tf.concat (and tf.transpose) inside a for loop with tf.range in the context of a GradientTape while using XLA dosn't work
@MoritzMSP Could you consider to calculate shapes and perform tf.concat and tf.transpose outside the loop. Pre-compute Shapes and Operations could help in this case. Thank you! Are you satisfied with the resolution of your issue?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62861">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62861">No</a>
 Are you satisfied with the resolution of your issue?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62861">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62861">No</a>
 @sushreebarsa This could indeed work in the given simplified example, however in other models I'm developing this is not possible since the values which are concatenated are dependent on the value of yi which is in turn dependent on the prior iteration of the loop.

e.g. Part of the compute function from another model:
```
@tf.function(input_signature=[tf.TensorSpec(shape=(None, 12, 1), dtype=tf.float32)],
                    jit_compile=False)
def compute(self, yi):
    eta_LR = yi[:, 0:3, :]
    rp0_R = yi[:, 3:6, :]
    drp0_R = yi[:, 6:9, :]    
    omegaLR_L = yi[:, 9:12, :]

    A_LR = Modeling.calcA_LR(eta_LR)
    A_RL = tf.transpose(A_LR, perm=[0, 2, 1])

    PhiUpper = tf.concat([tf.eye(3,batch_shape=[self.batchsize]), -A_RL@Modeling.skewSym(self.rgp_L)], axis=2)
    PhiLower = tf.concat([tf.zeros([self.batchsize, 2, 3]), Modeling.skewSym(A_LR[:, :, 2:3])[:, 0:2, :]], axis=2)
    Phi = tf.concat([PhiUpper, PhiLower], axis=1)

    #Code continues here 
            # --> LHS and RHS will be constructed (Phi is a part of LHS)
            # d_eta will be calculated

    sol = tf.linalg.solve(LHS, RHS)
    dd_rp0_R = sol[:, 0:3, :]
    d_omegaLR_L = sol[:, 3:6, :]

    return tf.concat([d_eta, drp0_R, dd_rp0_R, d_omegaLR_L], axis=1)
```
(The Modeling.skewSym(X) function just rearranges the elements in X with the shape [None, 3, 1] into a Matrix with a shape of [None, 3, 3])

--> In the tf,concat / tf.transpose Function A_LR and A_RL are used, which are depended on eta_LR and eta_LR is part of yi. Thats the reason why it can't be Pre-computed outside of the loop.
 @MoritzMSP Thank you for the update!
@sachinprasadhs Please find the replicated [gist](https://colab.research.google.com/gist/sushreebarsa/094dc8a7cf598f338c1ca7693e2a706d/62861.ipynb) of the error reported. Thank you! I am not familiar with tf.function, assigning to @cheshire who is familiar with this and might know who can help. I'm not sure this is expected behavior or not. I was able to simplify the program a bit:

```
class Model():
    def __init__(self, batchsize):
        self.batchsize = batchsize
        self.g = tf.Variable(initial_value=tf.ones([self.batchsize,1,1]), trainable=True, name="g")
        self.y0 = tf.ones([self.batchsize, 2, 1])


    @tf.function(input_signature=[tf.TensorSpec(shape=(None, 2, 1), dtype=tf.float32)],
                 jit_compile=False)
    def compute(self, yi):
        B = tf.concat([tf.zeros_like(self.g), self.g], 1)
        return B

class Estimator():
    def __init__(self, model):
        self.model = model
        self.dt = tf.constant(0.001)

    @tf.function(jit_compile=True, reduce_retracing=False)
    def estimate(self):
        with tf.GradientTape() as tape:
            yi = self.model.y0
            for i in tf.range(10):
                yi = self.model.compute(yi)
        grads = tape.gradient(yi, [self.model.g])
        return grads

device = "CPU:0"

with tf.device(device):
    batchsize = 5
    model = Model(batchsize)
    estimator = Estimator(model)
    grads = estimator.estimate()
```

Fails due to `ConcatOffset` not having constant time inputs. The error message is a weird red herring since the shapes are known but the actual arguments to `ConcatOffset` need to be constant. Also the Model.compute(..) has jit_compile=False but since it's called by Estimator.estimate(...) all the callees also have to be compiled. 

> If you remove either
> the GradientTape context
> or the for loop with tf.range
> or the XLA compilation
> the code will work as expected.

Removing these makes this totally skip compilation and so that's why the error doesn't show up. I'm not sure this is expected behavior. Looping in swachhandl@ who might know more.


