per-axis quantization. ``x_zero_point`` and ``x`` must have same type.
+    ``x`` and ``y`` must have 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 float8e4m3fn, float8e4m3fnuz,
+    float8e5m2, float8e5m2fnuz quantization, but the dequantization formula
+    remains the same for consistency and 'x_scale' still determines the
+    output type.
+
+    Parameters
+    ==========
+    x
+        Type T1.
+        N-D quantized input tensor to be de-quantized.
+    x_scale
+        Type T2.
+        Scale for input 'x'. It can be a scalar, which means a per-tensor/layer
+        dequantization, or a 1-D tensor for per-axis dequantization.
+    x_zero_point
+        Type T1.
+        Zero point for input 'x'. Shape must match x_scale. It's optional. Zero
+        point is 0 when it's not specified.
+    axis
+        Attribute.
+        (Optional) The axis of the dequantizing dimension of the input tensor.
+        Ignored for per-tensor quantization. Negative value means counting
+        dimensions from the back. Accepted range is [-r, r-1] where r =
+        rank(input).
+
+    Returns
+    =======
+    y : Var
+        Type T2.
+        N-D full precision output tensor. It has same shape as input 'x'.
+
+    Notes
+    =====
+    Signature: ``ai.onnx@19::DequantizeLinear``.
+
+    Type constraints:
+     - T1: `tensor(float8e4m3fn)`, `tensor(float8e4m3fnuz)`, `tensor(float8e5m2)`, `tensor(float8e5m2fnuz)`, `tensor(int32)`, `tensor(int8)`, `tensor(uint8)`
+     - T2: `tensor(bfloat16)`, `tensor(float)`, `tensor(float16)`
+    """
+    return _DequantizeLinear(
+        _DequantizeLinear.Attributes(
+            axis=AttrInt64(axis),
+        ),
+        _DequantizeLinear.Inputs(
+            x=x,
+            x_scale=x_scale,
+            x_zero_point=x_zero_point,
+        ),
+    ).outputs.y
+
+
+def equal(
+    A: Var,
+    B: Var,
+) -> Var:
+    r"""
+    Returns the tensor resulted from performing the ``equal`` logical
+    operation elementwise on the input tensors ``A`` and ``B`` (with
+    Numpy-style broadcasting support).
+
+    This operator supports **multidirectional (i.e., Numpy-style)
+    broadcasting**; for more details please check `the
+    doc <https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md>`__.
+
+    Parameters
+    ==========
+    A
+        Type T.
+        First input operand for the logical operator.
+    B
+        Type T.
+        Second input operand for the logical operator.
+
+    Returns
+    =======
+    C : Var
+        Type T1.
+        Result tensor.
+
+    Notes
+    =====
+    Signature: ``ai.onnx@19::Equal``.
+
+    Type constraints:
+     - T: `tensor(bfloat16)`, `tensor(bool)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)`
+     - T1: `tensor(bool)`
+    """
+    return _Equal(
+        _Equal.Attributes(),
+        _Equal.Inputs(
+            A=A,
+            B=B,
+        ),
+    ).outputs.C
+
+
+def identity(
+    input: Var,
+) -> Var:
+    r"""
+    Identity operator
+
+    Parameters
+    ==========
+    input
+        Type V.
+        Input tensor
+
+    Returns
+    =======
+    output : Var
+        Type V.
+        Tensor to copy input into.
+
+    Notes
+    =====
+    Signature: ``ai.onnx@19::Identity``.
+
+    Type constraints:
+     - V: `optional(seq(tensor(bool)))`, `optional(seq(tensor(complex128)))`, `optional(seq(tensor(complex64)))`, `optional(seq(tensor(double)))`, `optional(seq(tensor(float)))`, `optional(seq(tensor(float16)))`, `optional(seq(tensor(int16)))`, `optional(seq(tensor(int32)))`, `optional(seq(tensor(int64)))`, `optional(seq(tensor(int8)))`, `optional(seq(tensor(string)))`, `optional(seq(tensor(uint16)))`, `optional(seq(tensor(uint32)))`, `optional(seq(tensor(uint64)))`, `optional(seq(tensor(uint8)))`, `optional(tensor(bool))`, `optional(tensor(complex128))`, `optional(tensor(complex64))`, `optional(tensor(double))`, `optional(tensor(float))`, `optional(tensor(float16))`, `optional(tensor(int16))`, `optional(tensor(int32))`, `optional(tensor(int64))`, `optional(tensor(int8))`, `optional(tensor(string))`, `optional(tensor(uint16))`, `optional(tensor(uint32))`, `optional(tensor(uint64))`, `optional(tensor(uint8))`, `seq(tensor(bool))`, `seq(tensor(complex128))`, `seq(tensor(complex64))`, `seq(tensor(double))`, `seq(tensor(float))`, `seq(tensor(float16))`, `seq(tensor(int16))`, `seq(tensor(int32))`, `seq(tensor(int64))`, `seq(tensor(int8))`, `seq(tensor(string))`, `seq(tensor(uint16))`, `seq(tensor(uint32))`, `seq(tensor(uint64))`, `seq(tensor(uint8))`, `tensor(bfloat16)`, `tensor(bool)`, `tensor(complex128)`, `tensor(complex64)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(float8e4m3fn)`, `tensor(float8e4m3fnuz)`, `tensor(float8e5m2)`, `tensor(float8e5m2fnuz)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)`
+    """
+    return _Identity(
+        _Identity.Attributes(),
+        _Identity.Inputs(
+            input=input,
+        ),
+    ).outputs.output
+
+
+def if_(
+    cond: Var,
+    *,
+    else_branch: Callable[[], Iterable[Var]],
+    then_branch: Callable[[], Iterable[Var]],
+) -> Sequence[Var]:
+    r"""
+    If conditional
+
+    Parameters
+    ==========
+    cond
+        Type B.
+        Condition for the if
+    else_branch
+        Attribute.
+        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
+        Attribute.
+        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.
+
+    Returns
+    =======
+    outputs : Sequence[Var]
+        Type V.
+        Values that are live-out to the enclosing scope. The return values in
+        the ``then_branch`` and ``else_branch`` must be of the same data type.
+        The ``then_branch`` and ``else_branch`` may produce tensors with the
+        same element type and different shapes. If corresponding outputs from
+        the then-branch and the else-branch have static shapes S1 and S2, then
+        the shape of the corresponding output variable of the if-node (if
+        present) must be compatible with both S1 and S2 as it represents the
+        union of both possible shapes.For example, if in a model file, the first
+        output of ``then_branch`` is typed float tensor with shape [2] and the
+        first output of ``else_branch`` is another float tensor with shape [3],
+        If's first output should have (a) no shape set, or (b) a shape of rank 1
+        with neither ``dim_value`` nor ``dim_param`` set, or (c) a shape of rank
+        1 with a unique ``dim_param``. In contrast, the first output cannot have
+        the shape [2] since [2] and [3] are not compatible.
