RuntimeError: Given groups=1, weight of size [32, 3, 3], expected input[2, 300, 3] to have 3 channels, but got 300 channels instead

Question

#%%
import torch
from torch import nn
from torchvision.transforms import ToTensor
import torch.nn.functional as F
import math
import torchsummary
#%%

def conv1d():
   model = nn.Sequential(
        nn.Conv1d(3, 32, kernel_size=3, stride=1, padding='same'),
        nn.ReLU(),
        nn.MaxPool1d(kernel_size=2, stride=2, padding=1),
    
        nn.Conv1d(32, 64, kernel_size=3, stride=1, padding='same'),
        nn.ReLU(),
        nn.MaxPool1d(kernel_size=2, stride=2, padding=1),

        nn.Conv1d(64, 128, kernel_size=3, stride=1, padding='same'),
        nn.ReLU(),
        nn.MaxPool1d(kernel_size=2, stride=2, padding=1)
   )
   return model

class MultiCNN(nn.Module):
  def __init__(self):
    super(MultiCNN, self).__init__()
    self.conv_gra = conv1d()
    self.conv_la = conv1d()
    self.conv_gyro = conv1d()
    self.conv_mag = conv1d()

    self.final_linear = nn.Sequential(
        nn.Linear(128*4, 128),
        nn.ReLU(),
        nn.Dropout(0.2),
        nn.Linear(128, 7),
    )

  def forward(self, x):
    out1 = self.conv_gra(x[:,:,:3])
    out2 = self.conv_la(x[:,:,3:6])
    out3 = self.conv_gyro(x[:,:,6:9])
    out4 = self.conv_mag(x[:,:,9:12])

    out1 = out1.view(128, -1)
    out2 = out2.view(128, -1)
    out3 = out3.view(128, -1)
    out4 = out4.view(128, -1)
    
    

    total_out = torch.cat((out1,out2,out3,out4), dim=1)
    total_out = self.final_linear(total_out)
        
    return total_out
# %%

Error message Below RuntimeError: Given groups=1, weight of size [32, 3, 3], expected input[2, 300, 3] to have 3 channels, but got 300 channels instead Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...

I designed Multi CNN for Sequence data (for 4 sensor data)

Input size is (128, 300, 3) = (batch_size, time_sequence, input_features) but I don't know how can I solve this problem..

Plus, Why weight of size [32,3,3]?! Is this 1D Convolution??

Please any comments to me..!