import torch
import torch.nn as nn


class AttentionLayer(nn.Module):

    def __init__(self, embed_dim, num_heads, dropout_rate=0.1,
        feedforward_size=256):
        """The core module with both spatial attention module and 
           temporal attention model embedded within it.
        """
        super(AttentionLayer, self).__init__()
        self.spatial_attention = nn.MultiheadAttention(embed_dim, num_heads)
        self.temporal_attention = nn.MultiheadAttention(embed_dim, num_heads)
        self.linear1 = nn.Linear(embed_dim, feedforward_size)
        self.linear2 = nn.Linear(feedforward_size, embed_dim)
        self.layer_norm = nn.LayerNorm(embed_dim)
        self.dropout = nn.Dropout(dropout_rate)

    def forward(self, inputs):
        """
        :param inputs: shape (T, B, H)
        :returns out: shape (T, B, H)
        """
        spatial_out, _spatial_attention_matrix = self.spatial_attention(inputs,
            inputs, inputs)
        spatial_out = self.dropout(spatial_out)
        spatial_out += inputs
        spatial_out = self.layer_norm(spatial_out)
        temporal_out, _temporal_attention_matrix = self.temporal_attention(
            inputs, inputs, inputs)
        temporal_out = self.dropout(temporal_out)
        temporal_out += inputs
        temporal_out = self.layer_norm(temporal_out)
        attention_out = spatial_out + temporal_out
        out = self.linear1(attention_out)
        out = self.linear2(out)
        out = self.dropout(out)
        out += attention_out
        out = self.layer_norm(out)
        return out


def get_inputs():
    return [torch.rand([4, 4])]


def get_init_inputs():
    return [[], {'embed_dim': 4, 'num_heads': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_mul_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask)
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_4(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp29 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp34 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp37 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = 4.0
    tmp16 = tmp14 / tmp15
    tmp17 = tmp2 - tmp16
    tmp18 = tmp17 * tmp17
    tmp19 = tmp5 - tmp16
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp9 - tmp16
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp16
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = tmp27 / tmp15
    tmp30 = tmp29 + tmp1
    tmp32 = tmp31 + tmp4
    tmp33 = tmp30 + tmp32
    tmp35 = tmp34 + tmp8
    tmp36 = tmp33 + tmp35
    tmp38 = tmp37 + tmp12
    tmp39 = tmp36 + tmp38
    tmp40 = tmp39 / tmp15
    tmp41 = tmp30 - tmp40
    tmp42 = tmp41 * tmp41
    tmp43 = tmp32 - tmp40
    tmp44 = tmp43 * tmp43
    tmp45 = tmp42 + tmp44
    tmp46 = tmp35 - tmp40
    tmp47 = tmp46 * tmp46
    tmp48 = tmp45 + tmp47
    tmp49 = tmp38 - tmp40
    tmp50 = tmp49 * tmp49
    tmp51 = tmp48 + tmp50
    tmp52 = tmp51 / tmp15
    tl.store(out_ptr0 + x0, tmp16, xmask)
    tl.store(out_ptr1 + x0, tmp28, xmask)
    tl.store(out_ptr2 + x0, tmp40, xmask)
    tl.store(out_ptr3 + x0, tmp52, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_5(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr6 + x2, xmask)
    tmp16 = tl.load(in_ptr7 + x1, xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr8 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp4 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp15 = tmp14 + tmp1
    tmp17 = tmp15 - tmp16
    tmp19 = tmp18 + tmp6
    tmp20 = libdevice.rsqrt(tmp19)
    tmp21 = tmp17 * tmp20
    tmp22 = tmp21 * tmp10
    tmp23 = tmp22 + tmp12
    tmp24 = tmp13 + tmp23
    tl.store(out_ptr0 + x2, tmp24, xmask)