+
+    Notes
+    =====
+    Signature: ``ai.onnx@19::If``.
+
+    Type constraints:
+     - B: `tensor(bool)`
+     - V: `optional(seq(tensor(bfloat16)))`, `optional(seq(tensor(bool)))`, `optional(seq(tensor(complex128)))`, `optional(seq(tensor(complex64)))`, `optional(seq(tensor(double)))`, `optional(seq(tensor(float)))`, `optional(seq(tensor(float16)))`, `optional(seq(tensor(int16)))`, `optional(seq(tensor(int32)))`, `optional(seq(tensor(int64)))`, `optional(seq(tensor(int8)))`, `optional(seq(tensor(string)))`, `optional(seq(tensor(uint16)))`, `optional(seq(tensor(uint32)))`, `optional(seq(tensor(uint64)))`, `optional(seq(tensor(uint8)))`, `optional(tensor(bfloat16))`, `optional(tensor(bool))`, `optional(tensor(complex128))`, `optional(tensor(complex64))`, `optional(tensor(double))`, `optional(tensor(float))`, `optional(tensor(float16))`, `optional(tensor(float8e4m3fn))`, `optional(tensor(float8e4m3fnuz))`, `optional(tensor(float8e5m2))`, `optional(tensor(float8e5m2fnuz))`, `optional(tensor(int16))`, `optional(tensor(int32))`, `optional(tensor(int64))`, `optional(tensor(int8))`, `optional(tensor(string))`, `optional(tensor(uint16))`, `optional(tensor(uint32))`, `optional(tensor(uint64))`, `optional(tensor(uint8))`, `seq(tensor(bfloat16))`, `seq(tensor(bool))`, `seq(tensor(complex128))`, `seq(tensor(complex64))`, `seq(tensor(double))`, `seq(tensor(float))`, `seq(tensor(float16))`, `seq(tensor(float8e4m3fn))`, `seq(tensor(float8e4m3fnuz))`, `seq(tensor(float8e5m2))`, `seq(tensor(float8e5m2fnuz))`, `seq(tensor(int16))`, `seq(tensor(int32))`, `seq(tensor(int64))`, `seq(tensor(int8))`, `seq(tensor(string))`, `seq(tensor(uint16))`, `seq(tensor(uint32))`, `seq(tensor(uint64))`, `seq(tensor(uint8))`, `tensor(bfloat16)`, `tensor(bool)`, `tensor(complex128)`, `tensor(complex64)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(float8e4m3fn)`, `tensor(float8e4m3fnuz)`, `tensor(float8e5m2)`, `tensor(float8e5m2fnuz)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)`
+    """
+    _else_branch_subgraph: Graph = subgraph((), else_branch)
+    _then_branch_subgraph: Graph = subgraph((), then_branch)
+    return _If(
+        _If.Attributes(
+            else_branch=AttrGraph(_else_branch_subgraph),
+            then_branch=AttrGraph(_then_branch_subgraph),
+        ),
+        _If.Inputs(
+            cond=cond,
+        ),
+        out_variadic=len(_else_branch_subgraph.requested_results),
+    ).outputs.outputs
+
+
+def loop(
+    M: Optional[Var] = None,
+    cond: Optional[Var] = None,
+    v_initial: Sequence[Var] = (),
+    *,
+    body: Callable[..., Iterable[Var]],
+) -> Sequence[Var]:
+    r"""
+    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.
+
+    Parameters
+    ==========
+    M
+        Type I.
+        A maximum trip-count for the loop specified at runtime. Optional. Pass
+        empty string to skip.
+    cond
+        Type B.
+        A boolean termination condition. Optional. Pass empty string to skip.
+    v_initial
+        Type V.
+        The initial values of any loop-carried dependencies (values that change
+        across loop iterations)
+    body
+        Attribute.
+        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.
+
+    Returns
+    =======
+    v_final_and_scan_outputs : Sequence[Var]
+        Type V.
+        Final N loop carried dependency values then K scan_outputs. Scan outputs
+        must be Tensors.
+
+    Notes
+    =====
+    Signature: ``ai.onnx@19::Loop``.
+
+    Type constraints:
+     - I: `tensor(int64)`
+     - B: `tensor(bool)`
+     - V: `optional(seq(tensor(bfloat16)))`, `optional(seq(tensor(bool)))`, `optional(seq(tensor(complex128)))`, `optional(seq(tensor(complex64)))`, `optional(seq(tensor(double)))`, `optional(seq(tensor(float)))`, `optional(seq(tensor(float16)))`, `optional(seq(tensor(int16)))`, `optional(seq(tensor(int32)))`, `optional(seq(tensor(int64)))`, `optional(seq(tensor(int8)))`, `optional(seq(tensor(string)))`, `optional(seq(tensor(uint16)))`, `optional(seq(tensor(uint32)))`, `optional(seq(tensor(uint64)))`, `optional(seq(tensor(uint8)))`, `optional(tensor(bfloat16))`, `optional(tensor(bool))`, `optional(tensor(complex128))`, `optional(tensor(complex64))`, `optional(tensor(double))`, `optional(tensor(float))`, `optional(tensor(float16))`, `optional(tensor(float8e4m3fn))`, `optional(tensor(float8e4m3fnuz))`, `optional(tensor(float8e5m2))`, `optional(tensor(float8e5m2fnuz))`, `optional(tensor(int16))`, `optional(tensor(int32))`, `optional(tensor(int64))`, `optional(tensor(int8))`, `optional(tensor(string))`, `optional(tensor(uint16))`, `optional(tensor(uint32))`, `optional(tensor(uint64))`, `optional(tensor(uint8))`, `seq(tensor(bfloat16))`, `seq(tensor(bool))`, `seq(tensor(complex128))`, `seq(tensor(complex64))`, `seq(tensor(double))`, `seq(tensor(float))`, `seq(tensor(float16))`, `seq(tensor(float8e4m3fn))`, `seq(tensor(float8e4m3fnuz))`, `seq(tensor(float8e5m2))`, `seq(tensor(float8e5m2fnuz))`, `seq(tensor(int16))`, `seq(tensor(int32))`, `seq(tensor(int64))`, `seq(tensor(int8))`, `seq(tensor(string))`, `seq(tensor(uint16))`, `seq(tensor(uint32))`, `seq(tensor(uint64))`, `seq(tensor(uint8))`, `tensor(bfloat16)`, `tensor(bool)`, `tensor(complex128)`, `tensor(complex64)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(float8e4m3fn)`, `tensor(float8e4m3fnuz)`, `tensor(float8e5m2)`, `tensor(float8e5m2fnuz)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)`
+    """
+    _body_subgraph: Graph = subgraph(
+        typing_cast(List[Type], [Tensor(np.int64, (1,)), Tensor(np.bool_, (1,))])
+        + [var.unwrap_type() for var in v_initial],
+        body,
+    )
+    return _Loop(
+        _Loop.Attributes(
+            body=AttrGraph(_body_subgraph),
+        ),
+        _Loop.Inputs(
+            M=M,
+            cond=cond,
+            v_initial=v_initial,
+        ),
+        out_variadic=len(_body_subgraph.requested_results) - 1,
+    ).outputs.v_final_and_scan_outputs
+
+
+def pad(
+    data: Var,
+    pads: Var,
+    constant_value: Optional[Var] = None,
+    axes: Optional[Var] = None,
+    *,
+    mode: str = "constant",
+) -> Var:
+    r"""
+    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],
+       ]
+
+    Parameters
+    ==========
+    data
+        Type T.
