dense:
  description: |
    Applies the dense connected operation to the `input`. The dense function is defined as:

    .. math::
        output = input * weight^{T} + bias

    .. warning::
        This is an experimental API that is subject to change or deletion.

    Args:
        input (Tensor): Input Tensor of shape :math:`(*, in\_channels)`,
            where :math:`*` means any number of additional dimensions.
        weight (Tensor): The weight applied to the input.
            The shape is :math:`(out\_channels, in\_channels)` or :math:`(in\_channels)`.
        bias (Tensor, optional): Additive biases to the output.
            The shape is :math:`(out\_channels)` or :math:`()`. Defaults: ``None``, the `bias` is 0.

    Returns:
        Output whose shape is determined by the shape of the input and the weight.

    Raises:
        TypeError: If `input` is not Tensor.
        TypeError: If `weight` is not Tensor.
        TypeError: If `bias` is not Tensor.

    Supported Platforms:
        ``Ascend`` ``GPU``  ``CPU``

    Examples:
        >>> import numpy as np
        >>> import mindspore
        >>> from mindspore import Tensor, ops
        >>> input = Tensor([[-1., 1., 2.], [-3., -3., 1.]], mindspore.float32)
        >>> weight = Tensor([[-2., -2., -2.], [0., -1., 0.]], mindspore.float32)
        >>> bias = Tensor([0., 1.], mindspore.float32)
        >>> output = ops.dense(input, weight, bias)
        >>> print(output)
        [[-4.  0.]
         [10.  4.]]