Project: s41467-024-46946-w | Function: complex_relu (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Implements complex-valued ReLU activation function for processing optical field data with both magnitude and phase information in complex neural networks for speckle-based encryption

**KEY TERMS:** {
  "input_r": "real part of complex optical field",
  "input_i": "imaginary part of complex optical field",
  "relu": "rectified linear unit activation",
  "complex_field": "optical field with amplitude and phase components",
  "activation": "nonlinear transformation for neural processing"
}

**CONTEXT CODE:** #!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""
@author: spopoff
"""

from torch.nn.functional import relu, max_pool2d, dropout, dropout2d

def complex_relu(input_r,input_i):
    # MASKED: Implements complex-valued ReLU activation function for processing optical field data with both magnitude and phase information in complex neural networks for speckle-based encryption
    raise NotImplementedError("This function is masked for testing purposes.")

def complex_max_pool2d(input_r,input_i,kernel_size, stride=None, padding=0,
                                dilation=1, ceil_mode=False, return_indices=False):

    return max_pool2d(input_r, kernel_size, stride, padding, dilation,
                      ceil_mode, return_indices), \
           max_pool2d(input_i, kernel_size, stride, padding, dilation,
                      ceil_mode, return_indices)

def complex_dropout(input_r,input_i, p=0.5, training=True, inplace=False):
    return dropout(input_r, p, training, inplace), \
           dropout(input_i, p, training, inplace)


def complex_dropout2d(input_r,input_i, p=0.5, training=True, inplace=False):
    return dropout2d(input_r, p, training, inplace), \
           dropout2d(input_i, p, training, inplace)


Complete the function implementation replacing "raise NotImplementedError".