+        Input tensor.
+    pads
+        Type tensor(int64).
+        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
+        Type T.
+        (Optional) A scalar value to be used if the mode chosen is ``constant``
+        (by default it is 0, empty string or False).
+    axes
+        Type Tind.
+        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
+        Attribute.
+        Supported modes: ``constant``\ (default), ``reflect``, ``edge``,
+        ``wrap``
+
+    Returns
+    =======
+    output : Var
+        Type T.
+        Tensor after padding.
+
+    Notes
+    =====
+    Signature: ``ai.onnx@19::Pad``.
+
+    Type constraints:
+     - T: `tensor(bfloat16)`, `tensor(bool)`, `tensor(complex128)`, `tensor(complex64)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)`
+     - Tind: `tensor(int32)`, `tensor(int64)`
+    """
+    return _Pad(
+        _Pad.Attributes(
+            mode=AttrString(mode),
+        ),
+        _Pad.Inputs(
+            data=data,
+            pads=pads,
+            constant_value=constant_value,
+            axes=axes,
+        ),
+    ).outputs.output
+
+
+def quantize_linear(
+    x: Var,
+    y_scale: Var,
+    y_zero_point: Optional[Var] = None,
+    *,
+    axis: int = 1,
+    saturate: int = 1,
+) -> Var:
+    r"""
+    The linear quantization operator. It 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 same shape, and can be either
+    a scalar for per-tensor / per layer quantization, or a 1-D tensor for
+    per-axis quantization. The quantization formula is
+    ``y = saturate ((x / y_scale) + y_zero_point)``. For saturation, it
+    saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8. For (x
+    / y_scale), it's rounding to the nearest even. Refer to
+    https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and
+    'y' must have same type. 'y_zero_point' is usually not used for
+    quantization to float8e4m3fn, float8e4m3fnuz, float8e5m2,
+    float8e5m2fnuz, but the quantization formula remains the same for
+    consistency and the type of the attribute 'y_zero_point' still
+    determines the quantization type.
+
+    Parameters
+    ==========
+    x
+        Type T1.
+        N-D full precision Input tensor to be quantized.
+    y_scale
+        Type T1.
+        Scale for doing quantization to get 'y'. It can be a scalar, which means
+        per-tensor/layer quantization, or a 1-D Tensor for per-axis
+        quantization.
+    y_zero_point
+        Type T2.
+        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
+        Attribute.
+        (Optional) The axis of the quantization dimension of the input tensor.
+        Ignored for per-tensor quantization. Negative value means counting
+        dimensions from the back. Accepted range is [-r, r-1] where r =
+        rank(input).
+    saturate
+        Attribute.
+        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.
+
+    Returns
+    =======
+    y : Var
+        Type T2.
+        N-D quantized output tensor. It has same shape as input 'x'.
+
+    Notes
+    =====
+    Signature: ``ai.onnx@19::QuantizeLinear``.
+
+    Type constraints:
+     - T1: `tensor(bfloat16)`, `tensor(float)`, `tensor(float16)`, `tensor(int32)`
+     - T2: `tensor(float8e4m3fn)`, `tensor(float8e4m3fnuz)`, `tensor(float8e5m2)`, `tensor(float8e5m2fnuz)`, `tensor(int8)`, `tensor(uint8)`
+    """
+    return _QuantizeLinear(
+        _QuantizeLinear.Attributes(
+            axis=AttrInt64(axis),
+            saturate=AttrInt64(saturate),
+        ),
+        _QuantizeLinear.Inputs(
+            x=x,
+            y_scale=y_scale,
+            y_zero_point=y_zero_point,
+        ),
+    ).outputs.y
+
+
+def reshape(
+    data: Var,
+    shape: Var,
+    *,
+    allowzero: int = 0,
+) -> Var:
+    r"""
+    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.
+
+    Parameters
+    ==========
+    data
+        Type T.
+        An input tensor.
+    shape
+        Type tensor(int64).
+        Specified shape for output.
+    allowzero
+        Attribute.
+        (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.
+
+    Returns
+    =======
+    reshaped : Var
+        Type T.
+        Reshaped data.
+
+    Notes
+    =====
+    Signature: ``ai.onnx@19::Reshape``.
+
+    Type constraints:
+     - T: `tensor(bfloat16)`, `tensor(bool)`, `tensor(complex128)`, `tensor(complex64)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(float8e4m3fn)`, `tensor(float8e4m3fnuz)`, `tensor(float8e5m2)`, `tensor(float8e5m2fnuz)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)`
+    """
+    return _Reshape(
+        _Reshape.Attributes(
+            allowzero=AttrInt64(allowzero),
+        ),
+        _Reshape.Inputs(
+            data=data,
+            shape=shape,
+        ),
+    ).outputs.reshaped
+
+
+def resize(
+    X: Var,
+    roi: Optional[Var] = None,
+    scales: Optional[Var] = None,
+    sizes: Optional[Var] = None,
+    *,
+    antialias: int = 0,
+    axes: Optional[Iterable[int]] = None,
+    coordinate_transformation_mode: str = "half_pixel",
+    cubic_coeff_a: float = -0.75,
+    exclude_outside: int = 0,
+    extrapolation_value: float = 0.0,
+    keep_aspect_ratio_policy: str = "stretch",
+    mode: str = "nearest",
+    nearest_mode: str = "round_prefer_floor",
+) -> Var:
+    r"""
+    Resize the input tensor. In general, it calculates every value in the
+    output tensor as a weighted average of neighborhood (a.k.a. sampling
+    locations) in the input tensor. Each dimension value of the output
+    tensor is:
+
+    ::
+
+       output_dimension = floor(input_dimension * (roi_end - roi_start) * scale)
+
+    if input "sizes" is not specified.
+
+    Parameters
+    ==========
+    X
+        Type T1.
+        N-D tensor
+    roi
+        Type T2.
+        1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is
+        the rank of X or the length of axes, if provided. The RoIs' coordinates
+        are normalized in the coordinate system of the input image. It only
+        takes effect when coordinate_transformation_mode is "tf_crop_and_resize"
+    scales
+        Type tensor(float).