================ EXTRA DEBUG DUMP===============
Failed MLIR dump from simple version:

```
module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 1739 : i32}} {
  func.func @main(%arg0: tensor<5x2x1xf32>, %arg1: tensor<5x1x1xf32> {tf._user_specified_name = "input_5"}) -> tensor<5x1x1xf32> attributes {allow_soft_placement = false, tf.entry_function = {control_outputs = "while,gradient_tape/while/VariableShape", inputs = "unknown,while_input_5", outputs = "identity_RetVal"}} {
    %cst = "tf.Const"() <{value = dense<> : tensor<0xi32>}> : () -> tensor<0xi32>
    %cst_0 = "tf.Const"() <{value = dense<3> : tensor<i32>}> : () -> tensor<i32>
    %cst_1 = "tf.Const"() <{value = dense<[5, 1, 1]> : tensor<3xi32>}> : () -> tensor<3xi32>
    %cst_2 = "tf.Const"() <{value = dense<0.000000e+00> : tensor<5x2x1xf32>}> : () -> tensor<5x2x1xf32>
    %cst_3 = "tf.Const"() <{value = dense<1> : tensor<i32>}> : () -> tensor<i32>
    %cst_4 = "tf.Const"() <{value = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>
    %cst_5 = "tf.Const"() <{value = dense<10> : tensor<i32>}> : () -> tensor<i32>
    %cst_6 = "tf.Const"() <{value = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
    %cst_7 = "tf.Const"() <{value = dense<0.000000e+00> : tensor<5x1x1xf32>}> : () -> tensor<5x1x1xf32>
    %cst_8 = "tf.Const"() <{value = dense<1.000000e+00> : tensor<5x2x1xf32>}> : () -> tensor<5x2x1xf32>
    %cst_9 = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>
    %cst_10 = "tf.Const"() <{value = dense<0> : tensor<10xi32>}> : () -> tensor<10xi32>
    %0:4 = "tf.WhileRegion"(%cst_9, %cst_9, %cst_10, %cst_6) <{is_stateless = false, parallel_iterations = 10 : i64}> ({
    ^bb0(%arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<10xi32>, %arg5: tensor<1xi32>):
      %2 = "tf.Less"(%arg3, %cst_5) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i1>
      %3 = "tf.Less"(%arg2, %cst_5) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i1>
      %4 = "tf.LogicalAnd"(%3, %2) {device = ""} : (tensor<i1>, tensor<i1>) -> tensor<i1>
      "tf.Yield"(%4) : (tensor<i1>) -> ()
    }, {
    ^bb0(%arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<10xi32>, %arg5: tensor<1xi32>):
      %2 = "tf.AddV2"(%arg3, %cst_3) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i32>
      %3 = "tf.XlaDynamicUpdateSlice"(%arg4, %cst_4, %arg5) : (tensor<10xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<10xi32>
      %4 = "tf.AddV2"(%arg5, %cst_4) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
      %5 = "tf.AddV2"(%arg2, %cst_3) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i32>
      "tf.Yield"(%5, %2, %3, %4) : (tensor<i32>, tensor<i32>, tensor<10xi32>, tensor<1xi32>) -> ()
    }) {_num_original_outputs = 8 : i64, _read_only_resource_inputs = [5], _xla_propagate_compile_time_consts = true, device = ""} : (tensor<i32>, tensor<i32>, tensor<10xi32>, tensor<1xi32>) -> (tensor<i32>, tensor<i32>, tensor<10xi32>, tensor<1xi32>)
    %1:4 = "tf.WhileRegion"(%cst_9, %cst_8, %cst_7, %0#3) <{is_stateless = true, parallel_iterations = 10 : i64}> ({
    ^bb0(%arg2: tensor<i32>, %arg3: tensor<5x2x1xf32>, %arg4: tensor<5x1x1xf32>, %arg5: tensor<1xi32>):
      %2 = "tf.Less"(%arg2, %0#0) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i1>
      "tf.Yield"(%2) : (tensor<i1>) -> ()
    }, {
    ^bb0(%arg2: tensor<i32>, %arg3: tensor<5x2x1xf32>, %arg4: tensor<5x1x1xf32>, %arg5: tensor<1xi32>):
      %2 = "tf.Sub"(%arg5, %cst_4) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
      %3 = "tf.Slice"(%0#2, %2, %cst_4) : (tensor<10xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
      %4 = "tf.Reshape"(%3, %cst) : (tensor<1xi32>, tensor<0xi32>) -> tensor<i32>
      %5 = "tf.AddV2"(%arg2, %cst_3) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i32>
      %6 = "tf.FloorMod"(%4, %cst_0) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i32>
      %7:2 = "tf.ConcatOffset"(%6, %cst_1, %cst_1) {device = ""} : (tensor<i32>, tensor<3xi32>, tensor<3xi32>) -> (tensor<3xi32>, tensor<3xi32>)
      %8 = "tf.Slice"(%arg3, %7#1, %cst_1) {device = ""} : (tensor<5x2x1xf32>, tensor<3xi32>, tensor<3xi32>) -> tensor<5x1x1xf32>
      %9 = "tf.AddN"(%arg4, %8) {_class = ["loc:@gradient_tape/while/gradients/grad_ys_1"], device = ""} : (tensor<5x1x1xf32>, tensor<5x1x1xf32>) -> tensor<5x1x1xf32>
      "tf.Yield"(%5, %cst_2, %9, %2) : (tensor<i32>, tensor<5x2x1xf32>, tensor<5x1x1xf32>, tensor<1xi32>) -> ()
    }) {_num_original_outputs = 6 : i64, _read_only_resource_inputs = [], _xla_propagate_compile_time_consts = true, device = ""} : (tensor<i32>, tensor<5x2x1xf32>, tensor<5x1x1xf32>, tensor<1xi32>) -> (tensor<i32>, tensor<5x2x1xf32>, tensor<5x1x1xf32>, tensor<1xi32>)
    return %1#2 : tensor<5x1x1xf32>
  }
}
```

