Can you format correctly ? Here's an example of how to create a convolutional layer for a CNN in code: ```python import torch import torch.nn as nn class ConvolutionalLayer(nn.Module): def __init__(self, input_channels, output_channels, kernel_size, stride, padding, dilation, groups, bias): super(ConvolutionalLayer, self).__init__() self.conv = nn.Conv2d(input_channels, output_channels, kernel_size, stride, padding, dilation, groups, bias) def forward(self, x): x = self.conv(x) return x # Example usage input_channels = 1 output_channels = 1 kernel_size = (3, 3) stride = (1, 1) padding = (1, 1) dilation = (1, 1) groups = 1 bias = True convolutional_layer = ConvolutionalLayer(input_channels, output_channels, kernel_size, stride, padding, dilation, groups, bias) x = torch.randn(1, input_channels, 64, 64) output = convolutional_layer(x) print(output.shape) ``` This code creates a convolutional layer using the `nn.Conv2d` class from the `torch.nn` module in PyTorch. The layer has one input channel, one output channel, a (3, 3) kernel size, a (1, 1) stride, a (1, 1) padding, and a (1, 1) dilation. The `groups` parameter is set to 1 and the `bias` parameter is set to True. The `ConvolutionalLayer` class inherits from the `nn.Module` class and overrides the `__init__` method to define the convolutional layer. The `forward` method is also defined to define the forward pass of the layer. In the example usage, the `input_channels` and `output_channels` are set to 1, the `kernel_size` is set to (3, 3), the `stride` is set to (1, 1), the `padding` is set to (1, 1), the `dilation` is set to (1, 1), the `groups` is set to 1, and the `bias` is set to True. The `torch.randn` function generates a random tensor of the specified shape. The `convolutional_layer` is defined by passing the `input_channels`, `output_channels`, `kernel_size`, `stride`, `padding`, `dilation`, `groups`, and `bias` parameters to the `nn.Conv2d` constructor. The `convolutional_layer` is then used to convolve the input tensor `x`. The output of the convolutional layer is printed to the console. ``` There should be exactly 6 paragraphs separated by the markdown divider: ***.
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