+        The scale array along each dimension. It takes value greater than 0. If
+        it's less than 1, it's sampling down, otherwise, it's upsampling. The
+        number of elements of 'scales' should be the same as the rank of input
+        'X' or the length of 'axes', if provided. One of 'scales' and 'sizes'
+        MUST be specified and it is an error if both are specified. If 'sizes'
+        is needed, the user can use an empty string as the name of 'scales' in
+        this operator's input list.
+    sizes
+        Type tensor(int64).
+        Target size of the output tensor. Its interpretation depends on the
+        'keep_aspect_ratio_policy' value.The number of elements of 'sizes'
+        should be the same as the rank of input 'X', or the length of 'axes', if
+        provided. Only one of 'scales' and 'sizes' can be specified.
+    antialias
+        Attribute.
+        If set to 1, "linear" and "cubic" interpolation modes will use an
+        antialiasing filter when downscaling. Antialiasing is achieved by
+        stretching the resampling filter by a factor max(1, 1 / scale), which
+        means that when downsampling, more input pixels contribute to an output
+        pixel.
+    axes
+        Attribute.
+        If provided, it specifies a subset of axes that 'roi', 'scales' and
+        'sizes' refer to. If not provided, all axes are assumed [0, 1, ...,
+        r-1], where r = rank(data). Non-specified dimensions are interpreted as
+        non-resizable. 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.
+    coordinate_transformation_mode
+        Attribute.
+        This attribute describes how to transform the coordinate in the resized
+        tensor to the coordinate in the original tensor.
+
+        The coordinate of each dimension is transformed individually. Let's
+        describe a case using axis x as an example. Denote ``x_resized`` as the
+        coordinate of axis x in the resized tensor, ``x_original`` as the
+        coordinate of axis x in the original tensor, ``length_original`` as the
+        length of the original tensor in axis x, ``length_resized`` as the
+        length of the resized tensor in axis x,
+        ``scale = length_resized / length_original``, ``output_width`` the
+        target length on the axis x which can be a fractional number when it is
+        calculated out of a scale factor, and ``output_width_int`` the effective
+        output width as an integer.
+
+        if coordinate_transformation_mode is ``"half_pixel"``,
+
+        ::
+
+           x_original = (x_resized + 0.5) / scale - 0.5
+
+        if coordinate_transformation_mode is ``"half_pixel_symmetric"``,
+
+        ::
+
+           adjustment = output_width_int / output_width
+           center = input_width / 2
+           offset = center * (1 - adjustment)
+           x_ori = offset + (x + 0.5) / scale - 0.5
+
+        if coordinate_transformation_mode is ``"pytorch_half_pixel"``,
+
+        ::
+
+           x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0
+
+        if coordinate_transformation_mode is ``"align_corners"``,
+
+        ::
+
+           x_original = x_resized * (length_original - 1) / (length_resized - 1)
+
+        if coordinate_transformation_mode is ``"asymmetric"``,
+
+        ::
+
+           x_original = x_resized / scale
+
+        if coordinate_transformation_mode is ``"tf_crop_and_resize"``,
+
+        ::
+
+           x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1)
+
+        .
+    cubic_coeff_a
+        Attribute.
+        The coefficient 'a' used in cubic interpolation. Two common choice are
+        -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out
+        Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the
+        details. This attribute is valid only if mode is "cubic".
+    exclude_outside
+        Attribute.
+        If set to 1, the weight of sampling locations outside the tensor will be
+        set to 0 and the weight will be renormalized so that their sum is 1.0.
+        The default value is 0.
+    extrapolation_value
+        Attribute.
+        When coordinate_transformation_mode is "tf_crop_and_resize" and
+        x_original is outside the range [0, length_original - 1], this value is
+        used as the corresponding output value. Default is 0.0f.
+    keep_aspect_ratio_policy
+        Attribute.
+        This attribute describes how to interpret the ``sizes`` input with
+        regard to keeping the original aspect ratio of the input, and it is not
+        applicable when the ``scales`` input is used.
+
+        Given a set of ``sizes``, associated with a subset of ``axes``
+        (explicitly provided or default), and assuming ``d = axes[i]``, with
+        ``i`` being the index of the provided ``sizes``.
+
+        If ``keep_aspect_ratio_policy`` is ``"stretch"``, the original aspect
+        ratio is disregarded, and the input is resized to the specified size:
+        ``out_size[d] = sizes[i]``
+
+        If ``keep_aspect_ratio_policy`` is ``"not_larger"``, the sizes are
+        adjusted so that no extent of the output is larger than the specified
+        size, while keeping the original aspect ratio:
+
+        ::
+
+           scale = Min(sizes[i] / in_size[d])
+           out_size[d] = round_int(scale * in_size[i])
+
+        If ``keep_aspect_ratio_policy`` is ``"not_smaller"``, the sizes are
+        adjusted so that no extent of the output is smaller than the specified
+        size, while keeping the original aspect ratio:
+
+        ::
+
+           scale = Max(sizes[i] / in_size[d])
+           out_size[d] = round_int(scale * in_size[i])
+
+        For non-resizable axes (those not specified in ``axes``), the output
+        size will be equal to the input size.
+
+        Note: ``round_int`` stands for computing the nearest integer value,
+        rounding halfway cases up.
+    mode
+        Attribute.
+        Three interpolation modes: "nearest" (default), "linear" and "cubic".
+        The "linear" mode includes linear interpolation for 1D tensor and
+        N-linear interpolation for N-D tensor (for example, bilinear
+        interpolation for 2D tensor). The "cubic" mode includes cubic
+        interpolation for 1D tensor and N-cubic interpolation for N-D tensor
+        (for example, bicubic interpolation for 2D tensor).
+    nearest_mode
+        Attribute.
+        Four modes: "round_prefer_floor" (default, as known as round half down),
+        "round_prefer_ceil" (as known as round half up), "floor", "ceil". Only
+        used by nearest interpolation. It indicates how to get "nearest" pixel
+        in input tensor from x_original, so this attribute is valid only if
+        "mode" is "nearest".
+
+    Returns
+    =======
+    Y : Var
+        Type T1.
+        N-D tensor after resizing
+
+    Notes
+    =====
+    Signature: ``ai.onnx@19::Resize``.
+
+    Type constraints:
+     - T1: `tensor(bfloat16)`, `tensor(bool)`, `tensor(complex128)`, `tensor(complex64)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)`
+     - T2: `tensor(double)`, `tensor(float)`, `tensor(float16)`
+    """
+    return _Resize(
+        _Resize.Attributes(
+            antialias=AttrInt64(antialias),
+            axes=AttrInt64s.maybe(axes),
+            coordinate_transformation_mode=AttrString(coordinate_transformation_mode),
+            cubic_coeff_a=AttrFloat32(cubic_coeff_a),
+            exclude_outside=AttrInt64(exclude_outside),
+            extrapolation_value=AttrFloat32(extrapolation_value),
+            keep_aspect_ratio_policy=AttrString(keep_aspect_ratio_policy),
+            mode=AttrString(mode),
+            nearest_mode=AttrString(nearest_mode),
+        ),
+        _Resize.Inputs(
+            X=X,
+            roi=roi,
+            scales=scales,
+            sizes=sizes,
+        ),
+    ).outputs.Y
+
+
+def scan(
+    initial_state_and_scan_inputs: Sequence[Var],
+    *,
+    body: Callable[..., Iterable[Var]],
+    num_scan_inputs: int,
+    scan_input_axes: Optional[Iterable[int]] = None,
+    scan_input_directions: Optional[Iterable[int]] = None,
+    scan_output_axes: Optional[Iterable[int]] = None,
+    scan_output_directions: Optional[Iterable[int]] = None,
+) -> Sequence[Var]:
+    r"""
+    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
+       }
+
+    Parameters
+    ==========
+    initial_state_and_scan_inputs
+        Type V.
