In tensorflow, API is desribed asL
TensorFlow

tf.keras.layers.Conv2D | TensorFlow Core v2.6.0

2D convolution layer (e.g. spatial convolution over images).
tf.keras.layers.Conv2D(    filters, kernel_size, strides=(1, 1), padding='valid',    data_format=None, dilation_rate=(1, 1), groups=1, activation=None,    use_bias=True, kernel_initializer='glorot_uniform',    bias_initializer='zeros', kernel_regularizer=None,    bias_regularizer=None, activity_regularizer=None, kernel_constraint=None,    bias_constraint=None, **kwargs)

followed by some example:
# The inputs are 28x28 RGB images with `channels_last` and the batch
# size is 4.
input_shape = (4, 28, 28, 3)
x = tf.random.normal(input_shape)
y = tf.keras.layers.Conv2D(
2, 3, activation='relu', input_shape=input_shape[1:])(x)
print(y.shape)


Secondly, I am also "porting" doing pytorch equivalent but pytorch's conv2d API has no mentions of filters, only significant params are: channels in / out and kernel size:

https://pytorch.org/docs/stable/generated/torch.nn.Dropout.html#torch.nn.Dropout

So how one specifies the filter in pytorch?