@triton.jit
def triton_poi_fused_add_6(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_7(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_8(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (12, 4), (4, 1))
    assert_size_stride(primals_3, (12,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (12, 4), (4, 1))
    assert_size_stride(primals_9, (12,), (1,))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (4,), (1,))
    assert_size_stride(primals_12, (256, 4), (4, 1))
    assert_size_stride(primals_13, (256,), (1,))
    assert_size_stride(primals_14, (4, 256), (256, 1))
    assert_size_stride(primals_15, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4),
            (1, 4), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(reinterpret_tensor(primals_3, (4,), (1,), 4),
            primals_1, reinterpret_tensor(primals_2, (4, 4), (1, 4), 16),
            alpha=1, beta=1, out=buf1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(reinterpret_tensor(primals_3, (4,), (1,), 8),
            primals_1, reinterpret_tensor(primals_2, (4, 4), (1, 4), 32),
            alpha=1, beta=1, out=buf2)
        del primals_2
        buf3 = reinterpret_tensor(buf0, (4, 4, 1), (1, 4, 16), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(16)](buf3, primals_3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_3
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf3, reinterpret_tensor(buf1, (4, 1, 4), (1, 1,
            4), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(64)](buf4, buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf6 = buf4
        del buf4
        triton_poi_fused__softmax_2[grid(64)](buf5, buf6, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf6, reinterpret_tensor(buf2, (4, 4, 1), (1, 4,
            1), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        triton_poi_fused_clone_3[grid(4, 4)](buf7, buf8, 4, 4, XBLOCK=4,
            YBLOCK=4, num_warps=1, num_stages=1)
        buf9 = reinterpret_tensor(buf7, (4, 4), (4, 1), 0)
        del buf7
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf8, (4, 4), (4,
            1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha
            =1, beta=1, out=buf9)
        del primals_5
        buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(reinterpret_tensor(primals_9, (4,), (1,), 8),
            primals_1, reinterpret_tensor(primals_8, (4, 4), (1, 4), 32),
            alpha=1, beta=1, out=buf14)
        buf13 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(reinterpret_tensor(primals_9, (4,), (1,), 4),
            primals_1, reinterpret_tensor(primals_8, (4, 4), (1, 4), 16),
            alpha=1, beta=1, out=buf13)
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_8, (4, 4),
            (1, 4), 0), out=buf12)
        del primals_8
        buf15 = reinterpret_tensor(buf12, (4, 4, 1), (1, 4, 16), 0)
        del buf12
        triton_poi_fused_mul_0[grid(16)](buf15, primals_9, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_9
        buf16 = buf5
        del buf5
        extern_kernels.bmm(buf15, reinterpret_tensor(buf13, (4, 1, 4), (1, 
            1, 4), 0), out=buf16)
        buf17 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(64)](buf16, buf17, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf18 = buf16
        del buf16
        triton_poi_fused__softmax_2[grid(64)](buf17, buf18, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf17
        buf19 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf18, reinterpret_tensor(buf14, (4, 4, 1), (1, 
            4, 1), 0), out=buf19)
        buf20 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        triton_poi_fused_clone_3[grid(4, 4)](buf19, buf20, 4, 4, XBLOCK=4,
            YBLOCK=4, num_warps=1, num_stages=1)
        buf21 = reinterpret_tensor(buf19, (4, 4), (4, 1), 0)
        del buf19
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf20, (4, 4),
            (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf21)
        del primals_11
        buf10 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf11 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf22 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf23 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        triton_poi_fused_add_native_layer_norm_4[grid(4)](buf9, primals_1,
            buf21, buf10, buf11, buf22, buf23, 4, XBLOCK=4, num_warps=1,
            num_stages=1)
        buf24 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_5[grid(16)](buf9, primals_1,
            buf10, buf11, primals_6, primals_7, buf21, buf22, buf23, buf24,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf10
        del buf11
        buf25 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        extern_kernels.addmm(primals_13, buf24, reinterpret_tensor(
            primals_12, (4, 256), (1, 4), 0), alpha=1, beta=1, out=buf25)
        del primals_13
        buf26 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf25, reinterpret_tensor(primals_14, (256, 4), (
            1, 256), 0), out=buf26)
        buf27 = buf26
        del buf26
        triton_poi_fused_add_6[grid(16)](buf27, primals_15, buf24, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_15
        buf28 = buf23
        del buf23
        buf29 = buf22
        del buf22
        triton_poi_fused_native_layer_norm_7[grid(4)](buf27, buf28, buf29, 
            4, XBLOCK=4, num_warps=1, num_stages=1)
        buf30 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_8[grid(16)](buf27, buf28, buf29,
            primals_6, primals_7, buf30, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del buf28
        del buf29
        del primals_7
    return (buf30, primals_1, primals_6, buf6, reinterpret_tensor(buf8, (4,
        4), (4, 1), 0), buf9, buf18, reinterpret_tensor(buf20, (4, 4), (4, 
        1), 0), buf21, buf24, buf25, buf27, primals_14, primals_12,
        primals_10, reinterpret_tensor(buf14, (4, 1, 4), (1, 1, 4), 0),
        reinterpret_tensor(buf15, (4, 1, 4), (1, 1, 4), 0),
        reinterpret_tensor(buf13, (4, 4, 1), (1, 4, 1), 0), primals_4,
        reinterpret_tensor(buf2, (4, 1, 4), (1, 1, 4), 0),
        reinterpret_tensor(buf3, (4, 1, 4), (1, 1, 4), 0),
        reinterpret_tensor(buf1, (4, 4, 1), (1, 4, 1), 0))


class AttentionLayerNew(nn.Module):

    def __init__(self, embed_dim, num_heads, dropout_rate=0.1,
        feedforward_size=256):
        """The core module with both spatial attention module and 
           temporal attention model embedded within it.
        """
        super(AttentionLayerNew, self).__init__()
        self.spatial_attention = nn.MultiheadAttention(embed_dim, num_heads)
        self.temporal_attention = nn.MultiheadAttention(embed_dim, num_heads)
        self.linear1 = nn.Linear(embed_dim, feedforward_size)
        self.linear2 = nn.Linear(feedforward_size, embed_dim)
        self.layer_norm = nn.LayerNorm(embed_dim)
        self.dropout = nn.Dropout(dropout_rate)

    def forward(self, input_0):
        primals_2 = self.spatial_attention.in_proj_weight
        primals_3 = self.spatial_attention.in_proj_bias
        primals_1 = self.spatial_attention.out_proj.weight
        primals_5 = self.spatial_attention.out_proj.bias
        primals_8 = self.temporal_attention.in_proj_weight
        primals_9 = self.temporal_attention.in_proj_bias
        primals_4 = self.temporal_attention.out_proj.weight
        primals_6 = self.temporal_attention.out_proj.bias
        primals_12 = self.linear1.weight
        primals_13 = self.linear1.bias
        primals_14 = self.linear2.weight
        primals_7 = self.linear2.bias
        primals_11 = self.layer_norm.weight
        primals_15 = self.layer_norm.bias
        primals_10 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15])
        return output[0]