+        Initial values of the loop's N state variables followed by M scan_inputs
+    body
+        Attribute.
+        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
+        Attribute.
+        An attribute specifying the number of scan_inputs M.
+    scan_input_axes
+        Attribute.
+        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
+        Attribute.
+        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
+        Attribute.
+        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
+        Attribute.
+        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.
+
+    Returns
+    =======
+    final_state_and_scan_outputs : Sequence[Var]
+        Type V.
+        Final values of the loop's N state variables followed by K scan_outputs
+
+    Notes
+    =====
+    Signature: ``ai.onnx@19::Scan``.
+
+    Type constraints:
+     - V: `tensor(bfloat16)`, `tensor(bool)`, `tensor(complex128)`, `tensor(complex64)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(float8e4m3fn)`, `tensor(float8e4m3fnuz)`, `tensor(float8e5m2)`, `tensor(float8e5m2fnuz)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)`
+    """
+    _body_subgraph: Graph = subgraph(
+        [
+            Tensor(
+                var.unwrap_tensor().dtype,
+                (lambda x: x[1:] if x is not None else None)(var.unwrap_tensor().shape),
+            )
+            for var in initial_state_and_scan_inputs[:num_scan_inputs]
+        ]
+        + [
+            Tensor(var.unwrap_tensor().dtype)
+            for var in initial_state_and_scan_inputs[num_scan_inputs:]
+        ],
+        body,
+    )
+    return _Scan(
+        _Scan.Attributes(
+            body=AttrGraph(_body_subgraph),
+            num_scan_inputs=AttrInt64(num_scan_inputs),
+            scan_input_axes=AttrInt64s.maybe(scan_input_axes),
+            scan_input_directions=AttrInt64s.maybe(scan_input_directions),
+            scan_output_axes=AttrInt64s.maybe(scan_output_axes),
+            scan_output_directions=AttrInt64s.maybe(scan_output_directions),
+        ),
+        _Scan.Inputs(
+            initial_state_and_scan_inputs=initial_state_and_scan_inputs,
+        ),
+        out_variadic=len(_body_subgraph.requested_results),
+    ).outputs.final_state_and_scan_outputs
+
+
+def shape(
+    data: Var,
+    *,
+    end: Optional[int] = None,
+    start: int = 0,
+) -> Var:
+    r"""
+    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-1], 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.
+
+    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]
+
+    Parameters
+    ==========
+    data
+        Type T.
+        An input tensor.
+    end
+        Attribute.
+        (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
+        Attribute.
+        (Optional) Starting axis for slicing the shape. Default value is
+        0.Negative value means counting dimensions from the back.
+
+    Returns
+    =======
+    shape : Var
+        Type T1.
+        Shape of the input tensor
+
+    Notes
+    =====
+    Signature: ``ai.onnx@19::Shape``.
+
+    Type constraints:
+     - T: `tensor(bfloat16)`, `tensor(bool)`, `tensor(complex128)`, `tensor(complex64)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(float8e4m3fn)`, `tensor(float8e4m3fnuz)`, `tensor(float8e5m2)`, `tensor(float8e5m2fnuz)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)`
+     - T1: `tensor(int64)`
+    """
+    return _Shape(
+        _Shape.Attributes(
+            end=AttrInt64.maybe(end),
+            start=AttrInt64(start),
+        ),
+        _Shape.Inputs(
+            data=data,
+        ),
+    ).outputs.shape
+
+
+def size(
+    data: Var,
+) -> Var:
+    r"""
+    Takes a tensor as input and outputs a int64 scalar that equals to the
+    total number of elements of the input tensor.
+
+    Parameters
+    ==========
+    data
+        Type T.
+        An input tensor.
+
+    Returns
+    =======
+    size : Var
+        Type T1.
+        Total number of elements of the input tensor
+
+    Notes
+    =====
+    Signature: ``ai.onnx@19::Size``.
+
+    Type constraints:
+     - T: `tensor(bfloat16)`, `tensor(bool)`, `tensor(complex128)`, `tensor(complex64)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(float8e4m3fn)`, `tensor(float8e4m3fnuz)`, `tensor(float8e5m2)`, `tensor(float8e5m2fnuz)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)`
+     - T1: `tensor(int64)`
+    """
+    return _Size(
+        _Size.Attributes(),
+        _Size.Inputs(
+            data=data,
+        ),
+    ).outputs.size
+
+
+def const(value: npt.ArrayLike, dtype: npt.DTypeLike = None) -> Var:
+    """
+    Convenience function for creating constants.
+
+    Shorthand for ``constant(value=np.array(value, dtype))``. The types follow numpy rules.
