(l-onnx-doc-RandomNormal)= # RandomNormal (l-onnx-op-randomnormal-22)= ## RandomNormal - 22 ### Version - **name**: [RandomNormal (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#RandomNormal) - **domain**: `main` - **since_version**: `22` - **function**: `False` - **support_level**: `SupportType.COMMON` - **shape inference**: `True` This version of the operator has been available **since version 22**. ### Summary Generate a tensor with random values drawn from a normal distribution. The shape of the tensor is specified by the `shape` argument and the parameter of the normal distribution specified by `mean` and `scale`. The data type is specified by the 'dtype' argument. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. ### Attributes * **dtype - INT** (default is `'1'`): The data type for the elements of the output tensor. Default is TensorProto::FLOAT. * **mean - FLOAT** (default is `'0.0'`): The mean of the normal distribution. * **scale - FLOAT** (default is `'1.0'`): The standard deviation of the normal distribution. * **seed - FLOAT** : (Optional) Seed to the random generator, if not specified we will auto generate one. * **shape - INTS** (required) : The shape of the output tensor. ### Outputs - **output** (heterogeneous) - **T**: Output tensor of random values drawn from normal distribution ### Type Constraints * **T** in ( `tensor(bfloat16)`, `tensor(double)`, `tensor(float)`, `tensor(float16)` ): Constrain output types to float tensors. ```{toctree} text_diff_RandomNormal_1_22 ``` (l-onnx-op-randomnormal-1)= ## RandomNormal - 1 ### Version - **name**: [RandomNormal (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#RandomNormal) - **domain**: `main` - **since_version**: `1` - **function**: `False` - **support_level**: `SupportType.COMMON` - **shape inference**: `True` This version of the operator has been available **since version 1**. ### Summary Generate a tensor with random values drawn from a normal distribution. The shape of the tensor is specified by the `shape` argument and the parameter of the normal distribution specified by `mean` and `scale`. The data type is specified by the 'dtype' argument. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. ### Attributes * **dtype - INT** (default is `'1'`): The data type for the elements of the output tensor. Default is TensorProto::FLOAT. * **mean - FLOAT** (default is `'0.0'`): The mean of the normal distribution. * **scale - FLOAT** (default is `'1.0'`): The standard deviation of the normal distribution. * **seed - FLOAT** : (Optional) Seed to the random generator, if not specified we will auto generate one. * **shape - INTS** (required) : The shape of the output tensor. ### Outputs - **output** (heterogeneous) - **T**: Output tensor of random values drawn from normal distribution ### Type Constraints * **T** in ( `tensor(double)`, `tensor(float)`, `tensor(float16)` ): Constrain output types to float tensors.