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## Unity ##

*.cs diff=csharp text
*.cginc text
*.shader text

*.mat merge=unityyamlmerge eol=lf
*.anim merge=unityyamlmerge eol=lf
*.unity merge=unityyamlmerge eol=lf
*.prefab merge=unityyamlmerge eol=lf

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function trigger(el, eventType) {
  if (typeof eventType === 'string' && typeof el[eventType] === 'function') {
    el[eventType]();
  } else {
    const event =
      eventType === 'string'
        ? new Event(eventType, {bubbles: true})
        : eventType;
    el.dispatchEvent(event);
  }

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alizarin
amaranth
amber
amethyst
apricot
aqua
aquamarine
asparagus
auburn
azure

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alizarin
amaranth
amber
amethyst
apricot
aqua
aquamarine
asparagus
auburn
azure

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<useragentswitcher>
	<folder description="Browsers - Windows">
		<folder description="Legacy Browsers">
			<useragent description="Arora 0.6.0 - (Vista)" useragent="Mozilla/5.0 (Windows; U; Windows NT 6.0; en-US) AppleWebKit/527  (KHTML, like Gecko, Safari/419.3) Arora/0.6 (Change: )" appcodename="" appname="" appversion="" platform="" vendor="" vendorsub=""/>
			<useragent description="Avant Browser 1.2" useragent="Avant Browser/1.2.789rel1 (http://www.avantbrowser.com)" appcodename="" appname="" appversion="" platform="" vendor="" vendorsub=""/>
			<useragent description="Chrome 4.0 (Win 7)" useragent="Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US) AppleWebKit/532.5 (KHTML, like Gecko) Chrome/4.0.249.0 Safari/532.5" appcodename="" appname="" appversion="" platform="" vendor="" vendorsub=""/>
			<useragent description="Chrome 5.0 (Server 2003)" useragent="Mozilla/5.0 (Windows; U; Windows NT 5.2; en-US) AppleWebKit/532.9 (KHTML, like Gecko) Chrome/5.0.310.0 Safari/532.9" appcodename="" appname="" appversion=

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# Show Tensor Images utility function

from torchvision.utils import make_grid
import matplotlib.pyplot as plt

def show_tensor_images(image_tensor, num_images=25, size=(1, 28, 28)):
    '''
    Function for visualizing images: Given a tensor of images, number of images, and
    size per image, plots and prints the images in a uniform grid.
    '''

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def conv_backward(dH, cache):
    '''
    The backward computation for a convolution function
    
    Arguments:
    dH -- gradient of the cost with respect to output of the conv layer (H), numpy array of shape (n_H, n_W) assuming channels = 1
    cache -- cache of values needed for the conv_backward(), output of conv_forward()
    
    Returns:
    dX -- gradient of the cost with respect to input of the conv layer (X), numpy array of shape (n_H_prev, n_W_prev) assuming channels = 1

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def conv_forward(X, W):
    '''
    The forward computation for a convolution function
    
    Arguments:
    X -- output activations of the previous layer, numpy array of shape (n_H_prev, n_W_prev) assuming input channels = 1
    W -- Weights, numpy array of size (f, f) assuming number of filters = 1
    
    Returns:
    H -- conv output, numpy array of size (n_H, n_W)

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def conv_forward(X, W):
    '''
    The forward computation for a convolution function
    
    Arguments:
    X -- output activations of the previous layer, numpy array of shape (n_H_prev, n_W_prev) assuming input channels = 1
    W -- Weights, numpy array of size (f, f) assuming number of filters = 1
    
    Returns:
    H -- conv output, numpy array of size (n_H, n_W)

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