+    """
+
+    return constant(value=np.array(value, dtype))
+
+
+cum_sum = cumsum
+_OPERATORS = {
+    "Abs": _Abs,
+    "Acos": _Acos,
+    "Acosh": _Acosh,
+    "Add": _Add,
+    "And": _And,
+    "ArgMax": _ArgMax,
+    "ArgMin": _ArgMin,
+    "Asin": _Asin,
+    "Asinh": _Asinh,
+    "Atan": _Atan,
+    "Atanh": _Atanh,
+    "AveragePool": _AveragePool,
+    "BatchNormalization": _BatchNormalization,
+    "Bernoulli": _Bernoulli,
+    "BitShift": _BitShift,
+    "BitwiseAnd": _BitwiseAnd,
+    "BitwiseNot": _BitwiseNot,
+    "BitwiseOr": _BitwiseOr,
+    "BitwiseXor": _BitwiseXor,
+    "BlackmanWindow": _BlackmanWindow,
+    "Cast": _Cast,
+    "CastLike": _CastLike,
+    "Ceil": _Ceil,
+    "Celu": _Celu,
+    "CenterCropPad": _CenterCropPad,
+    "Clip": _Clip,
+    "Col2Im": _Col2Im,
+    "Compress": _Compress,
+    "Concat": _Concat,
+    "ConcatFromSequence": _ConcatFromSequence,
+    "Constant": _Constant,
+    "ConstantOfShape": _ConstantOfShape,
+    "Conv": _Conv,
+    "ConvInteger": _ConvInteger,
+    "ConvTranspose": _ConvTranspose,
+    "Cos": _Cos,
+    "Cosh": _Cosh,
+    "CumSum": _CumSum,
+    "DFT": _DFT,
+    "DeformConv": _DeformConv,
+    "DepthToSpace": _DepthToSpace,
+    "DequantizeLinear": _DequantizeLinear,
+    "Det": _Det,
+    "Div": _Div,
+    "Dropout": _Dropout,
+    "DynamicQuantizeLinear": _DynamicQuantizeLinear,
+    "Einsum": _Einsum,
+    "Elu": _Elu,
+    "Equal": _Equal,
+    "Erf": _Erf,
+    "Exp": _Exp,
+    "Expand": _Expand,
+    "EyeLike": _EyeLike,
+    "Flatten": _Flatten,
+    "Floor": _Floor,
+    "GRU": _GRU,
+    "Gather": _Gather,
+    "GatherElements": _GatherElements,
+    "GatherND": _GatherND,
+    "Gemm": _Gemm,
+    "GlobalAveragePool": _GlobalAveragePool,
+    "GlobalLpPool": _GlobalLpPool,
+    "GlobalMaxPool": _GlobalMaxPool,
+    "Greater": _Greater,
+    "GreaterOrEqual": _GreaterOrEqual,
+    "GridSample": _GridSample,
+    "GroupNormalization": _GroupNormalization,
+    "HammingWindow": _HammingWindow,
+    "HannWindow": _HannWindow,
+    "HardSigmoid": _HardSigmoid,
+    "HardSwish": _HardSwish,
+    "Hardmax": _Hardmax,
+    "Identity": _Identity,
+    "If": _If,
+    "InstanceNormalization": _InstanceNormalization,
+    "IsInf": _IsInf,
+    "IsNaN": _IsNaN,
+    "LRN": _LRN,
+    "LSTM": _LSTM,
+    "LayerNormalization": _LayerNormalization,
+    "LeakyRelu": _LeakyRelu,
+    "Less": _Less,
+    "LessOrEqual": _LessOrEqual,
+    "Log": _Log,
+    "LogSoftmax": _LogSoftmax,
+    "Loop": _Loop,
+    "LpNormalization": _LpNormalization,
+    "LpPool": _LpPool,
+    "MatMul": _MatMul,
+    "MatMulInteger": _MatMulInteger,
+    "Max": _Max,
+    "MaxPool": _MaxPool,
+    "MaxRoiPool": _MaxRoiPool,
+    "MaxUnpool": _MaxUnpool,
+    "Mean": _Mean,
+    "MeanVarianceNormalization": _MeanVarianceNormalization,
+    "MelWeightMatrix": _MelWeightMatrix,
+    "Min": _Min,
+    "Mish": _Mish,
+    "Mod": _Mod,
+    "Mul": _Mul,
+    "Multinomial": _Multinomial,
+    "Neg": _Neg,
+    "NegativeLogLikelihoodLoss": _NegativeLogLikelihoodLoss,
+    "NonMaxSuppression": _NonMaxSuppression,
+    "NonZero": _NonZero,
+    "Not": _Not,
+    "OneHot": _OneHot,
+    "Optional": _Optional,
+    "OptionalGetElement": _OptionalGetElement,
+    "OptionalHasElement": _OptionalHasElement,
+    "Or": _Or,
+    "PRelu": _PRelu,
+    "Pad": _Pad,
+    "Pow": _Pow,
+    "QLinearConv": _QLinearConv,
+    "QLinearMatMul": _QLinearMatMul,
+    "QuantizeLinear": _QuantizeLinear,
+    "RNN": _RNN,
+    "RandomNormal": _RandomNormal,
+    "RandomNormalLike": _RandomNormalLike,
+    "RandomUniform": _RandomUniform,
+    "RandomUniformLike": _RandomUniformLike,
+    "Range": _Range,
+    "Reciprocal": _Reciprocal,
+    "ReduceL1": _ReduceL1,
+    "ReduceL2": _ReduceL2,
+    "ReduceLogSum": _ReduceLogSum,
+    "ReduceLogSumExp": _ReduceLogSumExp,
+    "ReduceMax": _ReduceMax,
+    "ReduceMean": _ReduceMean,
+    "ReduceMin": _ReduceMin,
+    "ReduceProd": _ReduceProd,
+    "ReduceSum": _ReduceSum,
+    "ReduceSumSquare": _ReduceSumSquare,
+    "Relu": _Relu,
+    "Reshape": _Reshape,
+    "Resize": _Resize,
+    "ReverseSequence": _ReverseSequence,
+    "RoiAlign": _RoiAlign,
+    "Round": _Round,
+    "STFT": _STFT,
+    "Scan": _Scan,
+    "ScatterElements": _ScatterElements,
+    "ScatterND": _ScatterND,
+    "Selu": _Selu,
+    "SequenceAt": _SequenceAt,
+    "SequenceConstruct": _SequenceConstruct,
+    "SequenceEmpty": _SequenceEmpty,
+    "SequenceErase": _SequenceErase,
+    "SequenceInsert": _SequenceInsert,
+    "SequenceLength": _SequenceLength,
+    "SequenceMap": _SequenceMap,
+    "Shape": _Shape,
+    "Shrink": _Shrink,
+    "Sigmoid": _Sigmoid,
+    "Sign": _Sign,
+    "Sin": _Sin,
+    "Sinh": _Sinh,
+    "Size": _Size,
+    "Slice": _Slice,
+    "Softmax": _Softmax,
+    "SoftmaxCrossEntropyLoss": _SoftmaxCrossEntropyLoss,
+    "Softplus": _Softplus,
+    "Softsign": _Softsign,
+    "SpaceToDepth": _SpaceToDepth,
+    "Split": _Split,
+    "SplitToSequence": _SplitToSequence,
+    "Sqrt": _Sqrt,
+    "Squeeze": _Squeeze,
+    "StringNormalizer": _StringNormalizer,
+    "Sub": _Sub,
+    "Sum": _Sum,
+    "Tan": _Tan,
+    "Tanh": _Tanh,
+    "TfIdfVectorizer": _TfIdfVectorizer,
+    "ThresholdedRelu": _ThresholdedRelu,
+    "Tile": _Tile,
+    "TopK": _TopK,
+    "Transpose": _Transpose,