I'm not sure why `tf.ConcatOffset` is being created here upstream, but the op definition says the input for the dimension should be constant. Here it's the output of `tf.FLoorMod` and not const and hence the error. 

 Unfortunately I'm not very familiar with the behavior here. The documentation for ConcatOffset seems to mention that it is used for gradient computations: https://www.tensorflow.org/mlir/tf_ops#tfconcatoffset_tfconcatoffsetop 

Tagging @ishark and @wangpengmit to see if they know more about the gradient computation / concat op. 
### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

No

### Source

binary

### TensorFlow version

2.15.0.post1, 2.15.0

### Custom code

Yes

### OS platform and distribution

WSL Ubuntu 22.04

### Mobile device

_No response_

### Python version

3.10.12

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

12.2

### GPU model and memory

RTX 3080 Ti

### Current behavior?

### Current behavior
Even tho the shapes of the elements which are concatenated are well defined to compile time, when running the code provided, the execution fails with the error log provided. This is also the case, if you enforce the shapes with using tf.reshape(). 
If you remove either 
- the GradientTape context
- or the for loop with tf.range
- or the XLA compilation 

the code will work as expected.

Unrolling the for loop with range() is not desired since the number of iterations will be >50000 in the final project. Also converting the for loop in a tf.while loop with maximum_iterations specified will result in the same error. Specifying the batchsize to a constant value (also in the input_signature) won't resolve the issue either. Also changing the tf.device between GPU / CPU won't resolve the issue.
The same error arises if you try to use tf.transpose()


### Expected behavior
The arrays with well defined shapes at compile time will be concatenated / transposed when using a for loop with tf.range in the context of a GradientTape while using XLA (jit_compile=True)

### Standalone code to reproduce the issue

```shell
class Model():
    def __init__(self, batchsize):
        self.batchsize = batchsize
        self.g = tf.Variable(initial_value=tf.ones([self.batchsize,1,1]), trainable=True, name="g")
        self.m = tf.Variable(initial_value=tf.ones([self.batchsize,1,1]), trainable=True, name="m")
        self.d = tf.Variable(initial_value=tf.ones([self.batchsize,1,1]), trainable=True, name="d")
        self.k = tf.Variable(initial_value=tf.ones([self.batchsize,1,1]), trainable=True, name="k")
        self.y0 = tf.ones([self.batchsize, 2, 1])


    @tf.function(input_signature=[tf.TensorSpec(shape=(None, 2, 1), dtype=tf.float32)],
                 jit_compile=False)
    def compute(self, yi):
        A1 = tf.concat([tf.zeros_like(self.m), tf.ones_like(self.m)], 2)
        A2 = tf.concat([-self.k/self.m, -self.d/self.m], 2)
        A = tf.concat([A1, A2], 1)
        #A = tf.transpose(A, perm=[0, 2, 1])
        B = tf.concat([tf.zeros_like(self.g), self.g], 1)

        dy = tf.linalg.matmul(A, yi) + B
        return dy
    

class Estimator():
    def __init__(self, model):
        self.model = model
        self.dt = tf.constant(0.001)

    @tf.function(jit_compile=True, reduce_retracing=False)
    def estimate(self):
        with tf.GradientTape() as tape:
            yi = self.model.y0
            for i in tf.range(10):
                dyi = self.model.compute(yi)
                yi = yi + dyi*self.dt
        grads = tape.gradient(yi, [self.model.g, self.model.m, self.model.d, self.model.k])
        return grads



device = "CPU:0"
#device = "GPU:0"

with tf.device(device):    
    batchsize = 5
    model = Model(batchsize)
    estimator = Estimator(model)
    grads = estimator.estimate()
```


### Relevant log output

```shell
OP_REQUIRES failed at concat_op.cc:168 : INVALID_ARGUMENT: Input 0 to node `gradient_tape/while/gradients/while/StatefulPartitionedCall_grad/PartitionedCall/gradients/concat_3_grad/ConcatOffset` with op ConcatOffset must be a compile-time constant.

XLA compilation requires that operator arguments that represent shapes or dimensions be evaluated to concrete values at compile time. This error means that a shape or dimension argument could not be evaluated at compile time, usually because the value of the argument depends on a parameter to the computation, on a variable, or on a stateful operation such as a random number generator.
```
