# nn.Functional

* `cofhe.nn.functional.relu(input)`\
  Applies the ReLU activation function to the input tensor, replacing negative values with zero.
* `cofhe.nn.functional.softmax(input)`\
  Applies the softmax activation function across the input tensor, typically used for classification tasks.
* `cofhe.nn.functional.log_softmax(input)`\
  Applies the log softmax activation function to the input tensor. Useful when working with log probabilities.
* `cofhe.nn.functional.leaky_relu(input, negative_slope)`\
  Applies the Leaky ReLU activation function, where negative values are scaled by a `negative_slope` factor.
* `cofhe.nn.functional.tanh(input)`\
  Applies the hyperbolic tangent (Tanh) activation function to the input tensor.
* `cofhe.nn.functional.sigmoid(input)`\
  Applies the sigmoid activation function to the input tensor, mapping the values to the range \[0, 1].
* `cofhe.nn.functional.gelu(input)`\
  Applies the Gaussian Error Linear Unit (GELU) activation function, a smooth version of ReLU, widely used in transformer models.
* `cofhe.nn.functional.mse_loss(input, target)`\
  Computes the Mean Squared Error (MSE) loss between the input tensor and the target tensor.
* `cofhe.nn.functional.cross_entropy(input, target)`\
  Computes the Cross-Entropy loss between the input tensor (logits) and the target tensor (class labels).


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