+    "Trilu": _Trilu,
+    "Unique": _Unique,
+    "Unsqueeze": _Unsqueeze,
+    "Where": _Where,
+    "Xor": _Xor,
+}
+
+_CONSTRUCTORS = {
+    "Abs": abs,
+    "Acos": acos,
+    "Acosh": acosh,
+    "Add": add,
+    "And": and_,
+    "ArgMax": arg_max,
+    "ArgMin": arg_min,
+    "Asin": asin,
+    "Asinh": asinh,
+    "Atan": atan,
+    "Atanh": atanh,
+    "AveragePool": average_pool,
+    "BatchNormalization": batch_normalization,
+    "Bernoulli": bernoulli,
+    "BitShift": bit_shift,
+    "BitwiseAnd": bitwise_and,
+    "BitwiseNot": bitwise_not,
+    "BitwiseOr": bitwise_or,
+    "BitwiseXor": bitwise_xor,
+    "BlackmanWindow": blackman_window,
+    "Cast": cast,
+    "CastLike": cast_like,
+    "Ceil": ceil,
+    "Celu": celu,
+    "CenterCropPad": center_crop_pad,
+    "Clip": clip,
+    "Col2Im": col2_im,
+    "Compress": compress,
+    "Concat": concat,
+    "ConcatFromSequence": concat_from_sequence,
+    "Constant": constant,
+    "ConstantOfShape": constant_of_shape,
+    "Conv": conv,
+    "ConvInteger": conv_integer,
+    "ConvTranspose": conv_transpose,
+    "Cos": cos,
+    "Cosh": cosh,
+    "CumSum": cumsum,
+    "DFT": dft,
+    "DeformConv": deform_conv,
+    "DepthToSpace": depth_to_space,
+    "DequantizeLinear": dequantize_linear,
+    "Det": det,
+    "Div": div,
+    "Dropout": dropout,
+    "DynamicQuantizeLinear": dynamic_quantize_linear,
+    "Einsum": einsum,
+    "Elu": elu,
+    "Equal": equal,
+    "Erf": erf,
+    "Exp": exp,
+    "Expand": expand,
+    "EyeLike": eye_like,
+    "Flatten": flatten,
+    "Floor": floor,
+    "GRU": gru,
+    "Gather": gather,
+    "GatherElements": gather_elements,
+    "GatherND": gather_nd,
+    "Gemm": gemm,
+    "GlobalAveragePool": global_average_pool,
+    "GlobalLpPool": global_lp_pool,
+    "GlobalMaxPool": global_max_pool,
+    "Greater": greater,
+    "GreaterOrEqual": greater_or_equal,
+    "GridSample": grid_sample,
+    "GroupNormalization": group_normalization,
+    "HammingWindow": hamming_window,
+    "HannWindow": hann_window,
+    "HardSigmoid": hard_sigmoid,
+    "HardSwish": hard_swish,
+    "Hardmax": hardmax,
+    "Identity": identity,
+    "If": if_,
+    "InstanceNormalization": instance_normalization,
+    "IsInf": isinf,
+    "IsNaN": isnan,
+    "LRN": lrn,
+    "LSTM": lstm,
+    "LayerNormalization": layer_normalization,
+    "LeakyRelu": leaky_relu,
+    "Less": less,
+    "LessOrEqual": less_or_equal,
+    "Log": log,
+    "LogSoftmax": log_softmax,
+    "Loop": loop,
+    "LpNormalization": lp_normalization,
+    "LpPool": lp_pool,
+    "MatMul": matmul,
+    "MatMulInteger": matmul_integer,
+    "Max": max,
+    "MaxPool": max_pool,
+    "MaxRoiPool": max_roi_pool,
+    "MaxUnpool": max_unpool,
+    "Mean": mean,
+    "MeanVarianceNormalization": mean_variance_normalization,
+    "MelWeightMatrix": mel_weight_matrix,
+    "Min": min,
+    "Mish": mish,
+    "Mod": mod,
+    "Mul": mul,
+    "Multinomial": multinomial,
+    "Neg": neg,
+    "NegativeLogLikelihoodLoss": negative_log_likelihood_loss,
+    "NonMaxSuppression": non_max_suppression,
+    "NonZero": non_zero,
+    "Not": not_,
+    "OneHot": one_hot,
+    "Optional": optional,
+    "OptionalGetElement": optional_get_element,
+    "OptionalHasElement": optional_has_element,
+    "Or": or_,
+    "PRelu": prelu,
+    "Pad": pad,
+    "Pow": pow,
+    "QLinearConv": qlinear_conv,
+    "QLinearMatMul": qlinear_matmul,
+    "QuantizeLinear": quantize_linear,
+    "RNN": rnn,
+    "RandomNormal": random_normal,
+    "RandomNormalLike": random_normal_like,
+    "RandomUniform": random_uniform,
+    "RandomUniformLike": random_uniform_like,
+    "Range": range,
+    "Reciprocal": reciprocal,
+    "ReduceL1": reduce_l1,
+    "ReduceL2": reduce_l2,
+    "ReduceLogSum": reduce_log_sum,
+    "ReduceLogSumExp": reduce_log_sum_exp,
+    "ReduceMax": reduce_max,
+    "ReduceMean": reduce_mean,
+    "ReduceMin": reduce_min,
+    "ReduceProd": reduce_prod,
+    "ReduceSum": reduce_sum,
+    "ReduceSumSquare": reduce_sum_square,
+    "Relu": relu,
+    "Reshape": reshape,
+    "Resize": resize,
+    "ReverseSequence": reverse_sequence,
+    "RoiAlign": roi_align,
+    "Round": round,
+    "STFT": stft,
+    "Scan": scan,
+    "ScatterElements": scatter_elements,
+    "ScatterND": scatter_nd,
+    "Selu": selu,
+    "SequenceAt": sequence_at,
+    "SequenceConstruct": sequence_construct,
+    "SequenceEmpty": sequence_empty,
+    "SequenceErase": sequence_erase,
+    "SequenceInsert": sequence_insert,
+    "SequenceLength": sequence_length,
+    "SequenceMap": sequence_map,
+    "Shape": shape,
+    "Shrink": shrink,
+    "Sigmoid": sigmoid,
+    "Sign": sign,
+    "Sin": sin,
+    "Sinh": sinh,
+    "Size": size,
+    "Slice": slice,
+    "Softmax": softmax,
+    "SoftmaxCrossEntropyLoss": softmax_cross_entropy_loss,
+    "Softplus": softplus,
+    "Softsign": softsign,
+    "SpaceToDepth": space_to_depth,
+    "Split": split,
+    "SplitToSequence": split_to_sequence,
+    "Sqrt": sqrt,
+    "Squeeze": squeeze,
+    "StringNormalizer": string_normalizer,
+    "Sub": sub,
+    "Sum": sum,
+    "Tan": tan,
+    "Tanh": tanh,
+    "TfIdfVectorizer": tf_idf_vectorizer,
+    "ThresholdedRelu": thresholded_relu,
+    "Tile": tile,
+    "TopK": top_k,
+    "Transpose": transpose,
+    "Trilu": trilu,
+    "Unique": unique,
