value {
      type: DT_INT32
    }
  }
}

# MLIR-LABEL: func @main
# MLIR-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<1x1x1x256x!quant.uniform<i8:f32, 0.21632751372549019:27>>) -> tensor<1x6x31x!quant.uniform<i8:f32, 0.09363494573854933:22>>
# MLIR-SAME:  control_outputs = ""
# MLIR-SAME:  inputs = "input"
# MLIR-SAME:  outputs = "output"
# MLIR:         %[[shape:.*]] = arith.constant dense<[1, -1, 31]> : tensor<3xi32>

    value {
      type: DT_INT32
    }
  }
}

# MLIR-LABEL: func @main
# MLIR-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<1x1x1x256x!quant.uniform<i8:f32, 0.21632751372549019:27>>) -> tensor<1x6x31x!quant.uniform<i8:f32, 0.09363494573854933:22>>
# MLIR-SAME:  control_outputs = ""
# MLIR-SAME:  inputs = "input"
# MLIR-SAME:  outputs = "output"
# MLIR:         %[[shape:.*]] = arith.constant dense<[1, -1, 31]> : tensor<3xi32>

// and the module including the TFLite version.

func.func @main(tensor<3x2xi32>) -> tensor<3x2xi32> {
^bb0(%arg0: tensor<3x2xi32>):
  // CHECK: module attributes
  // CHECK-SAME: tfl.description = "MLIR Converted."
  // CHECK-SAME: tfl.schema_version = 3 : i32

  // CHECK:          %{{.*}} = "tfl.pseudo_const"() <{value = dense<{{\[\[1, 2\], \[3, 4\], \[5, 6\]\]}}> : tensor<3x2xi32>}>

If the passwords don't match, we return the same error.

#### Password hashing

"Hashing" means: converting some content (a password in this case) into a sequence of bytes (just a string) that looks like gibberish.

Whenever you pass exactly the same content (exactly the same password) you get exactly the same gibberish.

But you cannot convert from the gibberish back to the password.

	}
	return to
}

// SimplePodServiceAndAllSpecial finds the first Pod deployment that has a sidecar and doesn't use a headless service and removes all
// other "regular" pods that aren't part of the same Service. Pods that are part of the same Service but are in a
// different cluster or revision will still be included.
// Example:
//   - The full set of apps is a, b, c, d, e, headless, naked, and vm.
//   - The plain-pods are a, b and c.

// CHECK-SAME: %[[none]], %[[input_10]], %[[input_11]], %[[none]], %[[input_13]], %[[input_14]], %[[input_15]], %[[input_16]], %[[input_17]], %[[input_18]], %[[input_19]],
// CHECK-SAME: %[[none]], %[[input_21]], %[[input_22]], %[[input_23]])
// CHECK-SAME: effective_hidden_scale_intermediate = tensor<!quant.uniform<i8:f32, 0.0039215686274509803:-1>>

}

// CHECK-LABEL: reduce_window_with_max
// CHECK-SAME: %[[ARG0:.*]]: tensor<2x9x10x3x!quant.uniform<i8:f32, 3.000000e-01:-5>>
// CHECK-SAME: %[[ARG1:.*]]: tensor<!quant.uniform<i8:f32, 3.000000e-01:-5>>
// CHECK: %[[MAX_POOL:.*]] = "tfl.max_pool_2d"(%[[ARG0]])

    // MIN-MAX-CHECK-SAME: id = "composite_dot_general_fn_1_arg_0_calibration_method_1"
    // MIN-MAX-CHECK-DAG: %[[ARG1_ID:.*]] = "tf.Identity"(%arg1)
    // MIN-MAX-CHECK: %[[ARG1_AGG:.*]], {{.*}}, {{.*}}, {{.*}} = "tf.CustomAggregator"(%[[ARG1_ID]])
    // MIN-MAX-CHECK-SAME: id = "composite_dot_general_fn_1_arg_1_calibration_method_1"

// much stronger, but much slower, sponge instance for SHA3-512.)
//
// The SHA-3 functions are "drop-in" replacements for the SHA-2 functions.
// They produce output of the same length, with the same security strengths
// against all attacks. This means, in particular, that SHA3-256 only has
// 128-bit collision resistance, because its output length is 32 bytes.

    !!! tip
        Prefer to use the `Annotated` version if possible.

    ```Python hl_lines="17"
    {!> ../../../docs_src/dependencies/tutorial006.py!}
    ```

These dependencies will be executed/solved the same way as normal dependencies. But their value (if they return any) won't be passed to your *path operation function*.

!!! tip
    Some editors check for unused function parameters, and show them as errors.