+    "Unsqueeze": unsqueeze,
+    "Where": where,
+    "Xor": xor,
+}
+
+__all__ = [fun.__name__ for fun in _CONSTRUCTORS.values()]
diff --git a/tools/generate_opset.py b/tools/generate_opset.py
index a745bb6a..90049a9b 100644
--- a/tools/generate_opset.py
+++ b/tools/generate_opset.py
@@ -62,6 +62,7 @@
 LOOP16_OUT_VARIADIC_SOLUTION = "len(_body_subgraph.requested_results) - 1"
 SCAN16_OUT_VARIADIC_SOLUTION = "len(_body_subgraph.requested_results)"
 SEQUENCEMAP17_OUT_VARIADIC_SOLUTION = "len(_body_subgraph.requested_results)"
+SPLIT18_OUT_VARIADIC_SOLUTION = "num_outputs"
 
 IF16_SUBGRAPH_SOLUTION = {"else_branch": "()", "then_branch": "()"}
 LOOP16_SUBGRAPH_SOLUTION = {
@@ -86,6 +87,10 @@
     "Scan": SCAN16_OUT_VARIADIC_SOLUTION,
     "SequenceMap": SEQUENCEMAP17_OUT_VARIADIC_SOLUTION,
 }
+V18_OUT_VARIADIC_SOLUTIONS = {
+    **V16_OUT_VARIADIC_SOLUTIONS,
+    "Split": SPLIT18_OUT_VARIADIC_SOLUTION,
+}
 V16_SUBGRAPH_SOLUTIONS = {
     "If": IF16_SUBGRAPH_SOLUTION,
     "Loop": LOOP16_SUBGRAPH_SOLUTION,
@@ -97,6 +102,7 @@
     ("Cast", "to", ("npt.DTypeLike", "AttrDtype")),
     ("If", "then_branch", ("Callable[[], Iterable[Var]]", "AttrGraph")),
     ("If", "else_branch", ("Callable[[], Iterable[Var]]", "AttrGraph")),
+    ("Split", "num_outputs", ("int", "AttrInt64")),
 ]
 
 _TEMPLATE_DIR = Path(__file__).parent / "templates/"
@@ -246,15 +252,19 @@ def get_constructor_return(schema: onnx.defs.OpSchema) -> str:
 _PANDOC_GFM_TO_RST_CACHE: Dict[str, str] = {}
 
 
+def _pandoc_run(text: str):
+    return subprocess.run(
+        ["pandoc", "--from=gfm", "--to=rst"], input=text.encode(), capture_output=True
+    ).stdout.decode()
+
+
 def _pandoc_gfm_to_rst_run(*args: str) -> Tuple[str, ...]:
     if not args:
         return ()
 
-    import pandoc
-
     sep = f"\n\n{_PANDOC_SEP}{_PANDOC_SEP}\n\n"
     acc = sep.join([_PANDOC_SEP] + list(args) + [_PANDOC_SEP])
-    acc_results = pandoc.write(pandoc.read(acc, format="gfm"), format="rst")
+    acc_results = _pandoc_run(acc)
     _, *results, _ = acc_results.split(sep)
     for arg, result in zip(args, results):
         if _PANDOC_SEP in result:
@@ -659,13 +669,24 @@ def main(
         extras=["const"],
         type_inference={"Compress": "compress11"},
         value_propagation={"Constant": "constant13"},
-        out_variadic_solutions=V16_OUT_VARIADIC_SOLUTIONS,
+        out_variadic_solutions=V18_OUT_VARIADIC_SOLUTIONS,
         subgraphs_solutions=V16_SUBGRAPH_SOLUTIONS,
         attr_type_overrides=DEFAULT_ATTR_TYPE_OVERRIDES,
-        allow_extra_constructor_arguments=["Split"],
         gen_docstrings=gen_all_docstrings,
         inherited_schemas={s: ai_onnx_v17_module for s in ai_onnx_v17_schemas},
     )
+    ai_onnx_v19_schemas, ai_onnx_v19_module = main(
+        "ai.onnx",
+        19,
+        extras=["const"],
+        type_inference={"Compress": "compress11"},
+        value_propagation={"Constant": "constant13"},
+        out_variadic_solutions=V18_OUT_VARIADIC_SOLUTIONS,
+        subgraphs_solutions=V16_SUBGRAPH_SOLUTIONS,
+        attr_type_overrides=DEFAULT_ATTR_TYPE_OVERRIDES,
+        gen_docstrings=gen_all_docstrings,
+        inherited_schemas={s: ai_onnx_v18_module for s in ai_onnx_v18_schemas},
+    )
     ai_onnx_ml_v3_schemas, ai_onnx_ml_v3_module = main(
         "ai.onnx.ml",
         3,
diff --git a/tools/templates/docstring.jinja2 b/tools/templates/docstring.jinja2
index 3a2ec042..10d3ce08 100644
--- a/tools/templates/docstring.jinja2
+++ b/tools/templates/docstring.jinja2
@@ -5,12 +5,12 @@
 Parameters
 ==========
 {% for param in schema.inputs %}
-    {% if param.typeStr[0] is upper %}
-        {% do type_vars.update({param.typeStr: param.types}) %}
+    {% if param.type_str[0] is upper %}
+        {% do type_vars.update({param.type_str: param.types}) %}
     {% endif %}
 {{ param.name }}
 {% filter indent(width=4) %}
-    Type {{ param.typeStr }}.
+    Type {{ param.type_str }}.
 {{ param.description | format_github_markdown }}
 {% endfilter %}
 {% endfor %}
@@ -39,8 +39,8 @@ Parameters
 Returns
 =======
 {% for param in schema.outputs %}
-    {% if param.typeStr[0] is upper %}
-        {% do type_vars.update({param.typeStr: param.types}) %}
+    {% if param.type_str[0] is upper %}
+        {% do type_vars.update({param.type_str: param.types}) %}
     {% endif %}
 {{ param.name }} : {%
        if is_variadic(param) %}
@@ -49,7 +49,7 @@ Sequence[Var]
 Var
     {% endif %}
 {% filter indent(width=4) %}
-    Type {{ param.typeStr }}.
+    Type {{ param.type_str }}.
 {{ param.description | format_github_markdown }}
 {% endfilter %}
 {% endfor %}


Test patch:
diff --git a/tests/test_opsets.py b/tests/test_opsets.py
index a5741af2..e8dfd085 100644
--- a/tests/test_opsets.py
+++ b/tests/test_opsets.py
@@ -1,6 +1,7 @@
 import spox.opset.ai.onnx.ml.v3 as ml3
 import spox.opset.ai.onnx.v17 as op17
 import spox.opset.ai.onnx.v18 as op18
+import spox.opset.ai.onnx.v19 as op19
 
 
 def test_ai_onnx_v17():
@@ -9,6 +10,11 @@ def test_ai_onnx_v17():
 
 def test_ai_onnx_v18():
     op18.bitwise_and(op17.constant(value_int=7), op17.constant(value_int=10))
+    assert len(op18.split(op18.const([1, 2, 3]), num_outputs=3)) == 3
+
+
+def test_ai_onnx_v19():
+    op19.equal(op19.constant(value_string="abc"), op19.constant(value_string="abc"))
 
 
 def test_ai_onnx_ml_v3():